MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

TL;DR

This paper introduces a lightweight, fast 3D CNN architecture combined with GAN training for high-quality MRI super-resolution, outperforming existing methods in both image quality and computational efficiency.

Contribution

The paper presents a novel multi-level densely connected 3D CNN (mDCSRN) architecture with GAN-guided training, achieving superior MRI super-resolution performance.

Findings

Outperforms other deep learning methods in MRI super-resolution quality.

Provides faster processing with fewer parameters.

Generates sharp images with rich textures comparable to high-resolution MRI.

Abstract

High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information that is critical for diagnosis in the clinical application. However, HR MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio (SNR). Recent studies showed that with a deep convolutional neural network (CNN), HR generic images could be recovered from low-resolution (LR) inputs via single image super-resolution (SISR) approaches. Additionally, previous works have shown that a deep 3D CNN can generate high-quality SR MRIs by using learned image priors. However, 3D CNN with deep structures, have a large number of parameters and are computationally expensive. In this paper, we propose a novel 3D CNN architecture, namely a multi-level densely connected super-resolution network (mDCSRN), which is light-weight, fast and accurate. We also show that with the generative adversarial network (GAN)-guided training, the mDCSRN-GAN provides appealing sharp SR images with rich texture details that are highly comparable with the referenced HR images. Our results from experiments on a large public dataset with 1,113 subjects showed that this new architecture outperformed other popular deep learning methods in recovering 4x resolution-downgraded images in both quality and speed.