AUTHOR=Giraldo Diana L. , Khan Hamza , Pineda Gustavo , Liang Zhihua , Lozano-Castillo Alfonso , Van Wijmeersch Bart , Woodruff Henry C. , Lambin Philippe , Romero Eduardo , Peeters Liesbet M. , Sijbers Jan 

TITLE=Perceptual super-resolution in multiple sclerosis MRI

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 18 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1473132

DOI=10.3389/fnins.2024.1473132

ISSN=1662-453X

ABSTRACT=IntroductionMagnetic 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).MethodsOur 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.ResultsExtensive evaluation with MRI data of PwMS shows that our SR strategy leads to more accurate MRI reconstructions than competing methods. Furthermore, it improves lesion segmentation on low-resolution MRI, approaching the performance achievable with high-resolution images.DiscussionResults demonstrate the potential of our SR framework to facilitate the use of low-resolution retrospective MRI from real-world clinical settings to investigate quantitative image-based biomarkers of MS.