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Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI.
Med Image Anal 2019; 54:122-137MI

Abstract

Accurate reconstruction of the ensemble average propagators (EAPs) from undersampled diffusion MRI (dMRI) measurements is a well-motivated, actively researched problem in the field of dMRI acquisition and analysis. A number of approaches based on compressed sensing (CS) principles have been developed for this problem, achieving a considerable acceleration in the acquisition by leveraging sparse representations of the signal. Most recent methods in literature apply undersampling techniques in the (k, q)-space for the recovery of EAP in the joint (x, r)-space. Yet, the majority of these methods follow a pipeline of first reconstructing the diffusion images in the (x, q)-space and subsequently estimating the EAPs through a 3D Fourier transform. In this work, we present a novel approach to achieve the direct reconstruction of P(x, r) from partial (k, q)-space measurements, with geometric constraints involving the parallelism of level-sets of diffusion images from proximal q-space points. By directly reconstructing P(x, r)) from (k, q)-space data, we exploit the incoherence between the 6D sensing and reconstruction domains to the fullest, which is consistent with the CS-theory. Further, our approach aims to utilize the inherent structural similarity (parallelism) of the level-sets in the diffusion images corresponding to proximally-located q-space points in a CS framework to achieve further reduction in sample complexity that could facilitate faster acquisition in dMRI. We compare the proposed method to a state-of-the-art CS based EAP reconstruction method (from joint (k, q)-space) on simulated, phantom and real dMRI data demonstrating the benefits of exploiting the structural similarity in the q-space.

Authors+Show Affiliations

Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA.Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA.Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA. Electronic address: vemuri@cise.ufl.edu.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30903964

Citation

Sun, Jiaqi, et al. "Exploiting Structural Redundancy in Q-space for Improved EAP Reconstruction From Highly Undersampled (k, Q)-space in DMRI." Medical Image Analysis, vol. 54, 2019, pp. 122-137.
Sun J, Entezari A, Vemuri BC. Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI. Med Image Anal. 2019;54:122-137.
Sun, J., Entezari, A., & Vemuri, B. C. (2019). Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI. Medical Image Analysis, 54, pp. 122-137. doi:10.1016/j.media.2019.02.014.
Sun J, Entezari A, Vemuri BC. Exploiting Structural Redundancy in Q-space for Improved EAP Reconstruction From Highly Undersampled (k, Q)-space in DMRI. Med Image Anal. 2019;54:122-137. PubMed PMID: 30903964.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI. AU - Sun,Jiaqi, AU - Entezari,Alireza, AU - Vemuri,Baba C, Y1 - 2019/03/13/ PY - 2018/02/24/received PY - 2018/11/22/revised PY - 2019/02/26/accepted PY - 2019/3/25/pubmed PY - 2019/3/25/medline PY - 2019/3/24/entrez KW - (k,q)-space KW - Compressed sensing KW - Diffusion MRI KW - Ensemble average propagator KW - Parallel level sets KW - Sparse coding SP - 122 EP - 137 JF - Medical image analysis JO - Med Image Anal VL - 54 N2 - Accurate reconstruction of the ensemble average propagators (EAPs) from undersampled diffusion MRI (dMRI) measurements is a well-motivated, actively researched problem in the field of dMRI acquisition and analysis. A number of approaches based on compressed sensing (CS) principles have been developed for this problem, achieving a considerable acceleration in the acquisition by leveraging sparse representations of the signal. Most recent methods in literature apply undersampling techniques in the (k, q)-space for the recovery of EAP in the joint (x, r)-space. Yet, the majority of these methods follow a pipeline of first reconstructing the diffusion images in the (x, q)-space and subsequently estimating the EAPs through a 3D Fourier transform. In this work, we present a novel approach to achieve the direct reconstruction of P(x, r) from partial (k, q)-space measurements, with geometric constraints involving the parallelism of level-sets of diffusion images from proximal q-space points. By directly reconstructing P(x, r)) from (k, q)-space data, we exploit the incoherence between the 6D sensing and reconstruction domains to the fullest, which is consistent with the CS-theory. Further, our approach aims to utilize the inherent structural similarity (parallelism) of the level-sets in the diffusion images corresponding to proximally-located q-space points in a CS framework to achieve further reduction in sample complexity that could facilitate faster acquisition in dMRI. We compare the proposed method to a state-of-the-art CS based EAP reconstruction method (from joint (k, q)-space) on simulated, phantom and real dMRI data demonstrating the benefits of exploiting the structural similarity in the q-space. SN - 1361-8423 UR - https://www.unboundmedicine.com/medline/citation/30903964/Exploiting_structural_redundancy_in_q_space_for_improved_EAP_reconstruction_from_highly_undersampled__k_q__space_in_DMRI_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1361-8415(19)30026-X DB - PRIME DP - Unbound Medicine ER -