Abstract
Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac func- tion. However, current techniques require either human-adjusted post- processing with suboptimal regional accuracy, or specialized acquisitions with limited availability. In this paper, we propose to leverage the power of generative models to synthesize high-quality motion-derived strain values from routinely acquired CMR sequences. Specifically, we develop a novel Brownian bridge diffusion model in motion space to learn the probabilistic mapping between standard CMR motion estimated from widely adopted registration methods and highly accurate motion pro- vided by advanced strain imaging techniques. To promote the fidelity of anatomical structure in the generation process, our model is condi- tioned on the corresponding CMR images. We validate our method on large-scale multi-center CMR datasets including subjects of paired stan- dard cine CMR and advanced strain imaging acquisitions. Experimen- tal results demonstrate that our framework significantly improves the accuracy of motion prediction and strain analysis from standard CMRs compared to existing learning-based approaches. Our research represents a new paradigm for potentially developing cost-effective, clinically de- ployable AI tools for cardiac function assessment with enhanced strain accuracy in busy clinical workflows. Our code is publicly available at https://github.com/Rishov-MIA/Brownian-Bridge-strain-analysis.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/STACOM2026_003.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Rishov_Paul1
BibTex
@InProceedings{PauRis_Generative_MICCAISAT2026,
author = { Paul, Rishov AND Epstein, Frederick H. AND Zhang, Miaomiao},
title = { { Generative Brownian Bridge Diffusion In Motion Space For Enhanced Myocardial Strain Analysis } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17267},
month = {pending},
page = {pending}
}