@article {954,
	title = {Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA},
	journal = {Medical Image Analysis},
	volume = {77},
	year = {2022},
	pages = {102356},
	abstract = {<p>AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.</p>
},
	keywords = {AMURA, Diffusion anisotropy, Fast acquisition, diffusion MRI, white matter},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2022.102356},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841522000093},
	author = {Aja-Fern{\'a}ndez, Santiago and Pieciak, Tomasz and Mart{\'\i}n-Mart{\'\i}n, Carmen and {\'A}lvaro Planchuelo-G{\'o}mez and de Luis-Garc{\'\i}a, Rodrigo and Trist{\'a}n-Vega, Antonio}
}
@article {934,
	title = {Accurate free-water estimation in white matter from fast diffusion MRI acquisitions using the spherical means technique},
	journal = {Magnetic Resonance in Medicine},
	volume = {87},
	year = {2021},
	month = {2022},
	pages = {1028-1035},
	type = {Techncial Note},
	abstract = {<p>Purpose To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of different b-values. Data sets considered comprise 32-64 gradients near plus 6 gradients near . Theory and Methods The spherical means of each diffusion MRI set with the same b-value are computed. These means are related to the inherent diffusion parameters within the voxel (free- and cellular-water fractions; cellular-water diffusivity), which are solved by constrained nonlinear least squares regression. Results The proposed method outperforms those based on mixtures of two Gaussians for the kind of data sets considered. W.r.t. the accuracy, the former does not introduce significant biases in the scenarios of interest, while the latter can reach a bias of 5\%{\textendash}7\% if fiber crossings are present. W.r.t. the precision, a variance near , compared to 15\%, can be attained for usual configurations. Conclusion It is possible to compute reliable estimates of the free-water fraction inside the white matter by complementing typical DTI acquisitions with few gradients at a lowb-value. It can be done voxel-by-voxel, without imposing spatial regularity constraints.</p>
},
	keywords = {diffusion MRI, free water, spherical means, white matter},
	doi = {https://doi.org/10.1002/mrm.28997},
	author = {Antonio Trist{\'a}n-Vega and Guillem Par{\'\i}s and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez}
}
