An automated communications system operates to transfer data, metadata and methods from a provider computer to a consumer computer through a communications network. The transferred information controls the communications relationship, including responses by the consumer computer, updating of information, and processes for future communications. Information which changes in the provider computer is automatically updated in the consumer computer through the communications system in order to maintain continuity of the relationship. Transfer of metadata and methods permits intelligent processing of information by the consumer computer and combined control by the provider and consumer of the types and content of information subsequently transferred. Object oriented processing is used for storage and transfer of information. The use of metadata and methods further allows for automating may of the actions underlying the communications, including communication acknowledgements and archiving of information. Service objects and partner servers provide specialized data, metadata, and methods to providers and consumers to automate many common communications services and transactions useful to both providers and consumers. A combination of the provider and consumer programs and databases allows for additional functionality, including coordination of multiple users for a single database.

1. Field of the Invention
The present invention relates to data communications systems. More particularly, it relates to an automated communications system which coordinates the transfer of data, metadata, and instructions between databases in order to control and process communications.
2. Discussion of the Related Art
All communications consist of a mechanism for exchanging information between one entity, a provider, and another, a consumer. The terms "provider" and "consumer" are used to designate separate functions in information transfers. Typically an entity, at various times, operates as both a provider and a consumer in any communication relationship. These relationships may be one-to-one, such as between two individuals; one-to-many, such as between company and its customers; or many-to-many, such as between the members of a workgroup. These communications relationships may also exist over multiple communications networks, such phone networks, LANs, public data communications networks, radio and TV networks, wireless networks, and conventional postal mail networks.
Establishing, maintaining, operating, and even terminating any one of these types of communications relationships involves significant work on the part of both the provider and consumer. For example, to initiate any type of communications relationship, providers must first locate the consumers with whom to communicate and vice versa. Solving this problem is subject of several entire industries, such as the directory industry, the mailing list industry, and the advertising industry. Once a provider or consumer has been identified, contact information (e.g., names, titles, addresses, telephone numbers, electronic mail addresses, etc.) must be exchanged between the provider and consumer. This contact information must be maintained by both parties so that future communications can be effected as needed. When the contact information changes for an entity, all providers or consumers having relationships with the entity must be notified of the changes, who in turn must update their own records. This work also extends to other data and records exchanged in the context of the communications relationship, e.g. orders, receipts, product numbers, invoice numbers, customer numbers, notes, brochures, reports, etc. Maintenance of this information requires significant human time involvement for receiving information, storing information, indexing information, searching for desired information, and retrieving information. The human component of record maintenance also creates a potential for error, which can cause the information to be faulty or to become lost.
Once the communications relationship is established, the next major workload is the active use of the relationship to accomplish communications objectives. The problems here take different forms depending on the type of communications relationship. For example, in a one-to-many relationship, particularly a mass-market relationship such as a company and its customers, the problem is how to efficiently disseminate information about products and services to consumers. Optimally, such information would be disseminated only to the consumers who need the information, only at the precise time they need it, and only via the communications network the consumer preferred. However, knowing who needs what information, when, and how can be very difficult. Therefore, providers typically disseminate information widely in the form of mass advertisements and mailings via all possible communications mediums in order to reach all likely consumers. Because of this broad dissemination by providers, consumers receive large amounts of information, much of which is irrelevant to them. Consumers are forced to sort and filter through this information, and frequently much of it is discarded. Information which is kept may not be immediately useful, but may be needed at a later time. Unless the consumer expends a great deal of work to store, catalog, and index this information, the information can be difficult or impossible to find when the consumer actually needs it.
This same problem of efficient information distribution is exacerbated in many-to-many communications relationships, such as among the members of a workgroup. Here, communications are much more frequent and timely, and there is much greater quantity of information to be shared, stored, archived, and indexed. Members of a workgroup also have a strong need to employ communications for group coordination, such as scheduling meetings, conference calls, project deadlines, etc. These communications involve time deadlines and feedback requirements which are not typically present in one-to-many communications relationships.
With one-to-one communications relationships, the problem of efficient information disemination is lessened because the parties typically have a much higher knowledge of each other's needs and interests. Conversely, the need to use communications for coordination purposes is greatly increased, largely because between individuals the need for real-time communications sessions such as phone calls and personal meetings is acute. Thus the universal problem of "phone-tag", when both parties exchange numerous messages trying to coordinate the opportunity to communicate in real time.
The next workload involved in communications relationships is when the parties need to exchange, process, and store structured data. In a one-to-many communications relationship, a common example is a consumer ordering a product. The consumer must place a telephone call, locate a salesperson, and then manually transmit the necessary ordering information, which the salesperson must manually record. Paper or electronic product order forms can help automate this process for the provider, but they still must be filled out manually by the consumer. Many of these forms require the same standard information from the consumer, which the consumer must enter repeatedly. All of these information transfers require human involvement and thus create the potential for data errors. On the provider's part, more work is required to perform error checking on the order, process it, and in many cases return an acknowledgment to the consumer. Many providers invest heavily in data processing and electronic communications systems for automating these functions. However, the lack of a standard communications system for exchanging common data means that providers adopt largely proprietary systems, increasing the investment necessary for every provider. In addition, consumers must still interact with each these systems manually.
In a many-to-many communications relationship, such as a workgroup, the need for structured data exchange is even higher, especially when automated data processing tools such as computer software are in widespread use. Also, the need for structured data exchange for workgroup coordination activities, such as scheduling and planning, grows significantly.
One-to-one communications relationships may also involve strong needs for structured data exchange. For example, two individuals from different companies may need to review and revise a document involving both companies. The ability to do so electronically, using a secure method of exchange over public data networks, would make the task considerably easier. Individuals involved with one-to-one communications relationships also have an acute need to use structured data exchange to solve the problem of scheduling communications sessions, i.e. the phone-tag problem.
Since all communications relationships are inherently dynamic, they involve three other common tasks involved for providers and consumers: copying the relationship, transfering the relationship, and terminating the relationship. Copying is when one consumer wants to share a particular communications relationship with another consumer. For example, a mail-order catalog customer may wish to give a copy of the catalog to a friend, or a businessperson may need to share the phone number of a colleague with a customer. Transferring is when one provider assumes a consumer communications relationship from another, or one consumer assumes a provider communications relationship from another. An example would when a company changes the salesperson responsible for the customers in a sales territory, or when a customer transfers ownership of a product. Termination is when either a provider or consumer wishes to end a communications relationship, i.e. a provider no longer wants to distributes information, and/or a consumer no longer wants to receive, process, or store the information. A widespread example is consumers who wish to be dropped from direct mailing lists, and the providers who wish they could efficiently identify such consumers to save mailing costs. All three of these common, everyday communications relationship operations involve considerable effort on the part of the provider and consumers to carry out.
Therefore, a need exists for a communications system which allows providers and consumers to quickly and easily establish an automated communications relationship, one in which the data necessary to operate the communications relationship is exchanged and updated automatically, and which can control all types of communications via all types of communications network common to both the provider and consumer. A need also exists for a communications system which allows a provider to actively notify a consumer of new information in which the consumer may be interested, and which allows the consumer to precisely filter the information being sent by one or more providers. A need also exists for a communications system which allows providers and consumers to automatically structure, exchange, and process incoming or outgoing communications to the greatest extent possible. A need also exists for a communications system which allows providers and consumers to easily share access to many common communications services. Finally, a need exists for a communications control system which allows providers and consumers to easily copy, transfer, and terminate communications relationships.
Various computer-based systems have been created to provide mechanisms for communicating information. The Internet and World Wide Web provide a network of a large number of information sources, providing a voluminous amount of information. Computer programs exist which can be executed on Internet-connected computers to search these sources to obtain desired information. Additionally, through the medium of hypertext, providers of World Wide Web pages can create links in their pages between items of related information which can significantly aid consumers in finding desired information. However, the links to the information source are neither dynamic nor persistent; in the sense that they do not provide new or updated information once the consumer has found a topic of interest. "Bookmarks" in a web browser program can facilitate subsequent access to a particular web page to determine if new information is present. However, if the web page referenced by the bookmark is removed, the bookmark is no longer valid. Bookmark polling programs, such as Smart Bookmarks from First Floor, Inc., can also be used to determine whether a web page has changed since the last time the consumer viewed it. In addition, Smart Bookmarks can examine a changed page and automatically transfer to the consumer a text string embedded by the author of the page informing the consumer of the nature of the change. However, Smart Bookmarks' capability is limited to single text strings on single web pages. Therefore the consumer must locate and bookmark every Web page of interest. Smart Bookmarks does not provide a way for the consumer to filter the update messages, nor does it provide the consumer with any mechanism for exchanging structured information or managing a communications relationship with the provider.
A different type of Web monitoring solution is provided by Revnet Systems Inc. With its GroupMaster software, Web providers can create and insert special hyperlinks representing interest topics on the pages of their Web site. When a consumer clicks on this link a special data file is transferred to the consumer's GroupMaster client software. The client software then polls the Web server for updates to the interest topic input by the provider. Unlike Smart Bookmarks, all interest topics at the site can be checked in one update polling action. Update messages can be delivered to the consumer via the client software. However, these messages only contain links back to pages with follow-up information at the Web site. They do not store or index information from the provider, nor do they provide a mechanism for the consumer and provider to automate other types of structured data exchanges or manage a communications relationship.
Online navigation or "auto pilot" software, available from various commercial online services or software companies, can help the user automate access to online services, the Internet, and other public networks. The software provided by these services or companies can include capabilities such as automatic logons, automatic navigation of the online system according to consumer preferences, file searches, uploading and downloading data, and storage of data. Some systems can also automatically download the data necessary to update their own operation. However, the navigation software available from the online services typically requires that the consumer first establish an account with the online service, and may also involve establishing accounts with specific providers on the service. In addition, these navigator programs are specifically designed to work with the architecture and communications protocols of the online service, and cannot be easily adapted to other data communications networks, thus preventing other providers from using the functionality of the online service to create and distribute data in the same manner. Finally, they require that the consumer set up and maintain a communications relationship with each information provider on the service. If the provider changes its information offerings, the consumer must reprogram the autopilot or navigation software. This last disadvantage also applies to online navigation programs designed to work with the Internet and other non-proprietary public data networks.
Electronic mail (e-mail) systems are another electronic communications system that provides some communications contact persistence. E-maii addresses and messages can be stored and indexed within e-mail programs, or externally in other locations. E-mail rules engines allow for some degree of automated storage or response to certain message contents. However, these rules engines are typically constrained to acting on certain known information about the messages, such as the address of the message provider, or on semantic rules such as keywords which must be guessed by the provider and consumer. There is no common communications frame of reference, i.e., a structured data format and operations methodology, against which both the provider and consumer can operate to filter, classify, and organize messages. The lack of a common frame of reference also severely limits the capability of either the provider or consumer to automatically process the contents of an e-mail message, or to automatically respond to the message besides the capability to automatically address a reply message.
E-mail systems which support electronic forms overcome some of these limitations. Electronic forms allow the provider to control the content of a forms submission and to automatically or semi-automatically route that data around a network. Electronic forms also allow message consumers to automate a response to the forms provider which can be automatically processed by the provider. However, these forms must be received and processed by the consumer in the same manner as conventional e-mail messages. In other words, they do not provide a means for the consumer to control or filter messages from different providers. Forms also do not provide the consumer with a mechanism for automatically storing, indexing, or processing information from the provider. In addition, while they may automate the provider's ability to process the data returned from the forms, the consumer must still manually enter information in the form.
Specialized e-mail systems have been developed that combine the use of electronic forms with a system-wide data processing model. Examples are The Coordinator from Action Technologies, Inc., or OVAL from the MIT Center for Coordination Science. These systems allow providers and consumers to share a frame of reference for messaging such that messages can be classified into specific categories and actions. This allows message providers and consumers to automate the routing, storage, and processing of messages based on these category and actions. However, these systems require that all providers and consumers share the same frame of reference. They do not provide a generalized means for each provider on the system to establish and update their own frames of reference with one or more consumers, nor a generalized means for each consumer to coordinate the frames of reference they have with different providers.
A different approach to the problem of automating communications is the category of software that is commonly referred to as "software agents" or "mobile agents". An example is a platform for communications applications developed by General Magic, Inc. called Telescript. The Telescript language is an object-oriented, remote programming language with three core concepts: agents, places and "go". Agents "go" to places, where they interact with other agents to get work done on a user's behalf. Agents are mobile programs capable of transporting themselves from place to place in a Telescript network. The language is implemented by the Telescript engine. The Telescript engine is a multitasking interpreter that integrates onto an operating system through a programming interface called the Telescript API. The Telescript engine operates on server computers connected over a communications network. Telescript agents can operate independently anywhere within these server computers to control the transfer of message data and perform programmable actions upon that message data. For example, if a message recipient is not available, the message could be rerouted to a different location, rather than being returned to the sender undelivered. Telescript is similar to other agent technologies in that the architecture is based on agents interacting with other agents on server computers running agent "engines" or interpreters. In this architecture, the establishment of a communications relationship requires two agents: one to represent the provider and one the consumer. Although agent programming systems like Telescript provide the necessary tools for creating these agents, it is still necessary for both the provider and consumer to create and administer the necessary agents. Furthermore, Telescript does not provide a specific model for the filtering, storage, and indexing of communications between a provider and consumer via agents. Lastly, agent architectures require the addition of servers running agent interpreters to the communications network in order to operate, increasing the expense and complexity of the network.
A more specialized type of agent technology is delivery agents. Examples include Digital Delivery from Digital Delivery, Inc. and PointCast from PointCast Inc. Delivery agents do not require a network of specialized servers, but instead operate directly on a consumer's computer to retreive information of specific relevance to the user over a network. They are created by a particular provider to supply information from a server or servers under that providers control, or from a more generalized news source such as a wire service. Delivery agents allow a consumer to specify his/her topics of interest, which the delivery agent then uses to filter the available news stream and show the user only the information of interest. Delivery agents are also capable of storing and indexing the received data for the consumer. Other than communicating the consumer's topic preferences back to the provider, however, delivery agents do not provide a way to control or process other communications between the consumer and provider. In addition, since each delivery agent is typically designed as a separate executable program which must be installed and run separately, the consumer is limited as to the number of delivery agents the consumer can manage and run.
Another approach to automating communications and data transfers is shared replicated database systems such as Lotus Notes and Collabra Share. With these systems, information to be communicated is entered via a client program into one or more databases which may reside locally on client computers or on network server computers. These databases are then replicated to other server computers or local client computers throughout the system so that the data can be easily accessed by any other user of the system who needs the information and has the proper access privileges. Access privileges are controlled by one or more system administrators via the system servers. Some of these systems, notably Collabra Share, also allow users to "subscribe" to specific databases. These users can receive an e-mail notification from a database agent monitoring the database when a new entry or a certain condition has been made in that database. These systems may also employ electronic forms and forms processing languages to structure the data being entered into a database, and to take programmable actions based on the data entered. The architecture of these systems is designed for groups of users to share information related to specific topics, and to automate the transfer of data between different computer applications used by an organization. For this reason the core data structure of the architecture is a subject database or "forum". Each subject database covers a number of related interest topics under which all entries in the database are categorized. All copies of any subject database are synchronized throughout the system when data in any one copy has been changed.
While suitable for information sharing amongst the members of a group, this architecture is not well suited for automating communications relationships among a large number of information providers and consumers. First, all the providers and consumers need to be interconnected through the system in order to communicate. This could be done by having all providers and consumers enroll in one large system in which they all had access privileges. In such a system each provider would need to have at least one subject database for communicating with his/her consumers. This enormous number of subject databases would then need to be replicated among the large number of servers required to service the complete population of the system, which would quickly overwhelm the capacity of the servers or network to handle replication. A more realistic alternative would be to have each provider or group of providers operate and administer their own system, making their internal subject databases available to consumers via public data networks such as the Internet. Consumers would use the system client software to "subscribe" to the subject databases of each provider with which they desire to communicate. Only the subject databases a consumer subscribed to would be replicated on his/her desktop. This solution would spread the replication load to a large number of servers, each handling a smaller amount of traffic. However, each server would now have to manage replication for a large number of external consumers as well as internal group members. There is no easy way to distribute this replication load to the consumer's computer. Second, subject databases do not allow the consumer to control and filter the incoming communications from providers. Consumers must still scan the databases for items of interest. Providers could overcome this by creating a subject database for each interest topic, but the additional administrative and server replication overhead would strongly discourage this. Third, because notification of new information is handled via a separate application, e-mail, the consumer is forced to coordinate notification and data storage/response among two communications systems. Fourth, since subject databases are replicated from the servers, they do not give consumers an easy way to copy or transfer them to other consumers. Finally, because the entire system depends on server-based replication, administrative changes or reconfigurations of these servers such as system name or address changes require administrative updates to all subscribing consumers, a job which consumers must handle manually.
Consequently, a need exists for a communications control system which allows providers and consumers to quickly and easily establish an automated communications relationship; which automatically updates both parties with changes in communications control data from the other; which works with all communications networks shared by the provider and consumer; which allows both parties to automatically control, filter, store, index, and process communications from the other; which allows both providers and consumers to share many common communications services; and which allows both parties to easily manage, copy, transfer, and terminate the communications relationship.

Computer-based communication system and method using metadata defining a control structure


A method for reconstructing a radioactive emission image of an overall volume having first and second volumetric regions, each volumetric region having respectively independent dynamic characteristics. The method comprises the following steps: a) obtaining radioactive emissions from the overall volume, including the volumetric regions, b) reconstructing an initial radioactive emission image of the volumetric region according to the radioactive emissions, c) segmenting the initial radioactive emission image to delineate the first and second volumetric regions, and d) separately reconstructing the first and the second volumetric regions according to the respectively independent dynamic characteristics.

The present invention relates to a method and an apparatus for image reconstruction in nuclear medicine imaging and, more particularly, but not exclusively to image reconstruction in nuclear medicine imaging using gating techniques.
Radionuclide imaging aims at obtaining an image of a radioactively labeled substance, that is, a radiopharmaceutical, within the body, following administration, generally, by injection. The substance is chosen so as to be picked up by active pathologies to a different extent from the amount picked up by the surrounding, healthy tissue in consequence; the pathologies are operative as radioactive-emission sources and may be detected by radioactive-emission imaging. Pathology may appear as a concentrated source of high radiation, that is, a hot region, as may be associated with a tumor, or as a region of low-level radiation, which is nonetheless above the background level, as may be associated with carcinoma.
A reversed situation is similarly possible. Dead tissue has practically no pick up of radiopharmaceuticals, and is thus operative as a cold region.
The mechanism of localization of a radiopharmaceutical in a particular organ of interest depends on various processes in the organ of interest such as antigen-antibody reactions, physical trapping of particles, receptor site binding, removal of intentionally damaged cells from circulation, and transport of a chemical species across a cell membrane and into the cell by a normally operative metabolic process. A summary of the mechanisms of localization by radiopharmaceuticals is found in http://www(dot)lunis(dot)luc(dot)edu/nucmed/tutorial/radpharm/i(dot)htm.
The particular choice of a radionuclide for labeling antibodies depends upon the chemistry of the labeling procedure and the isotope nuclear properties, such as the number of gamma rays emitted, their respective energies, the emission of other particles such as beta or positrons, the isotope half-life, and the decay scheme.
In PET imaging, positron-emitting radioisotopes are used for labeling, and the imaging camera detects coincidence photons, the gamma pair of 0.511 Mev, traveling in opposite directions. Each one of the coincident detections defines a line of sight, along which annihilation takes place. As such, PET imaging collects emission events, which occurred in an imaginary tubular section enclosed by the PET detectors. A gold standard for PET imaging is PET NH3 rest myocardial perfusion imaging with N-13-ammonia (NH3), at a dose level of 740 MBq, with attenuation correction. Yet, since the annihilation gamma is of 0.511 Mev, regardless of the radioisotope, PET imaging does not provide spectral information, and does not differentiate between radioisotopes.
In SPECT imaging, primarily gamma emitting radioisotopes are used for labeling, and the imaging camera is designed to detect the actual gamma emission, generally, in an energy range of approximately 11-511 KeV. Generally, each detecting unit, which represents a single image pixel, has a collimator that defines the solid angle from which radioactive emission events may be detected.
Because PET imaging collects emission events, in the imaginary tubular section enclosed by the PET detectors, while SPECT imaging is limited to the solid collection angles defined by the collimators, generally, PET imaging has a higher sensitivity and spatial resolution than does SPECT. Therefore, the gold standard for spatial and time resolutions in nuclear imaging is defined for PET. For example, there is a gold standard for PET imaging for at rest myocardial perfusion with N-13-ammonia (NH3), at a dose of 740 MBq with attenuation correction."
Conventional SPECT cameras generally employ an Anger camera, in which a single-pixel scintillation detector, such as NaI(Tl), LSO, GSO, CsI, CaF, or the like, is associated with a plurality of photomultipliers. Dedicated algorithms provide a two dimensional image of the scintillations in the single pixel scintillation detector. There are several disadvantages to this system, for example:
1. The dedicated algorithms associated with the single pixel cannot reach the accuracy of a two-dimensional image of a plurality of single pixel detectors;
2. The single-pixel detector is a rigid unit, which does not have the flexibility of motion of a plurality of small detectors, each with independent motion; and
3. A single hot spot may cause the single pixel detector of the Anger camera to saturate, whereas when a plurality of single pixel detectors is employed, saturation is localized to a few pixels and does not affect the whole image.
Other SPECT cameras which employ a plurality of single pixel detectors are also known.
U.S. Pat. No. 6,628,984, to Weinberg, issued on Sep. 30, 2003 and entitled, "Handheld camera with tomographic capability," describes a tomographic imaging system, which includes a moveable detector or detectors capable of detecting gamma radiation; one or more position sensors for determining the position and angulation of the detector(s) in relation to a gamma ray emitting source; and a computational device for integrating the position and angulation of the detector(s) with information as to the energy and distribution of gamma rays detected by the detector and deriving a three dimensional representation of the source based on the integration. A method of imaging a radiation emitting lesion located in a volumetric region of interest also is disclosed.
U.S. Pat. No. 6,242,743, to DeVito, et al., issued on Jun. 5, 2001 and entitled, "Non-orbiting tomographic imaging system," describes a tomographic imaging system which images ionizing radiation such as gamma rays or x rays and which: 1) can produce tomographic images without requiring an orbiting motion of the detector(s) or collimator(s) around the object of interest, 2) produces smaller tomographic systems with enhanced system mobility, and 3) is capable of observing the object of interest from sufficiently many directions to allow multiple time-sequenced tomographic images to be produced. The system consists of a plurality of detector modules which are distributed about or around the object of interest and which fully or partially encircle it. The detector modules are positioned close to the object of interest thereby improving spatial resolution and image quality. The plurality of detectors view a portion of the patient or object of interest simultaneously from a plurality of positions. These attributes are achieved by configuring small modular radiation detector with high-resolution collimators in a combination of application-specific acquisition geometries and non-orbital detector module motion sequences composed of tilting, swiveling and translating motions, and combinations of such motions. Various kinds of module geometry and module or collimator motion sequences are possible, and several combinations of such geometry and motion are shown. The geometric configurations may be fixed or variable during the acquisition or between acquisition intervals. Clinical applications of various embodiments of U.S. Pat. No. 6,242,743 include imaging of the human heart, breast, brain or limbs, or small animals. Methods of using the non-orbiting tomographic imaging system are also included.
U.S. Pat. No. 5,939,724, to Eisen, et al., issued on Aug. 17, 1999, and entitled, "Light weight-camera head and—camera assemblies containing it," describes a lightweight gamma-camera head, assemblies, and kits that embody it. The gamma-camera head has a detector assembly which includes an array of room temperature, solid state spectroscopy grade detectors each associated with a collimator and preamplifier, which detectors and associated collimators and preamplifiers are arranged in parallel rows extending in a first direction and suitably spaced from each other in a second direction normal to the first direction, each of the parallel detector rows holding a plurality of detectors. The head may optionally have an electric motor for moving the detector in the second direction and optionally also in the first direction, either stepwise or continuously.
U.S. Pat. No. 6,525,320, to Juni, issued on Feb. 25, 2003, and entitled, single photon emission computed tomography system, describes a single photon emission computed tomography system, which produces multiple tomographic images of the type representing a three-dimensional distribution of a photon-emitting radioisotope. The system has a base including a patient support for supporting a patient such that a portion of the patient is located in a field of view. A longitudinal axis is defined through the field of view. A detector module is adjacent the field of view and includes a photon-responsive detector. The detector is an elongated strip with a central axis that is generally parallel to the longitudinal axis. The detector is operable to detect if a photon strikes the detector. The detector can also determine a position along the length of the strip where a photon is detected. A photon-blocking member is positioned between the field of view and the detector. The blocking member has an aperture slot for passage of photons aligned with the aperture slot. The slot is generally parallel to the longitudinal axis. A line of response is defined from the detector through the aperture. A displacement device moves either the detector module or the photon-blocking member relative to the other so that the aperture is displaced relative to the detector and the line of response is swept across at least a portion of the field of view.
U.S. Pat. No. 6,271,525, to Majewski, et al., issued on Aug. 7, 2001, and entitled, "Mini gamma camera, camera system and method of use," describes a gamma camera, which comprises essentially and in order from the front outer or gamma ray impinging surface: 1) a collimator, 2) a scintillator layer, 3) a light guide, 4) an array of position sensitive, high resolution photomultiplier tubes, and 5) printed circuitry for receipt of the output of the photomultipliers. There is also described, a system wherein the output supplied by the high resolution, position sensitive photomultipiler tubes is communicated to: a) a digitizer and b) a computer where it is processed using advanced image processing techniques and a specific algorithm to calculate the center of gravity of any abnormality observed during imaging, and c) optional image display and telecommunications ports.
U.S. Pat. No. 6,271,524, to Wainer, et al., issued on Aug. 7, 2001 and entitled, "Gamma ray collimator," describes a gamma ray collimator assembly comprising collimators of different gamma ray acceptance angles. For example, the acceptance angle of a first collimator may be between 0.2 and 5 degrees, and the acceptance angle of a second collimator may be between about 5 and 30 degrees.
U.S. Pat. No. 6,212,423, to Krakovitz, issued on Apr. 3, 2001 and entitled, "Diagnostic hybrid probes," describes a hybrid nuclear and ultrasonic probe, comprising a cylindrical outer casing surrounding a nuclear probe, which comprises two scintillator plates intersecting perpendicularly, each of the scintillator plates having a plurality of parallel collimators; and an ultrasonic probe situated between said casing at the intersection of said scintillator plates.
List mode data acquisition is known in PET studies, and enables the determination of coincidence. It relates to recording every radiation event together with data pertinent to that event, which includes:
i. the time the radiation event impinged upon a detector pixel, with respect to a clock, with respect to a time bin, or with respect to another time definition, for example, a time interval between two clock signals; and
ii. the detector pixel location with respect to a coordinate system, at the time of the impinging.
The knowledge of time and location enables the determination of coincidence counts, namely photon counts that arrive substantially simultaneously, 180 degrees apart.
The time and location data may be stamped onto the radiation-event data packet, for example, as a header or as a footer, or otherwise associated with the radiation-event data packet, as known.
The time-stamped data available in PET studies may further be used for perfusion studies, where the timing of physiological processes of short durations, that is, durations shorter than about half the time span between heartbeats, is important. Perfusion studies usually involve a sequence of continuous acquisitions, each of which may represent data acquisition duration of about 10-30 seconds, although longer durations are sometimes employed. Data from each of the frames is independently reconstructed to form a set of images that can be visualized and used to estimate physiological parameters. This approach involves selection of the set of acquisition times, where one must choose between collecting longer scans with good counting statistics but poor temporal resolution, or shorter scans that are noisy but preserve temporal resolution.
US Patent Application 2003010539, to Tumer, et al., published on Jun. 5, 2003, and entitled, "X-ray and gamma ray detector readout system," describes a readout electronics scheme, under development for high resolution, compact PET (positron emission tomography) imagers, using time tagging, based on LSO (lutetium ortho-oxysilicate, Lu.sub.2SiO.sub.5) scintillator and avalanche photodiode (APD) arrays.
There is some work relating to timing data in SPECT systems, employing Anger cameras.
U.S. Pat. No. 5,722,405, to Goldberg, issued on Mar. 3, 1998, and entitled, "Method and apparatus for acquisition and processsing of event data in semi list mode," describes a system for acquisition, processing and display of gated SPECT imaging data for use in diagnosing Coronary Artery Disease (CAD) in nuclear medicine, employing an Anger camera, and provides a physician with two parameters for evaluating CAD: information relating to the distribution of blood flow within the myocardium (perfusion) and information relating to myocardium wall motion (function). One aspect provides the physician with a display of functional images representing quantitative information relating to both perfusion and function with respect to selected regions of interest of the subject heart at end-diastole and end-systole segments of the cardiac cycle. The functional display consists of arcs of varied width depending on wall motion and color coded to illustrate degrees of myocardial perfusion for different pie shaped sections of a selected region of interest within a given short axis slice of reconstructed volumetric region data. Another aspect provides a series of display images allowing facilitated access, display, and comparison of the numerous image frames of the heart that may be collected during gated SPECT sessions. U.S. Pat. No. 5,722,405 also teaches the ability to define and recall parameter files representative of data acquisition and processing parameters and protocol for use in gated SPECT studies and includes a semi-list processing mode to increase efficiency of data acquisition within a camera computer system.
U.S. Pat. No. 7,026,623, to Oaknin, et al., issued on Apr. 11, 2006, and entitled, "Efficient single photon emission imaging," describes a method of diagnostic imaging in a shortened acquisition time for obtaining a reconstructed diagnostic image of a portion of a body of a human patient who has been administered with dosage of radiopharmaceutical substance radiating gamma rays, using SPECT and an Anger camera. The method comprises acquiring photons emitted from said portion of the body, by means of a detector capable of converting the photons into electric signals, wherein the total time of photon acquiring is substantially shorter than the clinically acceptable acquisition time; processing said electric signals by a position logic circuitry and thereby deriving data indicative of positions on said photon detector crystal, where the photons have impinged the detector; and reconstructing an image of a spatial distribution of the pharmaceutical substance within the portion of the body by iteratively processing said data. For example, the method includes effective acquisition time of less than 10 minutes, or less than 8 minutes, and acquiring photons in a list-mode procedure.
Current techniques record data with SPECT and electrocardiogram (ECG), and perform some gating to the data which is captured by the SPECT detectors, to incorporate the global and regional atrial and ventricular function and assessment of the relationship of perfusion to regional function.
Gated images are used to overcome distortions such as motion artifacts, which are caused due to motion of the heart during image acquisition. The images are needed as the physical model used for reconstruction assumes that the imaged objects are static. In gated imaging, photon-counting takes into account the portion of the heart contraction cycle within which a photon is measured. The Gating enables the reconstruction of an anatomical structure which is subject to periodic motion by enabling image acquisition only when the structure has reached the same configuration. Cardiac contraction is usually synchronized to the recorded electrocardiogram (ECG) signal that indicates the current heart pose. The period between a certain repetitive wave, such as R-wave, and a subsequent wave is divided into several time segments, called "frames", which are usually spaced evenly. Each photon which is detected by the PET detectors during one of the frames is collected and associated with the related frame.
In gated imaging, each frame generates a single dataset. The collection of all the datasets belonging to all the frames are defined as a "dynamic" dataset.
The dynamic dataset is created by dividing the time span between one R-wave to the next R-wave into M frames that usually have an identical duration. Each detected photon is accumulated into a dataset of one of the M frames. Each dataset of the M datasets contains data relevant to a defined portion ("snapshot") within the cardiac cycle.
Usually, during the image reconstruction process, each one of the gated datasets of the M frames is processed independently by a suitable reconstruction algorithm, see Leahy R et al., Computer tomography in: Handbook of Image and Video Processing, BovikA, Academic press, 2000, pp. 771-787; J. Kay. The EM algorithm in medical imaging, Stat. Meth. Med. Res., 6(1):55-75, January 1997; J. A. Fessler, Statistical image reconstruction methods for transmission tomography, Handbook of Medical Imaging, Volumetric region 2, pages 1-70. SPIE, Bellingham, 2000; R. M. Leahy et al., Statistical approaches in quantitative positron emission tomography, 10(2):147-65, April 2000; M. Defrise, A short reader's guide to 3D tomographic reconstruction, Computerized Medical Imaging and Graphics, 25(2):1 13-6, March 2001; Vandenberghe, Y. D'Asseler, et al. Iterative reconstruction algorithms in nuclear medicine, Computerized Medical Imaging and Graphics, 25(2):105-11, March 2001; G. L. Zeng. Image reconstruction, a tutorial, Computerized Medical Imaging and Graphics, 25(2):97-103, March 2001; and R. M. Lewitt et al., Overview of methods for image reconstruction from projections in emission computed tomography, Proc. IEEE, 91(9):1588-611, October 2003, which are incorporated herein by reference in its entirety.
A common practice in gated SPECT reconstruction is to divide the gated dynamic dataset into M 'non-gated' data sets. Each one of the datasets includes data from a single frame i. The reconstruction of each volumetric region is performed independently using the relevant data set.
In particular, once the emission data is obtained, the data is processed to reconstruct the intensity distribution within the measured volumetric region. The reconstruction process is generally complex, due to the large quantity of data that must be processed in order to obtain an accurate reconstruction. The following prior art statistical model may be used to perform reconstruction.
We assume an intensity distribution, I, defined over an input overall volume U, where U denotes a set of basic elements, such as pixels in two dimensional overall volumes and voxels in three dimensional overall volumes, and I(u) is the intensity of a given basic element u∈U. A detecting unit positioned on a radiation-emission-measuring-probe such as a PET detector or the like takes a series of measurements y=(yt)t=1T from different positions and orientations around the volumetric region U. The geometrical and physical properties of the detecting unit, together with its position and orientation in a given measurement t, determine the detection probability ϕt(u) of a photon emitted from location u in time t. Thus, the effective intensity of location u as viewed by the detecting unit during measurement t is ϕt(u)I(u).
The random count Xt(u) of photons that are emitted from location u and detected in measurement t is modeled by a Poisson process with mean ϕt(u)I(u). The total count of photons detected in measurement t is Yt=Σu∈U Xt(u), and the reconstruction problem is to reconstruct the intensities (I(u))u∈U from the measurements (yt)t=1T.
The 2-D Radon transform is a mathematical relationship that may be used for reconstructing the emission intensities of volumetric region U when the set of measurements (yt)t=1T is unconstrained. The Radon transform is not statistical and does not take into account the Poissonian nature of the counts. In addition, it models the views as line projections. The Radon transform maps the spatial domain (x,y) to the Radon domain (p,ϕ). For a fixed projection angle, the Radon transform is simply a projection of the object. A technique known in the ART as filtered back-projection (FBP) uses a back-projection operator and the inverse of the Radon transform to reconstruct the intensity distribution in volumetric region U from measurements (yt)t=1T.
The basic, idealized problem solved by the FBP approach is to reconstruct an image from its Radon transform. The Radon transform, when properly defined, has a well-defined inverse. However, in order to invert the transform one needs measured data spanning 180°. In many medical imaging situations, the positioning of the detecting unit relative to the emitting object is constrained, so that complete measured data is not available. Reconstruction based on filtered back-projection is therefore of limited use for medical imaging. Maximum likelihood (ML) and Maximum A Posteriori (MAP) estimation methods, which address the statistical nature of the counts, have been found to provide better image reconstructions than FBP.
Limited-angle tomography is a reconstruction technique in the related art which reconstructs an image from projections acquired over a limited range of angular directions. The success of the reconstruction process depends upon the extent of the angular range acquired compared with the angular range of the missing projections. Any reconstruction from a limited range of projections potentially results in spatial distortions (artifacts) in the image. Limited angle techniques can be applied for both the Radon transform and the statistical models, but better results are generally achieved within the statistical framework. While it is known that the severity of the artifacts increases with the increasing angular range of the missing projections, limited-angle tomography does not provide information on which projections should be used in order to most effectively reconstruct the image.
ML estimation is a widely used method in the related art for reconstructing an image from a constrained set of measurements. A parameterization of the generative model described above is obtained by assigning an intensity I(u) to every voxel in U. The likelihood of the observed data y=(yt)t, given the set of parameters I={I(u):u∈U} is:
                                                                                L                                                        (                                          y                      ❘                      I                                        )                                                  =                                                      ln                                                                                P                                                                    (                                                  y                          ❘                          I                                                )                                                                              =                                                                                ln                                                                                ∏                          t                                                                                P                                                                                (                                                                                y                                t                                                            ❘                              I                                                        )                                                                                =                                                                  ∑                        t                                                                                ln                                                                                P                                                                                (                                                                                ∑                                  u                                                                                x                                    t                                                                                (                                    u                                    )                                                                                ❘                              I                                                        )                                                                                =                                                                                ∑                    t                                                                          ln                                                                                Poisson                                                                    (                                                                              y                            t                                                    ❘                                                                                ∑                              u                                                                                ϕ                                  t                                                                                (                                  u                                  )                                                                                I                                                                                (                                  u                                  )                                                                                )                                                                                =                                                                                ∑                    t                                                                          {                                                                                -                                                                                ∑                                  u                                                                                ϕ                                      t                                                                                (                                      u                                      )                                                                                I                                                                                (                                    u                                    )                                                                                +                                                                                y                                t                                                                                ln                                                                                ∑                                  u                                                                                ϕ                                      t                                                                                (                                      u                                      )                                                                                I                                                                                (                                      u                                      )                                                                                -                                                          ln                                                                                (                                                                                y                                    t                                                                    !                                                                )                                                                                }                                                                                (        1        )
Note that the lower and upper bound of an indexing variable, such as voxels u and time index t, are omitted in the following description, when they are clear from the context.
There is currently no analytic way to solve Eqn. 1 for the maximum of the likelihood function. However, optimization methods that find local maxima of the likelihood are known. One such method is the Expectation-Maximization (EM) process.
Since the data generated by the model is only partially observable by our measurements, a basic ingredient of the EM formalism is to define a set of random variables that completely define the data generated by the model. In the current case, since Yt=ΣuXt(u), the set of variables {Xu(t):u∈U; t=1, . . . , T} is such a set; the generated data is x=(xt)t, where xt=(xt(u))u, and the observed data y is completely determined by x. The main tool in the EM formalism is the complete data likelihood:
                                                                                ln                                                                                P                                                              (                                              x                        ❘                        I                                            )                                                                      =                                                                                ln                                                                                ∏                        t                                                                                P                                                                          (                                                                                x                              t                                                        ❘                            I                                                    )                                                                                =                                                            ∑                      t                                                                                ln                                                                                ∏                          u                                                                                Poisson                                                                                (                                                                                x                                  t                                                                                (                                  u                                  )                                                                                ❘                                                                                ϕ                                    t                                                                                (                                    u                                    )                                                                                I                                                                                (                                    u                                    )                                                                                )                                                                                =                                                                                ∑                    t                                                                                ∑                      u                                                                                {                                                                                -                                                                                ϕ                                t                                                                                (                                u                                )                                                                                I                                                                                (                              u                              )                                                                                +                                                                                x                              t                                                                                (                              u                              )                                                                                ln                                                                                (                                                                                ϕ                                    t                                                                                (                                    u                                    )                                                                                I                                                                                (                                    u                                    )                                                                                )                                                                                +                                                  ln                                                                                (                                                                                x                                  t                                                                                (                                  u                                  )                                                                                !                                                        )                                                                                }                                                                                (        2        )
Since the likelihood depends on the complete data, which is only partially observable, we take its expectation with respect to the overall volume of the unobserved data, given the current set of hypothesized parameters (i.e. the current estimator). The result is a function Q(I|I′) which assigns likelihood to sets I of model parameters, given the current set I′, and given the observed data y:
                                                                                Q                                                        (                                          I                      ❘                                              I                        ′                                                              )                                                  =                                                                  E                                                        [                                                                  ln                                                                                P                                                                                (                                                          x                              ❘                              I                                                        )                                                                                y                                            ;                                              I                        ′                                                              ]                                                                                =                                                                                ∑                    t                                                                                ∑                      u                                                                                {                                                                                -                                                                                ϕ                                t                                                                                (                                u                                )                                                                                I                                                                                (                              u                              )                                                                                +                                                                              E                                                                                [                                                                                x                                      t                                                                                (                                      u                                      )                                                                                ❘                                                                      y                                    t                                                                                ;                                                                  I                                  ′                                                                                ]                                                                                ln                                                                                (                                                                                ϕ                                    t                                                                                (                                    u                                    )                                                                                I                                                                                (                                    u                                    )                                                                                )                                                                                +                        C                                            }                                                                                (        3        )
where C is a term which is independent of the intensities I. The function Q(I|I′) is maximized by the following new estimates:
                                                        I                                            (                u                )                                      =                                          1                                                      ∑                    t                                                                                ϕ                      t                                                                                (                      u                      )                                                                                ∑                  t                                                                  [                                                                                x                          t                                                                                (                          u                          )                                                                    ❘                                              y                        t                                                              ;                                          I                      ′                                                        ]                                                              ;                                                                    ∀                      u            ∈                          U              .                                                          (        4        )
The expectation in Equation 4 is obtained as follows:
                                                                                P                                                            X                      t                                                                                (                      u                      )                                                                                (                                                                                x                          t                                                                                (                          u                          )                                                                    ❘                                              y                        t                                                              ;                                          I                      ′                                                        )                                            =                                                                                P                                              Y                        t                                                                                (                                                                                y                            t                                                    ❘                                                                                x                              t                                                                                (                              u                              )                                                                                ;                                                  I                          ′                                                                    )                                                                                P                                                                        X                          t                                                                                (                          u                          )                                                                                (                                                                                x                            t                                                                                (                            u                            )                                                                          ❘                                                  I                          ′                                                                    )                                                                                P                                          Y                      τ                                                                                (                                                                  y                        t                                            ❘                                              I                        ′                                                              )                                                                                =                                                                                Poisson                        (                                                                                y                              t                                                        -                                                                                x                                t                                                                                (                                u                                )                                                                                ❘                                                                                ∑                                                              v                                ≠                                u                                                                                ϕ                                  t                                                                                (                                  v                                  )                                                                                I                                  ′                                                                                (                                  v                                  )                                                                                )                                                                                Poisson                        (                                                                                x                              t                                                                                (                              u                              )                                                                                ❘                                                                                ϕ                                t                                                                                (                                u                                )                                                                                I                                ′                                                                                (                                u                                )                                                                                )                                                                                Poisson                                                                                (                                                                  y                        t                                            ❘                                                                        ∑                          v                                                                                ϕ                              t                                                                                (                              v                              )                                                                                I                                                                                (                              v                              )                                                                                )                                                                                =                                                          Binomial                (                                                                                x                        t                                                                                (                        u                        )                                                              ❘                                                                                ϕ                            t                                                                                (                            u                            )                                                                                I                            ′                                                                                (                            u                            )                                                                                ∑                          v                                                                                ϕ                              t                                                                                (                              v                              )                                                                                I                              ′                                                                                (                              v                              )                                                                                ;                                      y                    t                                                  )                                                                        (        5        )
It follows that
            E                    [                                            xt                                            (                u                )                                      ❘            yt                    ;                      I            ′                          ]              =                  y        t                                                              ϕ              t                                                  (              u              )                                                                    I              ′                                                  (              u              )                                                            ∑            v                                                                        ϕ                t                                                          (                v                )                                                                                I                ′                                                          (                v                )                                                          ,and hence the EM iteration is:
                              I                                (            u            )                          =                              1                                          ∑                t                                                                            ϕ                  t                                                                  (                  u                  )                                                                                ∑              t                                                                  y                t                                                                                ϕ                      t                                                                                (                      u                      )                                                                                I                      ′                                                                                (                      u                      )                                                                                ∑                    v                                                                                ϕ                        t                                                                                (                        v                        )                                                                                I                        ′                                                                                (                        v                        )                                                                                (        6        )
It is provable that each EM iteration improves the likelihood. Thus, given a random starting estimator, the EM algorithm iterates the above improvement step until it converges to a local maximum of the likelihood. Several random starts increase the chance of finding a globally good estimator.
It is usually desired to maximize the expected posterior probability (given a proper prior) rather than the expected likelihood. In that case we assume a prior probability on the intensities P(I)=ΠuP(I(u)). A proper conjugate prior for the Poisson distribution is the Gamma distribution:
                              P                                (                          I                                            (                u                )                                      )                          =                              Gamma                                      (                                                                    I                                                              (                      u                      )                                                        ❘                                      α                    u                                                  ;                                  β                  u                                            )                                =                                                    β                u                                                      α                    u                                    +                  1                                                            Γ                                                  (                                                            α                      u                                        +                    1                                    )                                                                                I                                                  (                  u                  )                                                            α                u                                                                e                                                -                                      β                    u                                                                                I                                                        (                    u                    )                                                                                (        7        )
Now the maximization is done on Q(I|I′)=E[lnP(x|I)p(I)|y; I′]. Plugging the Gamma prior into Q, and solving for I(u), we get the following EM iteration for the maximum posterior estimation:
                              I                                (            u            )                          =                                                                            α                u                            +                                                ∑                  t                                                                  E                                                        [                                                                                x                            t                                                                                (                            u                            )                                                                          ❘                                                  y                          t                                                                    ;                                              I                        ′                                                              ]                                                                                β                u                            +                              ∑                                                      ϕ                    t                                                                          (                    u                    )                                                                                (            8            )                                                  =                                                            1                                                β                  u                                +                                                      ∑                    t                                                                                ϕ                      t                                                                                (                      u                      )                                                                                [                                          α                u                            +                                                ∑                  t                                                                                y                    t                                                                                ϕ                          t                                                                                (                          u                          )                                                                                I                          ′                                                                                (                          u                          )                                                                                ∑                        v                                                                                ϕ                            t                                                                                (                            u                            )                                                                                I                            ′                                                                                (                            v                            )                                                                                ]                                                                                (            9            )
The EM update step can be formulated in matrix notation as follows. Let Φ be the matrix of the projections [ϕt(u)]t,u, and let I, I′,y, α and β be represented as column vectors. Equation 8 can be written in vector and matrix notations as:
                    I        =                              α            +                                          I                ′                            ·                              (                                                      Φ                    T                                                                          y                                          Φ                                                                                I                        ′                                                                                )                                                          β            +                                          Φ                T                                                                                1                                                          (        10        )
where the explicit multiplication and division denote element-wise operations, and where 1 is a vector (of the appropriate length) consisting solely of 1's.
Limited computational resources (i.e., when the entire projection matrix Φ cannot be kept in memory) may require breaking the update computation according to a partition of Φ into a set of sub-matrices (Φi). In that case the intensities can be updated gradually (using only one sub-matrix at each step) according to the following computation:
                    I        =                              α            +                                          I                ′                            ·                                                ∑                  i                                                                                Φ                    i                    T                                                                                y                      i                                                                                Φ                        i                                                                                I                        ′                                                                                β            +                                          ∑                i                                                                            Φ                  i                  T                                                                                1                                                                        (        11        )
where yi denotes the vector of observations that are obtained using the views of Φi.
In order to achieve a reconstructed image which is adequate for medical diagnostic and treatment purposes, a high-resolution image of the tested object must be obtained. When high-resolution detecting units are used, their efficiency is relatively low, and the detecting units must remain at each position for a relatively long time in order to achieve a high probability of detection. Since during medical testing, measurements are generally performed at many locations as the detecting unit is moved relative to the observed organ, the testing procedure generally requires a long time and is physically and emotionally difficult for the patient. Additionally, reconstruction is based upon a large quantity of data, and is a lengthy and computationally complex process.
Reference is now made to FIG. 13, which is a schematic flowchart that illustrates steps of a typical prior art gated image reconstruction method. In FIG. 13, i denotes a frame counter and M denotes the number of frames. Usually, after all the M datasets are fetched, as shown at 2, and i is set with 1, as shown at 4, the dataset that corresponds with frame i is loaded into the processing unit, as shown at 6. During the following step, as shown at 8, the processing unit is used to perform a frame reconstruction according to the dataset that corresponds with frame i using any suitable reconstruction algorithm known in the art, such as the aforementioned EM algorithm, ordered subset expectation maximization (OSEM), and algebraic reconstruction techniques (ART) FBP.
As shown at 12, after the reconstruction is completed, the frame counter i is incremented by 1. If i is larger than M and there are no more frame the process ends. If i is not larger than M, the next dataset that corresponds with the subsequent frame is loading for reconstruction. In such a manner, the generation of a static imaging of the heart in a specific configuration becomes possible.
However, a known problem of such a method is the high computational load that is needed for the execution thereof. Even when using an ordered set method, such as the aforementioned Hudson et al. method, which is capable of reducing the computational load while giving similar results, the computational power needed to obtain a good image reconstruction is still quite high. Such a high computational load results in a longer reconstruction time that reduces the throughput of the processing unit and requires a more expensive processing hardware.
There is thus a widely recognized need for, and it would be highly advantageous to have, a method and an apparatus for image reconstruction in nuclear medicine imaging devoid of the above limitations.

Gating with anatomically varying durations


A method for fabricating a semiconductor device comprises the steps of depositing a metal film for forming interconnections and/or electrodes on an insulating film formed on a substrate and then applying a hydrostatic pressure exceeding atmospheric pressure to the deposited metal film; or comprises the steps of forming a passivation film on a metal film for forming interconnections and/or electrodes, which is formed on a substrate with an insulating film, and then applying a hydrostatic pressure exceeding atmospheric pressure to the passivation film formed. The method makes it possible to substantially improve density and adhesion of the metal film and the insulating film and to produce semiconductor devices equipped with high-quality electrodes or interconnections at a relatively low cost.

1. Field of the Invention
The present invention relates to a method for fabricating a semiconductor device, and in particular a method for fabricating a semiconductor device provided with interconnections or electrodes having high reliability. The present invention also relates to a method for producing interconnections, electrodes and insulating films which are of high density and firmly adhered to an underlying layer.
2. Description of the Prior Art
Metal films for forming interconnections or electrodes and insulating films for semiconductor devices have been deposited through a variety of methods such as the vacuum evaporation method, sputtering method and CVD method under a reduced pressure or atmospheric pressure. Such metal films formed on the substrate is then patterned into patterns for interconnections or electrodes, thereafter a passivation film is formed thereon to insulate and passivate the resulting interconnections or electrodes and to thus complete the semiconductor device.
Such metal and insulating films deposited on the underlying film under a reduced pressure or atmospheric pressure often have lower density or is not always sufficiently adhered to the underlying films. This becomes a cause of stress migration and electromigration, in particular, for fine interconnections, for instance, on the order of about 0.6 to 1.5 .mu.m and for thin interconnections, for instance, on the order of about 0.5 .mu.m, which occur as the degree of integration of LSI's increases. Accordingly, the density of films for interconnections, electrodes and insulating layers and the adhesion thereof to the underlying film greatly affect the yield of semiconductor devices and the reliability of the resulting interconnections. Therefore, this is an important problem to be solved in the fabrication of semiconductor devices.
For instance, it has been believed that the reduction in reliability of aluminum (Al) interconnections for IC's due to stress migration and electromigration results from low density of an Al film and insufficient adhesion thereof to an underlying film and a passivation film. It has likewise been believed that insufficient adhesion between a silicide layer and a polycrystalline silicon layer which constitute a two-layered structure (a polycide structure) used for forming interconnections and gate electrodes of semiconductor devices leads to peeling off or delaminating of the silicide film during a high temperature annealing process. It has been widely recognized that low-density insulating films cause corrosion of interconnections and instability of devices due to contaminants. These become a severe obstacle for the development of high quality semiconductor devices.
In general, a barrier layer of, for instance, titanium nitride is applied between Al interconnections and a silicon (si) substrate in order to prevent alloying reaction between Al and Si. The Si substrate and Al layers must be firmly adhered to such a barrier layer in order to obtain good barrier properties of the latter and lower contact resistance. For this reason, it is very important to develop a method for forming metal films having a high density and sufficient adhesion of the metal film to an underlying film for improving the yield of semiconductor devices and the reliability of interconnections.

Method for fabricating semiconductor device