PMC8279401_01
Patient's data to design workflow; (A) patient's MRI DICOM data, (B) reconstructed 3D anatomy of the proximal humerus with graded cross-section from distal to proximal aspect, and (C) designed patient-specific prosthesis. (D) Schematic diagram of the EBM process, and (E) final patient-specific AM prosthesis for tumor reconstruction.
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IRB-approved magnetic resonance imaging (MRI) data of a 9-year-old patient diagnosed with proximal humerus osteosarcoma was obtained (Fig 2A). The geometrical variations in cross-sections were incorporated into the design of the prosthesis shaft (Fig 2B). In order to improve the bone-implant interface and facilitate for bone ingrowth, porous structures were introduced at the shell of the distal interface of the AM prosthesis (Fig 2C). The patient-specific prosthesis was fabricated using an electron beam melting (EBM) machine (ArcamEBM, GE Additive Company, USA) which is a powder bed fusion technique (Fig 2D). The beam overlap was 0.2 mm with effective contour, support, and melt speed of 17.18, 50, and 500 mm/s, respectively. Total height of the build was 325.08 mm (Fig 2E). In order to predict the performance of the designed prosthesis, three different loading and boundary conditions, based on patient's body weight, were investigated via FEA (Fig 3B-3D) to simulate: (S1) previously established in-vitro biomechanical testing setup (Fig 3A and 3B), (S2) patient lifting a 1.4kg coffeepot during early rehabilitation (Fig 3C and 3D), and (S3) flexion at 90  with 2kg weight (Fig 3C and 3D).
(A) Biomechanical testing (cyclic loading) of AM prosthesis for tumor reconstruction. (B-D) Applied boundary conditions in the FEA models; the grey region indicates where the stem (green) was fixed to a reference point at the bottom of the images; (B) S1 which mimics the previously established biomechanical test setup, and (C-D) S2 and S3 boundary conditions with applied multi:directional joint reaction forces based on the previous in-vivo study.
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AM prosthesis was tested under cyclic loading in a universal test frame (ElectroForce 3550, TA Instruments). A custom-made fixture was prepared to emulate physiologically relevant loading conditions by securing the prosthesis stem in the test frame (Fig 3A). In order to predict the performance of the designed prosthesis, three different loading and boundary conditions, based on patient's body weight, were investigated via FEA (Fig 3B-3D) to simulate: (S1) previously established in-vitro biomechanical testing setup (Fig 3A and 3B), (S2) patient lifting a 1.4kg coffeepot during early rehabilitation (Fig 3C and 3D), and (S3) flexion at 90  with 2kg weight (Fig 3C and 3D).
(A) Presence and distribution of trapped powder in porous regions of the prosthesis. In the CT images, trapped powders had halftone grayscale, which is between the melted powder and the void space (i.e., pore) grayscale values. Representative (B) all-in focus image showing the surface texture of the as-built EBM patient-specific prosthesis, and (C) associated height map.
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Segmentation (TWS) plugin in ImageJ was employed. The TWS tool uses an open source machine learning software Weka (University of Waikato, Hamilton, NZ) to train a Random Forest classifier based on user-defined representative image stack. Segmented images were reconstructed in 3D for further analysis in Avizo (Fig 4A). Presence of the trapped powder in the designed porous regions was quantified based on percent volumetric fraction (Fig 4A). Surface morphology of the 3D printed prosthesis was evaluated using an optical microscope (i.e., non-contact surface inspection) (Axio Imager M2m, Carl Zeiss, Germany) (Fig 4B and 4C). In this study, confocal z-stacks were converted into height maps (Fig 4C) which were used to determine ISO (international organization for standardization) conforming surface roughness.