Meeting Banner
Abstract #2367

XSIM, a Susceptibility-Optimised Similarity Index Metric: Validation with 2016 and 2019 QSM Reconstruction Challenge Datasets

Carlos Milovic1,2, Cristian Tejos2,3,4, Pablo Irarrazaval2,3,4,5, and Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 4Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 5Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile


The Structural Similarity Index (SSIM) has become a popular quality metric to evaluate Quantitative Susceptibility Mapping (QSM) in a way that is closer to human perception than the Root-Mean-Squared-Error (RMSE). However, SSIM may over-penalize errors in diamagnetic tissues and under-penalize them in paramagnetic tissues. Extreme susceptibility artifacts may also compress the dynamic-range, resulting in unrealistically high SSIM scores (hacking). To overcome these problems we propose XSIM: SSIM implemented in the native QSM ppm range with new susceptibility-optimized internal parameters. We validated XSIM using data from both QSM challenges. XSIM avoids bias and metric-hacking, promoting sharp susceptibility maps and preventing over-regularization.

This abstract and the presentation materials are available to members only; a login is required.

Join Here