4D Myocardium Reconstruction with Decoupled Motion and Shape Model

TL;DR

This paper introduces a novel 4D myocardium reconstruction method that decouples motion and shape, effectively handling sparse 2D cine MRI slices to improve shape and motion estimation for clinical use.

Contribution

It presents the first 4D myocardial dataset and a decoupled motion-shape model that enhances reconstruction accuracy from limited slice data without ground truth deformation supervision.

Findings

Superior reconstruction performance demonstrated on multiple datasets

Enables clinical applications with improved shape and motion estimation

Introduces a new public 4D myocardial dataset

Abstract

Estimating the shape and motion state of the myocardium is essential in diagnosing cardiovascular diseases.However, cine magnetic resonance (CMR) imaging is dominated by 2D slices, whose large slice spacing challenges inter-slice shape reconstruction and motion acquisition.To address this problem, we propose a 4D reconstruction method that decouples motion and shape, which can predict the inter-/intra- shape and motion estimation from a given sparse point cloud sequence obtained from limited slices. Our framework comprises a neural motion model and an end-diastolic (ED) shape model. The implicit ED shape model can learn a continuous boundary and encourage the motion model to predict without the supervision of ground truth deformation, and the motion model enables canonical input of the shape model by deforming any point from any phase to the ED phase. Additionally, the constructed ED-space enables pre-training of the shape model, thereby guiding the motion model and addressing the issue of data scarcity. We propose the first 4D myocardial dataset as we know and verify our method on the proposed, public, and cross-modal datasets, showing superior reconstruction performance and enabling various clinical applications.