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Abstract #2238

Evaluation of generative models for synthetic CT images using SINGHA, a new spectrally informed metric

Veronica Ravano1,2,3, Adham Elwakil1,2,3, Thomas Yu1,2,3, Tom Hilbert1,2,3, Bénédicte Maréchal1,2,3, Jonas Richiardi2, Jean-Philippe Thiran3, Charbel Mourad2, Paul Margain4, Julien Favre4, Tobias Kober1,2,3, Patrick Omoumi2, and Stefan Sommer1,5
1Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 4Swiss Biomotion Lab, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 5Swiss Centre for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland

Synopsis

Keywords: Analysis/Processing, MSK, synthetic CT

Motivation: Synthetic CT (sCT) based on MRI could improve the characterization of bone pathology by estimating bone mineral density and providing a high level of structural details. However, evaluating the performance of sCT is challenging in both respects.

Goal(s): To propose an evaluation framework for sCT that quantifies accuracy both in terms of image intensity and depiction of structural details.

Approach: We propose the new frequency-based metric SINGHA that captures the sharpness difference between images.

Results: SINGHA was complementary to standard metrics and captured differences in high frequency content, thereby contributing to a more comprehensive evaluation of sCT images.

Impact: Using the newly introduced Spectrally-INformed Grading of High-frequency Attributes (SINGHA) in conjunction with standard intensity-based metrics enables to simultaneously evaluate synthetic CT accuracy in terms of bone mineral density and sharpness.

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