Abstract #1293

A Generalized Cascaded Self-Supervised Registration Pipeline with Physics-Informed Learning for Enhanced Quantitative Cardiac MRI

Xinqi Li^1,2, Yi Zhang³, Li-Ting Huang¹, Hsiao‐Huang Chang⁴, Thoralf Niendorf², Kim-Lien Nguyen⁵, Min-Chi Ku², Qian Tao³, and Hsin-Jung Yang¹

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ³Department of Imaging Physics, Delft University of Technology, Delft, Netherlands, ⁴Division of Cardiovascular Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan, ⁵School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Analysis/Processing, Cardiovascular

Motivation: Multi-parametric mapping has become a viable tool for myocardial tissue characterization. However, misregistration between multi-parametric maps makes pixel-wise analysis challenging.

Goal(s): To develop a registration pipeline for accurate registration of quantitative maps acquired at different scans.

Approach: A cascaded self-supervised registration pipeline was developed, integrating a generalizable physics-informed module and a two-level contrast-agnostic groupwise registration module to optimize model efficiency and minimize bias from physical constraints.

Results: The model demonstrates high registration quality for T₁ mapping of post-Gd administration kinetics, enabling reliable registration of dynamic mapping with varying T₁contrast.

Impact: The cascaded self-supervised pipeline with a physics-informed module offers a scalable framework to facilitate accurate and efficient image registration for image contrast modulation following multiple physical and physiological models.

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