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

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/2238
Primary Topic
Advanced X-ray and CT Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Veronica Ravano, Tom Hilbert, Paul Margain, Jonas Richiardi et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced X-ray and CT Imaging
article

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

Veronica Ravano, Tom Hilbert, Paul Margain, Jonas Richiardi, Ahmed S. Elwakil, Bénédicte Maréchal, Patrick Omoumi, Jean‐Philippe Thiran, Thomas Yu, Charbel Mourad, Julien Favre, Tobias Kober, Stefan Sommer
article en

Abstract

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 80%
Advanced X-ray and CT Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.