A Novel Data-Driven Method for the Analysis and Reconstruction of Cardiac Cine MRI

TL;DR

This paper introduces a data-driven approach using higher order dynamic mode decomposition and singular value decomposition to analyze, reconstruct, and model cardiac MRI data, enhancing image quality and understanding heart dynamics.

Contribution

A novel algorithm combining SVD and HODMD for 3D reconstruction and reduced order modeling of cardiac MRI data, improving image reconstruction and dynamic analysis.

Findings

Effective reconstruction of corrupted or missing MRI images.

Successful creation of reduced order models of heart dynamics.

Enhanced understanding of cardiac motion patterns.

Abstract

Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood flow. This work considers the application of the higher order dynamic mode decomposition (HODMD) method to a set of MR images of a heart, with the ultimate goal of identifying the main patterns and frequencies driving the heart dynamics. A novel algorithm based on singular value decomposition combined with HODMD is introduced, providing a three-dimensional reconstruction of the heart. This algorithm is applied (i) to reconstruct corrupted or missing images, and (ii) to build a reduced order model of the heart dynamics.