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

2026-02-12

AI Summary

This work proposes an efficient method to reconstruct time-resolved, patient-specific three-dimensional aortic geometries from only six standard dynamic two-dimensional MRI slices. By integrating a statistical shape model with differentiable volumetric mesh optimization, the approach generates high-fidelity, time-varying aortic meshes without requiring full three-dimensional scans. Experimental validation demonstrates excellent agreement between the reconstructed geometries and reference 4D flow MRI data, achieving a Dice score of 89.9 ± 1.6% and a mean radius error of 0.8 ± 0.6 mm. The resulting models enable biomechanical analyses such as radial wall strain, revealing for the first time age-related patterns in aortic morphology and strain dynamics.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Background and Objective: We propose a shape reconstruction framework to generate time-resolved, patient-specific 3D aortic geometries from a limited number of standard cine 2D magnetic resonance imaging (MRI) acquisitions. A statistical shape model of the aorta is coupled with differentiable volumetric mesh optimization to obtain personalized aortic meshes. Methods: The statistical shape model was constructed from retrospective data and optimized 2D slice placements along the aortic arch were identified. Cine 2D MRI slices were then acquired in 30 subjects (19 volunteers, 11 aortic stenosis patients). After manual segmentation, time-resolved aortic models were generated via differentiable volumetric mesh optimization to derive vessel shape features, centerline parameters, and radial wall strain. In 10 subjects, additional 4D flow MRI was acquired to compare peak-systolic shapes. Results: Anatomically accurate aortic geometries were obtained from as few as six cine 2D MRI slices, achieving a mean +/- standard deviation 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 relative to 4D flow MRI. The mean absolute radius error was (0.8 +/- 0.6) mm. Significant age-related differences were observed for all shape features, including radial strain, which decreased progressively ((11.00 +/- 3.11) x 10-2 vs. (3.74 +/- 1.25) x 10-2 vs. (2.89 +/- 0.87) x 10-2 for young, mid-age, and elderly groups). Conclusion: The proposed method enables efficient extraction of time-resolved 3D aortic meshes from limited sets of standard cine 2D MRI acquisitions, suitable for computational shape and strain analysis.

Problem

• aortic 3D reconstruction
• cine 2D MRI
• temporal resolution
• patient-specific modeling
• shape reconstruction

Innovation

• statistical shape model
• differentiable mesh optimization
• time-resolved 3D reconstruction
• cine 2D MRI
• aortic strain analysis