I. INTRODUCTION

II. TECHNICAL DESCRIPTION
The new STIR release includes a dedicated reader for SPECT projection data in interfile format 8 and a SPECT projector class. The SPECT interfile reader takes into account the characteristics of SPECT projections as the type of acquisition (circular or non-circular), the rotation radius for each projection, the ∀∃! direction, the extent of the rotation and its initial angle. The new projector for SPECT was created as a matrix projector type derived from the existing STIR ProjMatrixByBin class. This projector provides any necessary information for generation of the projector matrix using the SRL-UB routines. SRL-UB allows modelling of attenuation and Point Spread Function (PSF) in the projection matrix.
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The geometric part of the projection matrix can be calculated in two different ways: (A) if the PSF ∀∀! correction mode is selected, the collimator-detector response is modelled as a spatially variant 1D or 2D
Gaussian distribution in the image plane parallel to the detector plane (with width dependent on the distance between the plane containing the voxel and the detector); (B) else if it is not selected, a geometrical approach ! is used computing the orthogonal projection of the voxel on the detector. The geometrical approach provides higher computational speed and reduced memory requirements than the PSF approach, but is less accurate.
(∃!
In order to reduce computational burden and memory requirements, SRL-UB provides a mask option which allows computing/storing just those projection matrix elements belonging to voxels in the mask. SRL-UB allows PSF modelling using parallel and convergent collimators however, only parallel collimators were tested in the current STIR integration.
Attenuation contribution depends on the attenuation coefficients of the medium and is computed as a (∀! In contrast to other implementations, the projection calculations are not performed on-the-fly but instead )∃! the matrix is computed and stored in STIR's sparse matrix format. The projection matrix can be kept in memory or calculated per projection angle. In the latter case, the memory is released before a new angle is started, reducing memory requirements but increasing computation time for iterative reconstruction algorithms, as illustrated in the next section.
III. MATERIALS AND RESULTS

)∀!
In this section we show results on simulated and acquired data reconstructed using STIR as an illustration of its new capabilities.
A. Simulated data
The SimSET Monte Carlo code 10 was employed using the 2. 
#∃∃!
As regards to computational aspects, 
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The time column of Table 1 represents the total computational time to reconstruct the simulated data using 8 subsets and 40 subiterations. All configurations keeping the matrix in memory required more RAM but less computational time than when recalculating it per projection angle. The matrix size depends only on the PSF modelling and, as a consequence, reconstructions with equal PSF modelling require the same memory independent if attenuation is used or not. By using a mask (last row of Table 1 ) both memory and ##∃! computational time were reduced significant. 
#%∀!
was obtained using a centred cylinder (radius: 66.4 mm, length: 16.6 mm) on the uniform region. Figure 3 shows the SNR against the number of subiterations for each reconstruction algorithm: (A) OSEM, (B) OSMAPOSL with median prior (MRP) (penalization factor, PF = 1.0) and (C) OSSPS with uniform quadratic prior (UQP) (PF = 0.04). The PFs were selected empirically based on reasonable visual appearance. OSSPS was initialized using the OSEM image at 80 subiterations. In OSEM reconstruction, the SNR reaches a #&∃! maximum after few subiterations and then decreases as expected, 13 while in OSMAPOSL-MRP and OSSPS-UQP, the SNR converges to a stable value, which depends on the value of PF. To qualitatively illustrate the performance of the different algorithms, Figure 
#&∀!

B. Acquired data
Real data were also reconstructed with the new SPECT projector of STIR using projections acquired from three of the most commonly used scanner manufactures, GE Healthcare, Philips and SIEMENS. penalised algorithms is that this dependency can be studied analytically, making it possible to change the penalty in order to achieve uniform SNR 17 or resolution.
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#)∃!
In addition, we demonstrated the versatility of STIR in the reconstruction of acquired data from different commercial scanners with the availability of different reconstruction algorithms into one framework.
The integrated software will be included in STIR release 3.0. Future extensions to the library could integrate scatter correction, multi-pinhole collimators, motion compensated image reconstruction, 19 dynamic imaging and multi-tracer protocols, using existing tools in either STIR or SRL-UB.
#)∀!

V. CONCLUSIONS
In this work an extension of a PET reconstruction library to SPECT has been presented using simulated and acquired data from scanners of different manufacturers. Following the integration of the advanced SPECT modelling in the open source STIR project, we hope to enable the wider research community to study the # * ∃! impact of more advanced algorithms in several SPECT imaging scenarios and with different scanners.

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