Mouse brain MR super-resolution using a deep learning network trained with optical imaging data.

ABSTRACT:

Introduction

The resolution of magnetic resonance imaging is often limited at the millimeter level due to its inherent signal-to-noise disadvantage compared to other imaging modalities. Super-resolution (SR) of MRI data aims to enhance its resolution and diagnostic value. While deep learning-based SR has shown potential, its applications in MRI remain limited, especially for preclinical MRI, where large high-resolution MRI datasets for training are often lacking.

Methods

In this study, we first used high-resolution mouse brain auto-fluorescence (AF) data acquired using serial two-photon tomography (STPT) to examine the performance of deep learning-based SR for mouse brain images.

Results

We found that the best SR performance was obtained when the resolutions of traini

SUBMITTER: Liang Z 

PROVIDER: S-EPMC10365285 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature