A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

ABSTRACT:

Background

High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these images require long acquisition times, so routinely acquired follow-up images after treatment often consist of 2D low-resolution (LR) images (with thick slices in multiple planes).

Purpose

In this work, we present a super-resolution convolutional neural network, based on previous single-image MRI super-resolution work, that can reconstruct a HR image from 2D LR slices in multiple planes in order to facilitate the extraction of structural biomarkers from routine scans.

Methods

A multilevel densely connected super-resolution

SUBMITTER: Benitez-Aurioles J 

PROVIDER: S-EPMC11880662 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature