Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks

TL;DR

This paper introduces a deep learning approach for rapid, accurate estimation of cardiac tissue thickness from MRI images, eliminating the need for computationally intensive iterative methods and aiding in disease diagnosis.

Contribution

The authors develop a novel end-to-end neural network model that estimates cardiac tissue thickness directly from raw images, significantly reducing computation time compared to traditional methods.

Findings

Achieved high accuracy and generalizability across multiple datasets.

Thickness estimation is 100 times faster than mathematical models.

Demonstrated clinical relevance by analyzing thickness patterns in cardiac pathologies.

Abstract

Accurate estimation of shape thickness from medical images is crucial in clinical applications. For example, the thickness of myocardium is one of the key to cardiac disease diagnosis. While mathematical models are available to obtain accurate dense thickness estimation, they suffer from heavy computational overhead due to iterative solvers. To this end, we propose novel methods for dense thickness estimation, including a fast solver that estimates thickness from binary annular shapes and an end-to-end network that estimates thickness directly from raw cardiac images.We test the proposed models on three cardiac datasets and one synthetic dataset, achieving impressive results and generalizability on all. Thickness estimation is performed without iterative solvers or manual correction, which is 100 times faster than the mathematical model. We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.