@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 {810, title = {Motion-Robust and Blood-Suppressed M1-Optimized Diffusion MR Imaging of the Liver}, volume = {116}, year = {2019}, abstract = {

Liver DWI has been shown to enable the detection, characterization and treatment monitoring of focal liver lesions, as well as the assessment of diffuse liver disease (eg: fibrosis and cirrhosis)1,2. However, liver DWI is challenging because of the relatively short T2 of liver tissue and the motion sensitivity of diffusion encoding sequences3,4. Recently, advanced motion-robust DW gradient waveform design techniques5-7 enabled first motion moment-nulled (M1-nulled) and/or second motion moment nulled (M2-nulled) DWI with optimized echo time (TE). However, these motion moment-nulled gradient waveforms also compensate the signal from moving blood, which is nulled in standard liver DWI. Importantly, non-suppressed blood signal can mimic focal liver lesions and may confound the assessment and detection of true focal lesions in DWI, as well as introduce bias and variability in quantitative diffusion measures. Consequently, the lack of blood suppression in motion moment-nulled DWI techniques may hinder their clinical applicability for liver DWI.

}, author = {Yuxin Zhang and {\'O}scar Pe{\~n}a-Nogales and James H. Holmes and Diego Hernando} } @article {817, title = {Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling}, journal = {Magnetic resonance in medicine}, volume = {81}, year = {2019}, pages = {989{\textendash}1003}, author = {{\'O}scar Pe{\~n}a-Nogales and Zhang, Yuxin and Wang, Xiaoke and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Holmes, James H and Hernando, Diego} } @proceedings {809, title = {Reduced Eddy Current induced image distortions and Peripheral Nerve Stimulation based on the Optimal Diffusion-weighting Gradient Waveform Design (ODGD) formulation}, volume = {3488}, year = {2019}, abstract = {

Diffusion-Weighted MRI (DW-MRI) often suffers from signal attenuation due to long TE, motion-related artefacts, dephasing due to concomitant gradients (CGs), and image distortions due to eddy currents (ECs). Further, the application of rapidly switching gradients may cause peripheral nerve stimulation (PNS). These challenges hinder the progress, application and interpretability of DW-MRI. Therefore, based on the Optimized Diffusion-weighting Gradient waveforms Design (ODGD) formulation, in this work we design optimal (minimum TE) nth-order moment-nulling diffusion-weighting gradient waveforms with or without CG-nulling able to reduce EC induced distortions and PNS-effects. We assessed the feasibility of these waveforms in simulations and phantom experiments.

}, author = {{\'O}scar Pe{\~n}a-Nogales and Yuxin Zhang and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and James H. Holmes and Diego Hernando} } @article {794, title = {Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson{\textquoteright}s Disease}, journal = {Frontiers in Neuroscience}, volume = {12}, year = {2018}, pages = {967}, author = {{\'O}scar Pe{\~n}a-Nogales and Ellmore, Timothy Michael and Rodrigo de Luis-Garc{\'\i}a and Suescun, Jessika and Schiess, Mya Caryn and Giancardo, Luca} } @proceedings {760, title = {Optimal Diffusion-weighting Gradient Waveform Design (ODGD): Formulation and Experimental Validation}, year = {2018}, pages = {685}, address = {Paris, France}, author = {{\'O}scar Pe{\~n}a-Nogales and Yuxin Zhang and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and James H. Holmes and Diego Hernando} } @article {793, title = {Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling}, journal = {Magnetic resonance in medicine}, year = {2018}, author = {{\'O}scar Pe{\~n}a-Nogales and Zhang, Yuxin and Wang, Xiaoke and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Holmes, James H and Hernando, Diego} } @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 = {

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.

}, author = {{\'O}scar Pe{\~n}a-Nogales and Diego Hernando and Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a} } @conference {689, title = {Effect of sampling on the estimation of the apparent coefficient of diffusion in MRI}, booktitle = {ICASSP 2017}, year = {2017}, month = {2017}, publisher = {IEEE signal processing Society}, organization = {IEEE signal processing Society}, address = {New Orleans, LA}, author = {Santiago Aja-Fern{\'a}ndez and {\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a} } @conference {700, title = {Groupwise Non-Rigid Registration on Multiparametric Abdominal DWI Acquisitions for Robust ADC Estimation: Comparison with Pairwise Approaches and Different Multimodal Metrics}, booktitle = {International Symposium on Biomedical Imaging: From Nano to Macro (ISBI2017)}, year = {2017}, month = {2017}, address = {Melbourne, Australia}, abstract = {

Registration of diffusion weighted datasets remains a challenging\ task in the process of quantifying diffusion indexes.\ Respiratory and cardiac motion, as well as echo-planar characteristic\ geometric distortions, may greatly limit accuracy on\ parameter estimation, specially for the liver. This work proposes\ a methodology for the non-rigid registration of multiparametric\ abdominal diffusion weighted imaging by using\ different well-known metrics under the groupwise paradigm.\ A three-stage validation of the methodology is carried out on\ a computational diffusion phantom, a watery solution phantom\ and a set of voluntary patients. Diffusion estimation\ accuracy has been directly calculated on the computational\ phantom and indirectly by means of a residual analysis on\ the real data. On the other hand, effectiveness in distortion\ correction has been measured on the phantom. Results have\ shown statistical significant improvements compared to pairwise\ registration being able to cope with elastic deformations.

}, author = {Santiago Sanz-Est{\'e}banez and {\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @proceedings {725, title = {Monte-Carlo Analysis of Quantitative Diffusion Measurements Using Motion-Compensated Diffusion Weighting Waveforms}, year = {2017}, pages = {1733}, address = {Honolulu, HI, USA}, abstract = {

Advanced diffusion MRI acquisition strategies based on motion-compensated diffusion-econding waveforms have been proposed to reduce the signal voids caused by tissue motion. However, quantitative diffusion measurements obtained from these motion-compensated waveforms may be baised relative to standard monopolar gradient waveforms. This study evaluated the effect of different diffusion encoding gradient waveforms on the signal decay and diffusion measurements, using Monte-Carlo simulations with different microstructures and several reconstruction signal models. The results show substantial bias in observed signal decay and quantiative diffusion measurements in the same microstructure across different gradient waveforms, in the presence of restricted diffusion.

}, author = {Yuxin Zhang and {\'O}scar Pe{\~n}a-Nogales and James H. Holmes and Diego Hernando} } @proceedings {724, title = {Optimal design of motion-compensated diffusion gradient waveforms }, year = {2017}, pages = {3340}, address = {Honolulu, HI, USA}, abstract = {

Diffusion-Weighted MRI (DW-MRI) often suffers from motion-related artifacts in organs that experience physiological motion. Importantly, organ motion during the application of diffusion gradients results in signal losses, which complicate image interpretation and bias quantitative measures. Motion-compensated gradient designs have been proposed, however they typically result in substantially lower b-values or severe concomitant gradient effects. In this work, we develop an approach for design of first- and second-order motion-compensated gradient waveforms based on a b-value maximization formulation including concomitant gradient nulling, and we compare it to existing techniques. The proposed design provides optimized b-values with motion compensation and concomitant gradient nulling.

}, author = {{\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Yuxin Zhang and James H. Holmes and Diego Hernando} }