@article {946,
	title = {A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {210},
	year = {2021},
	pages = {106371},
	issn = {0169-2607},
	doi = {10.1016/j.cmpb.2021.106371},
	url = {https://doi.org/10.1016/j.cmpb.2021.106371},
	author = {Moya-S{\'a}ez, Elisa and {\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos}
}
@proceedings {723,
	title = {Determination of the optimal set of b-values for ADC mapping under a Rician noise assumption},
	year = {2017},
	pages = {3341},
	address = {Honolulu, HI, USA},
	abstract = {<p>Mapping of the apparent diffusion coefficient (ADC), estimated from a set of diffusion-weighted (DW) images acquired with different b-values, often suffers from low SNR, which can introduce large variance in ADC maps. Unfortunately, there is no consensus on the optimal b-values to maximize the noise performance of ADC map. In this work, we determine the optimal b-values to maximize the noise performance of ADC mapping by using a Cram{\'e}r-Rao Lower Bound (CRLB) approach under realistic noise assumptions. The strong agreement between the CRLB-based analysis, Monte-Carlo simulations, and ADC phantom experiment, suggests the utility of this approach to optimize DW-MRI acquisitions.</p>
},
	author = {{\'O}scar Pe{\~n}a-Nogales and Diego Hernando and Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a}
}
