Invertible Sharpening Network for MRI Reconstruction Enhancement

TL;DR

This paper introduces InvSharpNet, an invertible neural network that enhances MRI image sharpness during reconstruction, focusing on improving visual quality and diagnostic confidence rather than just traditional metrics.

Contribution

The paper presents a novel invertible sharpening network with a backward training strategy that improves MRI reconstruction sharpness and visual quality, validated by radiologist evaluations.

Findings

Improved image sharpness with few artifacts.

Enhanced diagnostic confidence according to radiologists.

Effective invertible transformation for MRI sharpening.

Abstract

High-quality MRI reconstruction plays a critical role in clinical applications. Deep learning-based methods have achieved promising results on MRI reconstruction. However, most state-of-the-art methods were designed to optimize the evaluation metrics commonly used for natural images, such as PSNR and SSIM, whereas the visual quality is not primarily pursued. Compared to the fully-sampled images, the reconstructed images are often blurry, where high-frequency features might not be sharp enough for confident clinical diagnosis. To this end, we propose an invertible sharpening network (InvSharpNet) to improve the visual quality of MRI reconstructions. During training, unlike the traditional methods that learn to map the input data to the ground truth, InvSharpNet adapts a backward training strategy that learns a blurring transform from the ground truth (fully-sampled image) to the input data (blurry reconstruction). During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility. The experiments on various MRI datasets demonstrate that InvSharpNet can improve reconstruction sharpness with few artifacts. The results were also evaluated by radiologists, indicating better visual quality and diagnostic confidence of our proposed method.