@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}
}
@article {2232,
	title = {Multi-tissue spherical deconvolution of tensor-valued diffusion MRI.},
	journal = {Neuroimage},
	volume = {245},
	year = {2021},
	month = {2021 12 15},
	pages = {118717},
	abstract = {Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2~mm isotropic resolution in approximately 5:15~min.},
	keywords = {Brain Mapping, diffusion tensor imaging, Healthy Volunteers, Humans, Image Processing, Computer-Assisted, white matter},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2021.118717},
	author = {Ben Jeurissen and Szczepankiewicz, Filip}
}
@conference {Jeurissen2019-sb,
	title = {Improved precision and accuracy in q-space trajectory imaging by model-based super-resolution reconstruction},
	year = {2019},
	pages = {556},
	author = {Ben Jeurissen and Westin, Carl-Fredrik and Jan Sijbers and Szczepankiewicz, Filip}
}
@conference {Jeurissen2018-af,
	title = {Spherical deconvolution of diffusion MRI data with tensor-valued encodings},
	year = {2018},
	pages = {1559},
	author = {Ben Jeurissen and Szczepankiewicz, Filip}
}
