Paper Info
Title
Accelerated high b-value diffusion-weighted MR imaging via phase-constrained low-rank tensor model
Abstract
High b-value Diffusion-weighted MRI (DWI) is promising in cancer imaging but suffers from long acquisition time and low signal-to-noise ratio (SNR). We propose a low-rank tensor model that exploits correlation across both diffusion-induced signal decays and neighboring k-space samples, to accelerate the acquisition of DWI using an extended range of b-values (0 s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 2500 s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and limited (orthogonal only) diffusion directions, an imaging scheme that is increasingly used for brain gliomas evaluation. A phase constraint accounts for phase variations between b-values is also applied. Our method integrates parallel imaging and partial Fourier acquisition naturally, and undersamples along phase-encoding direction only. Reconstruction results using both patient and simulated data with an acceleration factor of 8 show improved SNR and reduced aliasing, as compared to parallel imaging only method as well as two other low-rank model-based methods.
Year
DOI
Venue
2018
10.1109/ISBI.2018.8363589
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Keywords
Field
DocType
cancer imaging,diffusion-weighted imaging,high b-value,constrained reconstruction,low-rank tensor
Brain gliomas,Diffusion MRI,Pattern recognition,Tensor,Computer science,Parallel imaging,Fourier transform,High-B-Value Diffusion-Weighted MR Imaging,Aliasing,Artificial intelligence,Acceleration
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-5386-3637-4
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Lianli Liu100.34
Adam Johansson200.34
James M. Balter3638.12
Yue Cao4167.85
J. A. Fessler51743229.34