doi: 10.18178/ijml.2025.15.1.1175
ARTEC: Accelerated Reconstruction of High Angular Resolution Diffusion Imaging with Trajectory Error Correction
Ashutosh Vaish1,*
,
Anubha Gupta2 ,
Ajit Rajwade2
- 1SBILab, Department of ECE, IIIT-Delhi, India
- 2Department of CSE, IIT-Bombay, India
- Manuscript receivedJune 12, 2024
- revisedJanuary 10, 2025
- acceptedJanuary 14, 2025
- publishedMarch 17, 2025
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is being increasingly used to study neural connectivity of brain regions. High Angular Resolution Diffusion Imaging (HARDI) reliably estimates the local orientations of white matter tracts for neural connectivity analysis in the brain. However, HARDI suffers from long acquisition times, limiting its clinical usage. An effective way to reduce the acquisition time is to acquire an undersampled signal followed by its compressive reconstruction. In general, due to various calibration inaccuracies in the MR scanners, acquisition trajectories in the k-space get perturbed. Although radial sampling allows significant undersampling, the trajectory errors are more pronounced with it. Hence, if the trajectory errors are not corrected, compressive reconstruction of the signal would be adversely impacted. In this work, we propose ARTEC, a joint framework of accelerated reconstruction of the HARDI signal undersampled in the joint (k-q)-space, while incorporating trajectory error corrections. Simulation results on both phantom and real data demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.
Keywords
- Diffusion MRI
- HARDI compressed sensing
- trajectory errors
- Spherical Ridgelets
How to Cite
Ashutosh Vaish, Anubha Gupta, and Ajit Rajwade, "ARTEC: Accelerated Reconstruction of High Angular Resolution Diffusion Imaging with Trajectory Error Correction," International Journal of Machine Learning, vol. 15, no. 1, pp. 29-33, 2025. https://doi.org/10.18178/ijml.2025.15.1.1175
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).