@mastersthesis {2465,
	title = {Accurate and precise parameter estimation for diffusion magnetic resonance imaging},
	year = {2026},
	type = {PhD thesis},
	abstract = {			
Diffusion magnetic resonance imaging (dMRI) offers a unique way of imaging the human brain noninvasively. By carefully controlling various acquisition parameters, the dMRI signal can be used to probe the movement of water molecules inside biological tissues, revealing important information about tissue structure. However, this signal is subject to noise and other undesired electromagnetic effects that reduce the signal-to-noise ratio. Efficient acquisition schemes and robust estimators are required to obtain accurate and precise tissue maps of the human brain. In this thesis, we have aimed our efforts towards improving the accuracy, precision, generalizability and applicability of several dMRI analysis methods. Constrained spherical deconvolution (CSD) is a popular dMRI technique that can be used to infer the local tissue densities and their orientations in the human brain, which consists of cerebrospinal fluid, anisotropic white matter and isotropic gray matter. This is achieved by densely sampling q-space, the space of q-vectors representing the direction and strength of diffusion weighting. After sampling q-space in multiple shells, tissue densities and orientations can be estimated by deconvolving this multi-shell dMRI signal with the respective response functions for white matter, gray matter and cerebrospinal fluid. These response functions are typically represented using spherical harmonics (SH) basis functions. However, when sampling is nonspherical, either due to inhomogeneous gradients or by design such as Cartesian sampling, the estimated tissue maps suffer from biases. To counter this issue, we adopted a compact response function model that accounts for nonspherical sampling. On multi-shell data, our approach provides fiber orientation density functions and tissue densities indistinguishable from those estimated using SH. On Cartesian data, estimates are on par with those obtained from shell-wise data, significantly broadening the range of data sets analyzable using CSD. In addition, inhomogeneous gradients can be accounted for, resulting in more accurate apparent tissue densities and connectivity metrics. Q-space trajectory imaging is a dMRI technique that uses time-varying q-vectors to sensitize the dMRI signal to microscopic variations in heterogeneous tissues. By modelling the dMRI signal with a diffusion tensor distribution (DTD), this approach allows teasing apart variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To improve the estimation of the DTD parameters, we propose an efficient acquisition scheme optimized for the most used QTI-derived microstructural parameters. A constrained iteratively reweighted least squares estimator is used to further improve the bias and precision of the DTD parameters.},
	author = {Jan Morez}
}
@article {2274,
	title = {Optimal experimental design and estimation for q-space trajectory imaging},
	journal = {Human Brain Mapping},
	volume = {44},
	year = {2023},
	pages = {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 measures derived from the DTD. The improved precision of these schemes compared to a naive 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.},
	doi = {10.1002/hbm.26175},
	author = {Jan Morez and Szczepankiewicz, Filip and Arnold Jan den Dekker and Floris Vanhevel and Jan Sijbers and Ben Jeurissen}
}
@conference {2233,
	title = {Optimal acquisition settings for simultaneous diffusion kurtosis, free water fraction and T2 estimation},
	year = {2022},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@article {2179,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {NeuroImage},
	volume = {240},
	year = {2021},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118367},
	author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fernandez and Evren Ozarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fenxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas and Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G Schilling}
}
@conference {2182,
	title = {Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model},
	volume = {34},
	year = {2021},
	pages = {S54-S55},
	doi = {https://doi.org/10.1007/s10334-021-00947-8},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@article {2080,
	title = {Constrained spherical deconvolution of non-spherically sampled diffusion MRI data},
	journal = {Human Brain Mapping},
	volume = {42},
	year = {2020},
	pages = {521{\textendash}538},
	doi = {10.1002/hbm.25241},
	author = {Jan Morez and Jan Sijbers and Floris Vanhevel and Ben Jeurissen}
}
@conference {Morez2020-al,
	title = {Optimal experimental design for multi-tissue spherical deconvolution of diffusion MRI},
	year = {2020},
	pages = {4321},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {Morez2019-uz,
	title = {A comparison of response function tensor models for multi-tissue spherical deconvolution},
	volume = {32 (Suppl. 1)},
	year = {2019},
	pages = {S138{\textendash}S139},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {Morez2017-he,
	title = {Spherical Deconvolution of Non-Spherically Sampled Diffusion MRI Data},
	year = {2017},
	pages = {66},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {1794,
	title = {Spherical Deconvolution of Non-Spherically Sampled Diffusion MRI Data},
	year = {2017},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
