TY - JOUR
T1 - XQ-SR
T2 - Joint x-q space super-resolution with application to infant diffusion MRI
AU - Chen, Geng
AU - Dong, Bin
AU - Zhang, Yong
AU - Lin, W.
AU - Shen, Dinggang
AU - Yap, Pew Thian
N1 - Funding Information:
This work was supported in part by NIH grants (NS093842, EB022880, EB006733, and 1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium. Data were provided in part by the Human Connectome Project, WU–Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research ; and by the McDonnell Center for Systems Neuroscience at Washington University.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/10
Y1 - 2019/10
N2 - Diffusion MRI (DMRI) is a powerful tool for studying early brain development and disorders. However, the typically low spatio–angular resolution of DMRI diminishes structural details and limits quantitative analysis to simple diffusion models. This problem is aggravated for infant DMRI since (i) the infant brain is significantly smaller than that of an adult, demanding higher spatial resolution to capture subtle structures; and (ii) the typically limited scan time of unsedated infants poses significant challenges to DMRI acquisition with high spatio–angular resolution. Post–acquisition super–resolution (SR) is an important alternative for increasing the resolution of DMRI data without prolonging acquisition times. However, most existing methods focus on the SR of only either the spatial domain (x–space) or the diffusion wavevector domain (q–space). For more effective resolution enhancement, we propose a framework for joint SR in both spatial and wavevector domains. More specifically, we first establish the signal relationships in x–q space using a robust neighborhood matching technique. We then harness the signal relationships to regularize the ill–posed inverse problem associated with the recovery of high–resolution data from their low–resolution counterpart. Extensive experiments on synthetic, adult, and infant DMRI data demonstrate that our method is able to recover high–resolution DMRI data with remarkably improved quality.
AB - Diffusion MRI (DMRI) is a powerful tool for studying early brain development and disorders. However, the typically low spatio–angular resolution of DMRI diminishes structural details and limits quantitative analysis to simple diffusion models. This problem is aggravated for infant DMRI since (i) the infant brain is significantly smaller than that of an adult, demanding higher spatial resolution to capture subtle structures; and (ii) the typically limited scan time of unsedated infants poses significant challenges to DMRI acquisition with high spatio–angular resolution. Post–acquisition super–resolution (SR) is an important alternative for increasing the resolution of DMRI data without prolonging acquisition times. However, most existing methods focus on the SR of only either the spatial domain (x–space) or the diffusion wavevector domain (q–space). For more effective resolution enhancement, we propose a framework for joint SR in both spatial and wavevector domains. More specifically, we first establish the signal relationships in x–q space using a robust neighborhood matching technique. We then harness the signal relationships to regularize the ill–posed inverse problem associated with the recovery of high–resolution data from their low–resolution counterpart. Extensive experiments on synthetic, adult, and infant DMRI data demonstrate that our method is able to recover high–resolution DMRI data with remarkably improved quality.
KW - Diffusion MRI
KW - Neighborhood matching
KW - Regularization
KW - Super resolution
UR - http://www.scopus.com/inward/record.url?scp=85068228232&partnerID=8YFLogxK
U2 - 10.1016/j.media.2019.06.010
DO - 10.1016/j.media.2019.06.010
M3 - Article
C2 - 31279215
AN - SCOPUS:85068228232
SN - 1361-8415
VL - 57
SP - 44
EP - 55
JO - Medical Image Analysis
JF - Medical Image Analysis
ER -