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

Author:

Liang Zifei,Zhang Jiangyang

Abstract

IntroductionThe 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.MethodsIn 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.ResultsWe found that the best SR performance was obtained when the resolutions of training and target data were matched. We then applied the network trained using AF data to MRI data of the mouse brain, and found that the performance of the SR network depended on the tissue contrast presented in the MRI data. Using transfer learning and a limited set of high-resolution mouse brain MRI data, we were able to fine-tune the initial network trained using AF to enhance the resolution of MRI data.DiscussionOur results suggest that deep learning SR networks trained using high-resolution data of a different modality can be applied to MRI data after transfer learning.

Publisher

Frontiers Media SA

Subject

General Medicine

Reference67 articles.

1. Diffusion-weighted MR imaging of acute stroke: correlation with T2-weighted and magnetic susceptibility-enhanced MR imaging in cats;Moseley;AJNR Am J Neuroradiol,1990

2. Open science CBS neuroimaging repository: sharing ultra-high-field MR images of the brain;Tardif;Neuroimage,2016

3. Pros and cons of ultra-high-field MRI/MRS for human application;Ladd;Prog Nucl Magn Reson Spectrosc,2018

4. Ultra-high-field MR neuroimaging;Balchandani;AJNR Am J Neuroradiol,2015

5. Connectome 2.0: developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome;Huang;Neuroimage,2021

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