Perceptual super-resolution in multiple sclerosis MRI.

ABSTRACT:

Introduction

Magnetic resonance imaging (MRI) is crucial for diagnosing and monitoring of multiple sclerosis (MS) as it is used to assess lesions in the brain and spinal cord. However, in real-world clinical settings, MRI scans are often acquired with thick slices, limiting their utility for automated quantitative analyses. This work presents a single-image super-resolution (SR) reconstruction framework that leverages SR convolutional neural networks (CNN) to enhance the through-plane resolution of structural MRI in people with MS (PwMS).

Methods

Our strategy involves the supervised fine-tuning of CNN architectures, guided by a content loss function that promotes perceptual quality, as well as reconstruction accuracy, to recover high-level image features.

Results

Exte

SUBMITTER: Giraldo DL 

PROVIDER: S-EPMC11534588 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature