Optimized Response Function Estimation for Spherical Deconvolution
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Optimized Response Function Estimation for Spherical Deconvolution. / Dela Haije, Tom; Feragen, Aasa.
Computational Diffusion MRI. Springer VS, 2020. s. 25-34 (Mathematics and Visualization).Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
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TY - CHAP
T1 - Optimized Response Function Estimation for Spherical Deconvolution
AU - Dela Haije, Tom
AU - Feragen, Aasa
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Constrained spherical deconvolution (CSD) is the most widely used algorithm to estimate fiber orientations for tractography in diffusion-weighted magnetic resonance imaging. CSD models the diffusion-weighted signal as the convolution of a fiber orientation distribution function and a “single fiber response function”, representing the signal profile of a population of aligned fibers. The performance of CSD relies crucially on the robust and accurate estimation of this response function, which is typically done by aligning and averaging a set of noisy, rotated single fiber signals. We show that errors in the alignment step of this procedure lead to an observable bias, and introduce an alternative algorithm based on rotational invariants that entirely avoids the problematic alignment step. The corresponding estimator is proven to be unbiased and consistent, which is verified experimentally.
AB - Constrained spherical deconvolution (CSD) is the most widely used algorithm to estimate fiber orientations for tractography in diffusion-weighted magnetic resonance imaging. CSD models the diffusion-weighted signal as the convolution of a fiber orientation distribution function and a “single fiber response function”, representing the signal profile of a population of aligned fibers. The performance of CSD relies crucially on the robust and accurate estimation of this response function, which is typically done by aligning and averaging a set of noisy, rotated single fiber signals. We show that errors in the alignment step of this procedure lead to an observable bias, and introduce an alternative algorithm based on rotational invariants that entirely avoids the problematic alignment step. The corresponding estimator is proven to be unbiased and consistent, which is verified experimentally.
KW - Alignment
KW - Constrained spherical deconvolution
KW - Diffusion MRI
KW - Invariant
KW - Response function estimation
KW - Spherical harmonics
UR - http://www.scopus.com/inward/record.url?scp=85095862681&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-52893-5_3
DO - 10.1007/978-3-030-52893-5_3
M3 - Book chapter
AN - SCOPUS:85095862681
T3 - Mathematics and Visualization
SP - 25
EP - 34
BT - Computational Diffusion MRI
PB - Springer VS
ER -
ID: 271603652