@proceedings {990,
	title = {Assessing the variability of brain diffusion MRI preprocessing pipelines using a Region-of-Interest analysis},
	volume = {5015},
	year = {2023},
	month = {2023},
	abstract = {<p>The lack of a standardized preprocessing pipeline is a significant source of variability that might lower the reproducibility of studies, especially across sites and with incomplete description of the preprocessing workflows. We evaluate the downstream impact of variability in preprocessing workflow by quantifying the reproducibility and variability of region-of-interest (ROI) analyses. While many pipelines achieve excellent reproducibility in most ROI, we observed a large variability in performance of preprocessing workflows to the extent that some pipelines are detrimental to the data quality and reproducibility.</p>
},
	author = {Veraart, Jelle and Winzeck, Stephan and {\'A}lvaro Planchuelo-G{\'o}mez and Fricke, Bj{\"o}rn and Kornaropoulos, Evgenios N and Merisaari, Harri and Pieciak, Tomasz and Zou, Yukai and Descoteaux, Maxime}
}
@proceedings {988,
	title = {Comparison of data-driven and physics-informed learning approaches for optimising multi-contrast MRI acquisition protocols},
	volume = {3701},
	year = {2023},
	month = {2023},
	abstract = {<p>Multi-contrast MRI is used to assess the biological properties of tissues, but excessively long times are required to acquire high-quality datasets. To reduce acquisition time, physics-informed Machine Learning approaches were developed to select the optimal subset of measurements, decreasing the number of volumes by approximately 63\%, and predict the MRI signal and quantitative maps. These selection methods were compared to a full data-driven and two manual strategies. Synthetic and real 5D-Diffusion-T1-T2* data from five healthy participants were used. Feature selection via a combination of Machine Learning and physics modelling provides accurate estimation of quantitative parameters and prediction of MRI signal.</p>
},
	author = {{\'A}lvaro Planchuelo-G{\'o}mez and Descoteaux, Maxime and Aja-Fern{\'a}ndez, Santiago and Hutter, Jana and Jones, Derek K and Tax, Chantal M W}
}
@conference {974,
	title = {Data-driven and physics-informed learning of efficient acquisition protocols},
	booktitle = {ISMRM Workshop on Diffusion MRI: From Research to Clinic},
	year = {2022},
	address = {Amsterdam, The Netherlands},
	author = {{\'A}lvaro Planchuelo-G{\'o}mez and Descoteaux, Maxime and Aja-Fern{\'a}ndez, Santiago and Hutter, Jana and Jones, Derek K. and Tax, Chantal M.W.}
}
