Time-resolved aortic 3D shape reconstruction from a limited number of cine 2D MRI slices

TL;DR

This study demonstrates a method to accurately reconstruct 3D aortic geometries over time from limited 2D MRI slices using a statistical shape model and mesh optimization, enabling detailed analysis with minimal imaging data.

Contribution

The paper introduces a novel framework combining statistical shape modeling and differentiable mesh optimization for time-resolved 3D aortic reconstruction from few 2D MRI slices.

Findings

Accurate 3D aortic reconstructions achieved with as few as six MRI slices.

High agreement with 4D flow MRI reference data (Dice score ~90%).

Detected age-related differences in aortic strain and geometry.

Abstract

Background and Objective: To assess the feasibility and accuracy of reconstructing time-resolved, three-dimensional, subject-specific aortic geometries from a limited number of standard cine 2D magnetic resonance imaging (MRI) acquisitions. This is achieved by coupling a statistical shape model with a differentiable volumetric mesh optimization algorithm. Methods: Cine 2D MRI slices were manually segmented and used to reconstruct subject-specific aortic geometries via a differentiable mesh optimization algorithm, constrained by a statistical shape model. Optimal slice positioning was first evaluated on synthetic data, followed by in-vivo acquisition in 30 subjects (19 volunteers and 11 aortic stenosis patients). Time-resolved aortic geometries were reconstructed, from which geometric descriptors and radial strain were derived. In a subset of 10 subjects, 4D flow MRI data was acquired to provide volumetric reference for peak-systolic shape comparison. Results: Accurate reconstruction was achieved using as few as six cine 2D MRI slices. Agreement with 4D flow MRI reference data yielded a Dice score of (89.9 +/- 1.6) %, Intersection over Union of (81.7 +/- 2.7) %, Hausdorff distance of (7.3 +/- 3.3) mm, and Chamfer distance of (3.7 +/- 0.6) mm. The mean absolute radius error along the aortic arch was (0.8 +/- 0.6) mm. Secondary analysis demonstrated significant differences in geometric features and radial strain across age groups, with strain decreasing progressively with age at values of (11.00 +/- 3.11) x 10-2 vs. (3.74 +/- 1.25) x 10-2 vs. (2.89 +/- 0.87) x 10-2 for the young, mid-age, and elderly groups, respectively. Conclusion: The proposed framework enables reconstruction of time-resolved, subject-specific aortic geometries from a limited number of standard cine 2D MRI acquisitions, providing a practical basis for downstream computational analysis.