Optimal experimental design and estimation for q-space trajectory imaging
Morez, Jan ; Szczepankiewicz, Filip LU
; den Dekker, Arnold J.
; Vanhevel, Floris
; Sijbers, Jan
and Jeurissen, Ben
(2023)
In Human Brain Mapping
44(4).
p.1793-1809
- Abstract
- Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar... (More)
- Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4664805e-a3e4-4c7d-a966-c9e7582785b3
- author
- Morez, Jan
; Szczepankiewicz, Filip
LU
; den Dekker, Arnold J.
; Vanhevel, Floris
; Sijbers, Jan
and Jeurissen, Ben
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- acquisition, diffusion magnetic resonance imaging, optimal experimental design, parameter estimation, q-space trajectory imaging, tensor-valued diffusion encoding
- in
- Human Brain Mapping
- volume
- 44
- issue
- 4
- pages
- 17 pages
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85145095697
- pmid:36564927
- ISSN
- 1065-9471
- DOI
- 10.1002/hbm.26175
- language
- English
- LU publication?
- yes
- id
- 4664805e-a3e4-4c7d-a966-c9e7582785b3
- date added to LUP
- 2023-01-03 15:45:13
- date last changed
- 2023-10-26 14:54:38
@article{4664805e-a3e4-4c7d-a966-c9e7582785b3,
abstract = {{Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.}},
author = {{Morez, Jan and Szczepankiewicz, Filip and den Dekker, Arnold J. and Vanhevel, Floris and Sijbers, Jan and Jeurissen, Ben}},
issn = {{1065-9471}},
keywords = {{acquisition; diffusion magnetic resonance imaging; optimal experimental design; parameter estimation; q-space trajectory imaging; tensor-valued diffusion encoding}},
language = {{eng}},
number = {{4}},
pages = {{1793--1809}},
publisher = {{Wiley-Blackwell}},
series = {{Human Brain Mapping}},
title = {{Optimal experimental design and estimation for q-space trajectory imaging}},
url = {{http://dx.doi.org/10.1002/hbm.26175}},
doi = {{10.1002/hbm.26175}},
volume = {{44}},
year = {{2023}},
}