@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 = {

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.

}, 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 {985, title = {Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies}, volume = {2786}, year = {2023}, month = {2023}, abstract = {

This work gathers the results of the QuadD22 challenge, held in MICCAI 2022. We evaluate whether Deep Learning (DL) Techniques are able to improve the quality of diffusion MRI data in clinical studies. To that end, we focused on a real study on migraine, where the differences between groups are drastically reduced when using 21 gradient directions instead of 61. Thus, we asked the participants to augment dMRI data acquired with only 21 directions to 61 via DL. The results were evaluated using a real clinical study with TBSS in which we statistically compared episodic migraine to chronic migraine.

}, author = {Aja-Fernandez, Santiago and Martin-Martin, Carmen and Pieciak, Tomasz and {\'A}lvaro Planchuelo-G{\'o}mez and Faiyaz, Abrar and Uddin, Nasir and Tiwari, Abhishek and Shigwan, Saurabh J and Zheng, Tianshu and Cao, Zuozhen and Blumberg, Stefano B and Sen, Snigdha and Yigit Avci, Mehmet and Li, Zihan and Wang, Xinyi and Tang, Zihao and Rauland, Amelie and Merhof, Dorit and Manzano Maria, Renata and Campos, Vinicius P and HashemiazadehKolowri, SeyyedKazem and DiBella, Edward and Peng, Chenxu and Chen, Zan and Ullah, Irfan and Mani, Merry and Eckstrom, Samuel and Baete, Steven H and Scifitto, Scifitto and Singh, Rajeev Kumar and Wu, Dan and Goodwin-Allcock, Tobias and Slator, Paddy J and Bilgic, Berkin and Tian, Qiyuan and Cabezas, Mariano and Santini, Tales and Andrade da Costa Vieira, Marcelo and Shen, Zhimin and Abdolmotalleby, Hesam and Filipiak, Patryk and Tristan-Vega, Antonio and de Luis-Garcia, Rodrigo} } @article {995, title = {Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies}, journal = {NeuroImage: Clinical}, volume = {39}, year = {2023}, pages = {103483}, abstract = {

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

}, keywords = {Angular resolution, Artificial Intelligence, Deep learning, Diffusion tensor, diffusion MRI, machine learning}, issn = {2213-1582}, doi = {https://doi.org/10.1016/j.nicl.2023.103483}, url = {https://www.sciencedirect.com/science/article/pii/S2213158223001742}, author = {Santiago Aja-Fern{\'a}ndez and Carmen Mart{\'\i}n-Mart{\'\i}n and {\'A}lvaro Planchuelo-G{\'o}mez and Abrar Faiyaz and Md Nasir Uddin and Giovanni Schifitto and Abhishek Tiwari and Saurabh J. Shigwan and Rajeev Kumar Singh and Tianshu Zheng and Zuozhen Cao and Dan Wu and Stefano B. Blumberg and Snigdha Sen and Tobias Goodwin-Allcock and Paddy J. Slator and Mehmet Yigit Avci and Zihan Li and Berkin Bilgic and Qiyuan Tian and Xinyi Wang and Zihao Tang and Mariano Cabezas and Amelie Rauland and Dorit Merhof and Renata Manzano Maria and Vin{\'\i}cius Paran{\'\i}ba Campos and Tales Santini and Marcelo Andrade da Costa Vieira and SeyyedKazem HashemizadehKolowri and Edward DiBella and Chenxu Peng and Zhimin Shen and Zan Chen and Irfan Ullah and Merry Mani and Hesam Abdolmotalleby and Samuel Eckstrom and Steven H. Baete and Patryk Filipiak and Tanxin Dong and Qiuyun Fan and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Tomasz Pieciak} } @article {953, title = {Search for schizophrenia and bipolar biotypes using functional network properties}, journal = {Brain and Behavior}, volume = {11}, year = {2021}, pages = {e2415}, abstract = {

Introduction: Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements.

Methods: A clustering analysis was performed using the electroencephalogram-based network parameters derived from graph-theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons.

Results: We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path-length, and lower small-world index compared to controls. The modulation of clustering coefficient and path-length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients{\textasciiacute} clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation.

Conclusion: These data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity.

}, keywords = {Biotypes, bipolar disorder, diffusion, electroencephalogram, network, schizophrenia}, doi = {https://doi.org/10.1002/brb3.2415}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/brb3.2415}, author = {Fern{\'a}ndez-Linsenbarth, In{\'e}s and {\'A}lvaro Planchuelo-G{\'o}mez and Be{\~n}o-Ruiz-de-la-Sierra, Rosa M. and D{\'\i}ez, Alvaro and Arjona, Antonio and P{\'e}rez, Adela and Rodr{\'\i}guez-Lorenzana, Alberto and del Valle, Pilar and de Luis-Garc{\'\i}a, Rodrigo and Mascialino, Guido and Holgado-Madera, Pedro and Segarra-Echevarr{\'\i}a, Rafael and Gomez-Pilar, Javier and N{\'u}{\~n}ez, Pablo and Bote-Boneaechea, Berta and Zambrana-G{\'o}mez, Antonio and Roig-Herrero, Alejandro and Molina, Vicente} } @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 {zhao2020doa, title = {DoA Prediction Based Beamforming with Low Training Overhead for Highly-Mobile UAV Communication with Cellular Networks}, journal = {Applied Sciences}, volume = {10}, number = {13}, year = {2020}, pages = {4420}, publisher = {Multidisciplinary Digital Publishing Institute}, author = {Zhao, Tianxiao and Luo, Chunbo and Zhou, Jianming and Guo, Dechun and Chen, Ning and Pablo Casaseca-de-la-Higuera} } @article {816, title = {Computation of exact g-factor maps in 3D GRAPPA reconstructions}, journal = {Magnetic resonance in medicine}, volume = {81}, year = {2019}, pages = {1353{\textendash}1367}, author = {I{\~n}aki Rabanillo-Viloria and Zhu, Ante and Santiago Aja-Fern{\'a}ndez and Alberola-L{\'o}pez, Carlos and Hernando, Diego} } @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} } @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} } @article {768, title = {A novel infrared video surveillance system using deep learning based techniques}, journal = {Multimedia Tools and Applications}, volume = {77}, year = {2018}, chapter = {26657}, author = {Zhang, Huaizhong and Luo, Chunbo and Wang, Qi and Kitchin, Matthew and Parmley, Andrew and Monge-Alvarez, Jesus and Pablo Casaseca-de-la-Higuera} } @article {698, title = {Abnormal Capillary Vasodynamics Contribute to Ictal Neurodegeneration in Epilepsy}, journal = {Scientific Reports}, volume = {7}, year = {2017}, abstract = {

Seizure-driven brain damage in epilepsy accumulates over time, especially in the hippocampus, which can lead to sclerosis, cognitive decline, and death. Excitotoxicity is the prevalent model to explain ictal neurodegeneration. Current labeling technologies cannot distinguish between excitotoxicity and hypoxia, however, because they share common molecular mechanisms. This leaves open the possibility that undetected ischemic hypoxia, due to ictal blood flow restriction, could contribute to neurodegeneration previously ascribed to excitotoxicity. We tested this possibility with Confocal Laser Endomicroscopy (CLE) and novel stereological analyses in several models of epileptic mice. We found a higher number and magnitude of NG2+ mural-cell mediated capillary constrictions in the hippocampus of epileptic mice than in that of normal mice, in addition to spatial coupling between capillary constrictions and oxidative stressed neurons and neurodegeneration. These results reveal a role for hypoxia driven by capillary blood flow restriction in ictal neurodegeneration. {\textcopyright} 2017 The Author(s).

}, doi = {10.1038/srep43276}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014072909\&doi=10.1038\%2fsrep43276\&partnerID=40\&md5=e9d3567266bdc360a7addc92be350c8d}, author = {Leal-Campanario, R. and Alarcon-Martinez, L. and Rieiro, H. and Martinez-Conde, S. and Alarcon-Martinez, T. and Zhao, X. and LaMee, J. and Popp, P.J. and Calhoun, M.E. and J I Arribas and Schlegel, A.A. and Di Stasi, L.L. and Rho, J.M. and Inge, L. and Otero-Millan, J. and Treiman, D.M. and Macknik, S.L.} } @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} } @conference {774, title = {Systematic infrared image quality improvement using deep learning based techniques}, booktitle = {Remote Sensing Technologies and Applications in Urban Environments}, year = {2016}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Zhang, Huaizhong and Pablo Casaseca-de-la-Higuera and Luo, Chunbo and Wang, Qi and Kitchin, Matthew and Parmley, Andrew and Monge-Alvarez, Jesus} }