Super-Resolution Diffusion Tensor Imaging using SRCNN: A Feasibility Study

Nahla M.H. Elsaid, Yu Chien Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High-resolution diffusion imaging with submillimeter isotropic voxels requires long scan times that are usually clinically impractical. Even with those long scans, the image quality can still suffer from low signal-to-noise ratio (SNR) and severe geometric distortion due to long echo spacing in echo-planar imaging sequences. In this study, we proposed and validated the efficacy of using a state-of-the-art deep-learning method, super-resolution convolutional neural network (SRCNN), to achieve submillimeter super-resolution diffusion-weighted (DW) images. The 2D-based deep-learning method was validated by comparing with the ground truth using numerical simulations and by studying region-of-interest (ROI) using real human data of three healthy volunteers. Furthermore, we interrogated the proposed method under different real-life SNR conditions. The results demonstrated that the proposed deep-learning method was able to reproduce sufficient details in the anatomy that can only be detected using high-resolution diffusion imaging. The percentage errors in diffusion tensor imaging (DTI) derived metrics were less than 8% when the baseline SNR larger than 20. The ROI results demonstrated that the proposed method produced comparable values of diffusion metrics to the matched high-resolution diffusion metrics of real human data. Particularly, the patterns of distributions of the subjects were similar between the proposed method and real data across whole-brain gray-matter and white-matter ROIs. A deep-learned submillimeter resolution of 0.625 mm diffusion directional image showed high image quality, particularly in the cortical gray matter. We demonstrated the feasibility of using a deep-learning algorithm based on SRCNN in DTI. This approach can be a robust alternative when acquiring the true sub-millimeter diffusion MRI is not available.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2830-2834
Number of pages5
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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    Elsaid, N. M. H., & Wu, Y. C. (2019). Super-Resolution Diffusion Tensor Imaging using SRCNN: A Feasibility Study. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 2830-2834). [8857125] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857125