@article {912,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {bioRxiv},
	year = {2021},
	month = {2021},
	doi = {10.1101/2021.03.02.433228},
	url = {https://www.biorxiv.org/content/early/2021/03/02/2021.03.02.433228},
	author = {De Luca, Alberto and Ianus, Andrada and Leemans, Alexander and Palombo, Marco and Shemesh, Noam and Zhang, Hui and Alexander, Daniel C and Nilsson, Markus and Froeling, Martijn and Biessels, Geert-Jan and Zucchelli, Mauro and Frigo, Matteo and Albay, Enes and Sedlar, Sara and Alimi, Abib and Deslauriers-Gauthier, Samuel and Deriche, Rachid and Fick, Rutger and Maryam Afzali and Tomasz Pieciak and Bogusz, Fabian and Santiago Aja-Fern{\'a}ndez and Ozarslan, Evren and Derek K. Jones and Chen, Haoze and Jin, Mingwu and Zhang, Zhijie and Wang, Fengxiang and Nath, Vishwesh and Parvathaneni, Prasanna and Morez, Jan and Sijbers, Jan and Jeurissen, Ben and Fadnavis, Shreyas and Endres, Stefan and Rokem, Ariel and Garyfallidis, Eleftherios and Sanchez, Irina and Prchkovska, Vesna and Rodrigues, Paulo and Landman, Bennet A and Schilling, Kurt G}
}
@article {933,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {NeuroImage},
	year = {2021},
	month = {2021},
	pages = {118367},
	issn = {1053-8119},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118367},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811921006431},
	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-Fern{\'a}ndez and Evren {\"O}zarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fengxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas 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}
}
@article {839,
	title = {Direction-averaged diffusion-weighted MRI signal using different axisymmetric B-tensor encoding schemes},
	journal = {Magnetic Resonance in Medicine},
	volume = {n/a},
	year = {2020},
	month = {2020},
	keywords = {B-tensor encoding, diffusion-weighted~MRI, direction-averaged diffusion signal, high b-value, power-law},
	doi = {10.1002/mrm.28191},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28191},
	author = {Maryam Afzali and Santiago Aja-Fern{\'a}ndez and Derek K. Jones}
}
@article {844,
	title = {Micro-structure diffusion scalar measures from reduced MRI acquisitions},
	journal = {PLOS ONE},
	volume = {15},
	year = {2020},
	month = {2020},
	pages = {1-25},
	abstract = {<p>In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity. In this work, we propose the so-called {\textquotedblleft}Apparent Measures Using Reduced Acquisitions{\textquotedblright} (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.</p>
},
	doi = {10.1371/journal.pone.0229526},
	url = {https://doi.org/10.1371/journal.pone.0229526},
	author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Maryam Afzali and Molendowska, Malwina and Tomasz Pieciak and Antonio Trist{\'a}n-Vega}
}
@article {914,
	title = {Q-space quantitative diffusion MRI measures using a stretched-exponential representation},
	journal = {arXiv},
	year = {2020},
	url = {https://arxiv.org/abs/2009.07376},
	author = {Tomasz Pieciak and Maryam Afzali and Fabian Bogusz and Santiago Aja-Fern{\'a}ndez and Derek K. Jones}
}
