Learning Volumetric Neural Deformable Models to Recover 3D Regional Heart Wall Motion from Multi-Planar Tagged MRI

TL;DR

This paper introduces volumetric neural deformable models that accurately recover 3D heart wall motion from multi-planar 2D MRI data by combining global deformation functions and a novel hybrid point transformer for data fusion.

Contribution

The paper proposes a new volumetric neural deformable model and a hybrid point transformer to improve 3D heart motion recovery from 2D MRI data, addressing incomplete sampling and information fusion challenges.

Findings

High accuracy in dense 3D motion recovery from sparse 2D cues.

Effective fusion of multi-planar apparent motion cues.

Validated on synthetic 3D heart motion dataset.

Abstract

Multi-planar tagged MRI is the gold standard for regional heart wall motion evaluation. However, accurate recovery of the 3D true heart wall motion from a set of 2D apparent motion cues is challenging, due to incomplete sampling of the true motion and difficulty in information fusion from apparent motion cues observed on multiple imaging planes. To solve these challenges, we introduce a novel class of volumetric neural deformable models ($\upsilon$NDMs). Our $\upsilon$NDMs represent heart wall geometry and motion through a set of low-dimensional global deformation parameter functions and a diffeomorphic point flow regularized local deformation field. To learn such global and local deformation for 2D apparent motion mapping to 3D true motion, we design a hybrid point transformer, which incorporates both point cross-attention and self-attention mechanisms. While use of point cross-attention can learn to fuse 2D apparent motion cues into material point true motion hints, point self-attention hierarchically organised as an encoder-decoder structure can further learn to refine these hints and map them into 3D true motion. We have performed experiments on a large cohort of synthetic 3D regional heart wall motion dataset. The results demonstrated the high accuracy of our method for the recovery of dense 3D true motion from sparse 2D apparent motion cues. Project page is at https://github.com/DeepTag/VolumetricNeuralDeformableModels.