Groupwise Image Registration with Edge-Based Loss for Low-SNR Cardiac MRI

TL;DR

This paper introduces AiM-ED, a fast deep learning-based method for registering low-SNR cardiac MRI images that leverages edge detection to improve image quality and robustness in noisy conditions.

Contribution

AiM-ED is a novel joint registration approach that uses edge-based loss and handles multiple noisy images simultaneously, outperforming traditional methods.

Findings

AiM-ED achieves higher SNR recovery and perceptual quality metrics.

It outperforms traditional registration and VoxelMorph in noisy conditions.

The method is validated on synthetic and real clinical data.

Abstract

Purpose: To perform image registration and averaging of multiple free-breathing single-shot cardiac images, where the individual images may have a low signal-to-noise ratio (SNR). Methods: To address low SNR encountered in single-shot imaging, especially at low field strengths, we propose a fast deep learning (DL)-based image registration method, called Averaging Morph with Edge Detection (AiM-ED). AiM-ED jointly registers multiple noisy source images to a noisy target image and utilizes a noise-robust pre-trained edge detector to define the training loss. We validate AiM-ED using synthetic late gadolinium enhanced (LGE) images from the MR extended cardiac-torso (MRXCAT) phantom and free-breathing single-shot LGE images from healthy subjects (24 slices) and patients (5 slices) under various levels of added noise. Additionally, we demonstrate the clinical feasibility of AiM-ED by applying it to data from patients (6 slices) scanned on a 0.55T scanner. Results: Compared to a traditional energy-minimization-based image registration method and DL-based VoxelMorph, images registered using AiM-ED exhibit higher values of recovery SNR and three perceptual image quality metrics. An ablation study shows the benefit of both jointly processing multiple source images and using an edge map in AiM-ED. Conclusion: For single-shot LGE imaging, AiM-ED outperforms existing image registration methods in terms of image quality. With fast inference, minimal training data requirements, and robust performance at various noise levels, AiM-ED has the potential to benefit single-shot CMR applications.