High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Zihao Tang; Sheng Chen; Arkiev D'Souza; Dongnan Liu; Fernando Calamante; Michael Barnett; Weidong Cai; Chenyu Wang; Mariano Cabezas

doi:10.3389/fradi.2023.1238566

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Front Radiol. 2023 Sep 11:3:1238566. doi: 10.3389/fradi.2023.1238566. eCollection 2023.

Authors

Zihao Tang^{1

2}, Sheng Chen^{1

2}, Arkiev D'Souza^{2

3}, Dongnan Liu^{1

2}, Fernando Calamante^{2

4

5}, Michael Barnett^{2

6}, Weidong Cai¹, Chenyu Wang^{2

6}, Mariano Cabezas²

Affiliations

¹ School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
² Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
³ Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
⁴ School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia.
⁵ Sydney Imaging, The University of Sydney, Sydney, NSW, Australia.
⁶ Sydney Neuroimaging Analysis Centre, Sydney, NSW, Australia.

Abstract

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

Keywords: DTI; DWI; MRI; deep learning; fractional anisotropy; high angular resolution.

Grants and funding

U54 MH091657/MH/NIMH NIH HHS/United States