Optimized Response Function Estimation for Spherical Deconvolution

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Standard

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

Harvard

Dela Haije, T & Feragen, A 2020, Optimized Response Function Estimation for Spherical Deconvolution. i Computational Diffusion MRI. Springer VS, Mathematics and Visualization, s. 25-34. https://doi.org/10.1007/978-3-030-52893-5_3

APA

Dela Haije, T., & Feragen, A. (2020). Optimized Response Function Estimation for Spherical Deconvolution. I Computational Diffusion MRI (s. 25-34). Springer VS. Mathematics and Visualization https://doi.org/10.1007/978-3-030-52893-5_3

Vancouver

Dela Haije T, Feragen A. Optimized Response Function Estimation for Spherical Deconvolution. I Computational Diffusion MRI. Springer VS. 2020. s. 25-34. (Mathematics and Visualization). https://doi.org/10.1007/978-3-030-52893-5_3

Author

Dela Haije, Tom ; Feragen, Aasa. / Optimized Response Function Estimation for Spherical Deconvolution. Computational Diffusion MRI. Springer VS, 2020. s. 25-34 (Mathematics and Visualization).

Bibtex

@inbook{ea7e6a91e4684880b16b8b4ad948e201,
title = "Optimized Response Function Estimation for Spherical Deconvolution",
abstract = "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.",
keywords = "Alignment, Constrained spherical deconvolution, Diffusion MRI, Invariant, Response function estimation, Spherical harmonics",
author = "{Dela Haije}, Tom and Aasa Feragen",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.",
year = "2020",
doi = "10.1007/978-3-030-52893-5_3",
language = "English",
series = "Mathematics and Visualization",
publisher = "Springer VS",
pages = "25--34",
booktitle = "Computational Diffusion MRI",

}

RIS

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

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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