@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}
}
