Generative Brownian Bridge Diffusion In Motion Space For Enhanced Myocardial Strain Analysis
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors developed a new AI method that can improve how doctors measure heart muscle movement using common MRI scans. Instead of needing special and expensive imaging, their model learns to turn standard MRI data into more precise strain measurements that show heart function better. They tested this on large datasets and found it worked more accurately than previous AI techniques. This approach could make heart function analysis easier and cheaper in hospitals. The authors also shared their code openly for others to use.
Myocardial strainCardiac magnetic resonance (CMR)Brownian bridge diffusion modelMotion estimationStrain imagingProbabilistic mappingImage registrationCardiac functionGenerative modelsAI in medical imaging
Authors
Rishov Paul, Frederick H. Epstein, Miaomiao Zhang
Abstract
Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function. However, current techniques require either human-adjusted post-processing with suboptimal regional accuracy, or specialized and costly imaging acquisitions. 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 provided by advanced strain imaging techniques. To promote the fidelity of anatomical structure in the generation process, our model is conditioned on the corresponding CMR images. We validate our method on large-scale multi-center CMR datasets including subjects of paired standard cine CMR and advanced strain imaging acquisitions. Experimental 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 deployable AI tools for cardiac function assessment with enhanced strain accuracy in busy clinical workflows. Our code is publicly available at anonymous.4open.science/r/Brownian-Bridge-strain-analysis-1140.