@article {991, title = {HYDI-DSI revisited: constrained non-parametric EAP imaging without q-space re-gridding}, journal = {Medical Image Analysis}, volume = {84}, year = {2023}, month = {02/2023}, chapter = {102728}, abstract = {

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular ResolutionDiffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated.
We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.

}, keywords = {Diffusion Spectrum Imaging, Ensemble Average Propagator, Hybrid Diffusion Imaging, diffusion MRI}, doi = {102728}, url = {https://www.sciencedirect.com/science/article/pii/S1361841522003565}, author = {Antonio Trist{\'a}n-Vega and Tomasz Pieciak and Guillem Par{\'\i}s and Justino R. Rodr{\'\i}guez-Galv{\'a}n and Santiago Aja-Fern{\'a}ndez} } @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 {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 {844, title = {Micro-structure diffusion scalar measures from reduced MRI acquisitions}, journal = {PLOS ONE}, volume = {15}, year = {2020}, month = {2020}, pages = {1-25}, abstract = {

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.

}, 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} } @inbook {818, title = {Return-to-Axis Probability Calculation from Single-Shell Acquisitions}, booktitle = {Computational Diffusion MRI}, year = {2019}, pages = {29-41}, publisher = {Springer}, organization = {Springer}, isbn = {978-3-030-05830-2}, doi = {10.1007/978-3-030-05831-9_3}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Molendowska, Malwina and Tomasz Pieciak and Luis-Garc{\'\i}a, Rodrigo} } @conference {815, title = {Single-Shell Return-to-the-Origin Probability Diffusion Mri Measure Under a Non-Stationary Rician Distributed Noise}, booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, year = {2019}, publisher = {IEEE}, organization = {IEEE}, author = {Tomasz Pieciak and Bogusz, Fabian and Antonio Trist{\'a}n-Vega and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez} } @conference {752, title = {Bias Correction for Non-Stationary Noise Filtering in MRI}, booktitle = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI)}, year = {2018}, month = {2018}, address = {Washington DC}, author = {Tomasz Pieciak and I{\~n}aki Rabanillo-Viloria and Santiago Aja-Fern{\'a}ndez} } @proceedings {759, title = {Return-to-the-origin probability calculation in single shell acquisitions}, year = {2018}, pages = {1414}, address = {Paris, France}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Malwina Molendowska and Tomasz Pieciak and Rodrigo de Luis-Garc{\'\i}a} } @article {779, title = {Robust Estimation of the Apparent Diffusion Coefficient Invariant to Acquisition Noise and Physiological Motion}, journal = {Magnetic Resonance Imaging}, volume = {53}, year = {2018}, pages = {123-133}, abstract = {

In this work we have proposed a methodology for the estimation of the apparent diffusion coefficient in the body from multiple breath hold diffusion weighted images, which is robust to two preeminent confounding factors: noise and motion during acquisition. We have extended a method for the joint groupwise multimodal registration and apparent diffusion coefficient estimation, previously proposed by the authors, in order to correct the bias that arises from the non-Gaussianity of the data and the registration procedure. Results show that the proposed methodology provides a statistically significant improvement both in robustness for displacement fields calculation and in terms of accuracy for the apparent diffusion coefficient estimation as compared with traditional sequential approaches. Reproducibility has also been measured on real data in terms of the distribution of apparent diffusion coefficient differences obtained from different b-values subsets. Our proposal has shown to be able to effectively correct the estimation bias by introducing additional computationally light procedures to the original method, thus providing robust apparent diffusion coefficient maps in the liver and allowing an accurate and reproducible analysis of the tissue.

}, keywords = {Acquisition Noise, Apparent Diffusion Coefficient, Diffusion Weighted Imaging, Multimodal Groupwise Registration, Patient Movement Correction}, doi = {https://doi.org/10.1016/j.mri.2018.07.005}, url = {https://www.sciencedirect.com/science/article/pii/S0730725X18300687}, author = {Santiago Sanz-Est{\'e}banez and Tomasz Pieciak and Carlos Alberola-L{\'o}pez and Santiago Aja-Fern{\'a}ndez} } @article {781, title = {Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers}, journal = {Magnetic Resonance Imaging}, volume = {54}, year = {2018}, month = {2018}, pages = {194 - 213}, issn = {0730-725X}, doi = {https://doi.org/10.1016/j.mri.2018.03.001}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X18300262}, author = {Santiago Aja-Fern{\'a}ndez and Tomasz Pieciak and Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Vicente Molina and Rodrigo de Luis-Garc{\'\i}a} } @article {666, title = {Non-Stationary Rician Noise Estimation in Parallel MRI using a Single Image: a Variance-Stabilizing Approach}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {39}, year = {2017}, month = {2017}, pages = {2015-2029}, chapter = {2015}, abstract = {

Parallel magnetic resonance imaging (pMRI) techniques have gained a great importance both in research and clinical communities recently since they considerably accelerate the image acquisition process. However, the image reconstruction algorithms needed to correct the subsampling artifacts affect the nature of noise, i.e. it becomes non-stationary. Some methods have been proposed in the literature dealing with the non-stationary noise in pMRI. However, their performance depends on information not usually available such as multiple acquisitions, receiver noise matrices, sensitivity coil profiles, reconstruction coefficients, or even biophysical models of the data. Besides, some methods show an undesirable granular pattern on the estimates as a side effect of local estimation. Finally, some methods make strong assumptions that just hold in the case of high signal-to-noise ratio (SNR), which limits their usability in real scenarios. We propose a new automatic noise estimation technique for non-stationary Rician noise that overcomes the aforementioned drawbacks. Its effectiveness is due to the derivation of a variance-stabilizing transformation designed to deal with any SNR. The method was compared to the main state-of-the-art methods in synthetic and real scenarios. Numerical results confirm the robustness of the method and its better performance for the whole range of SNRs.

}, issn = {0162-8828}, doi = {10.1109/TPAMI.2016.2625789}, author = {Tomasz Pieciak and Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas S{\'a}nchez-Ferrero} } @conference {655, title = {Variance Stabilization of Noncentral-Chi Data: Application to Noise Estimation in MRI}, booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 2016}, year = {2016}, month = {2016}, address = {Prague, Czech Republic}, author = {Tomasz Pieciak and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {513, title = {Spatially variant noise estimation in MRI: A homomorphic approach}, journal = {Medical Image Analysis}, volume = {20}, year = {2015}, pages = {184 - 197}, doi = {http://dx.doi.org/10.1016/j.media.2014.11.005}, author = {Santiago Aja-Fern{\'a}ndez and Tomasz Pieciak and Gonzalo Vegas-S{\'a}nchez-Ferrero} }