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Abstract #5216

Improving synthetic CT generation with enhanced registration

Paul Margain1, Stefan Sommer2,3, Veronica Ravano3,4,5, Adham Elwakil3,4, Tom Hilbert3,4,5, Charbel Mourad5, Julien Favre1, and Patrick Omoumi1,5
1Swiss BioMotion Lab, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 2Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland, 3Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Zurich, Lausanne, Switzerland, 4LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 5Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

Synopsis

Keywords: Synthetic MR, Machine Learning/Artificial Intelligence, MSK, Bone, Registration

Motivation: Synthetic CT (sCT) generation from MRI can provide mineralized tissue data without ionizing radiation. Voxel to voxel correspondence between MRI and CT for supervised training is one of the challenges in the generation of sCT.

Goal(s): This study aims to evaluate the impact of registration techniques on sCT bone generation quality.

Approach: Two registration methods, image-based and bone-centered, were applied to pre-process MRI-CT data before training 3D U-Net models for sCT generation. Model performance was assessed using MAE and PSNR.

Results: Bone-centered registration significantly improved sCT accuracy, with lower MAE and higher PSNR.

Impact: Bone-centered registration enhances synthetic CT generation for bone imaging, potentially advancing musculoskeletal imaging applications focused on bone pathology.

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Keywords