TY - JOUR
T1 - MAPL1
T2 - q-space reconstruction using ℓ1-regularized mean apparent propagator
AU - Varela-Mattatall, Gabriel
AU - Castillo-Passi, Carlos
AU - Koch, Alexandra
AU - Mura, Joaquin
AU - Stirnberg, Rüdiger
AU - Uribe, Sergio
AU - Tejos, Cristian
AU - Stöcker, Tony
AU - Irarrazaval, Pablo
N1 - Funding Information:
The authors gratefully acknowledge CONICYT for funding this research through Fondecyt 1191710, ANID/PIA/ACT192064, PIA‐Anillo ACT1416, scholarship CONICYT‐PCHA/Doctorado‐Nacional/2014‐21140344; and Millenium Science Initiative of the Ministry of Economy, Development and Tourism, grant Nucleus for Cardiovascular Magnetic Resonance.
Funding Information:
Data were provided in part by the Human Connectome Project, WU‐Minn Consortium ( http://www.humanconnectomeproject.org , 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:
© 2020 International Society for Magnetic Resonance in Medicine
PY - 2020/10
Y1 - 2020/10
N2 - Purpose: To improve the quality of mean apparent propagator (MAP) reconstruction from a limited number of q-space samples. Methods: We implement an (Formula presented.) -regularised MAP (MAPL1) to consider higher order basis functions and to improve the fit without increasing the number of q-space samples. We compare MAPL1 with the least-squares optimization subject to non-negativity (MAP), and the Laplacian-regularized MAP (MAPL). We use simulations of crossing fibers and compute the normalized mean squared error (NMSE) and the Pearson’s correlation coefficient to evaluate the reconstruction quality in q-space. We also compare coefficient-based diffusion indices in the simulations and in in vivo data. Results: Results indicate that MAPL1 improves NMSE in 1 to 3% when compared to MAP or MAPL in a high undersampling regime. Additionally, MAPL1 produces more reproducible and accurate results for all sampling rates when there are enough basis functions to meet the sparsity criterion for the regularizer. These improved reconstructions also produce better coefficient-based diffusion indices for in vivo data. Conclusions: Adding an (Formula presented.) regularizer to MAP allows the use of more basis functions and a better fit without increasing the number of q-space samples. The impact of our research is that a complete diffusion spectrum can be reconstructed from an acquisition time very similar to a diffusion tensor imaging protocol.
AB - Purpose: To improve the quality of mean apparent propagator (MAP) reconstruction from a limited number of q-space samples. Methods: We implement an (Formula presented.) -regularised MAP (MAPL1) to consider higher order basis functions and to improve the fit without increasing the number of q-space samples. We compare MAPL1 with the least-squares optimization subject to non-negativity (MAP), and the Laplacian-regularized MAP (MAPL). We use simulations of crossing fibers and compute the normalized mean squared error (NMSE) and the Pearson’s correlation coefficient to evaluate the reconstruction quality in q-space. We also compare coefficient-based diffusion indices in the simulations and in in vivo data. Results: Results indicate that MAPL1 improves NMSE in 1 to 3% when compared to MAP or MAPL in a high undersampling regime. Additionally, MAPL1 produces more reproducible and accurate results for all sampling rates when there are enough basis functions to meet the sparsity criterion for the regularizer. These improved reconstructions also produce better coefficient-based diffusion indices for in vivo data. Conclusions: Adding an (Formula presented.) regularizer to MAP allows the use of more basis functions and a better fit without increasing the number of q-space samples. The impact of our research is that a complete diffusion spectrum can be reconstructed from an acquisition time very similar to a diffusion tensor imaging protocol.
KW - compressed sensing
KW - diffusion magnetic resonance imaging
KW - diffusion propagator
KW - Laplacian-regularizer
KW - mean apparent propagator
KW - q-space reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85083044721&partnerID=8YFLogxK
U2 - 10.1002/mrm.28268
DO - 10.1002/mrm.28268
M3 - Article
C2 - 32270542
AN - SCOPUS:85083044721
SN - 0740-3194
VL - 84
SP - 2219
EP - 2230
JO - Magnetic Resonance in Medicine
JF - Magnetic Resonance in Medicine
IS - 4
ER -