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1. Consensus-based guidance for conducting and reporting multi-analyst studies

Author

Aczel, Balazs, Szaszi, Barnabas, Nilsonne, Gustav, van den Akker, Olmo R, Albers, Casper J, van Assen, Marcel ALM, Bastiaansen, Jojanneke A, Benjamin, Daniel, Boehm, Udo, Botvinik-Nezer, Rotem, Bringmann, Laura F, Busch, Niko A, Caruyer, Emmanuel, Cataldo, Andrea M, Cowan, Nelson, Delios, Andrew, van Dongen, Noah NN, Donkin, Chris, van Doorn, Johnny B, Dreber, Anna, Dutilh, Gilles, Egan, Gary F, Gernsbacher, Morton Ann, Hoekstra, Rink, Hoffmann, Sabine, Holzmeister, Felix, Huber, Juergen, Johannesson, Magnus, Jonas, Kai J, Kindel, Alexander T, Kirchler, Michael, Kunkels, Yoram K, Lindsay, D Stephen, Mangin, Jean-Francois, Matzke, Dora, Munafò, Marcus R, Newell, Ben R, Nosek, Brian A, Poldrack, Russell A, van Ravenzwaaij, Don, Rieskamp, Jörg, Salganik, Matthew J, Sarafoglou, Alexandra, Schonberg, Tom, Schweinsberg, Martin, Shanks, David, Silberzahn, Raphael, Simons, Daniel J, Spellman, Barbara A, St-Jean, Samuel, Starns, Jeffrey J, Uhlmann, Eric Luis, Wicherts, Jelte, and Wagenmakers, Eric-Jan

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Health Sciences, Consensus, Data Analysis, Datasets as Topic, Research, multi-analyst, metascience, statistical practice, science forum, expert consensus, analytical variability, None, medicine, neuroscience, none, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

Published
2021

2. Harmonization of diffusion MRI datasets with adaptive dictionary learning

Author

St-Jean, Samuel, Viergever, Max A., and Leemans, Alexander

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Diffusion magnetic resonance imaging is a noninvasive imaging technique that can indirectly infer the microstructure of tissues and provide metrics which are subject to normal variability across subjects. Potentially abnormal values or features may yield essential information to support analysis of controls and patients cohorts, but subtle confounds affecting diffusion MRI, such as those due to difference in scanning protocols or hardware, can lead to systematic errors which could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability present in the data. Overcomplete dictionaries, which are learned automatically from the data and do not require paired samples, are then used to reconstruct the data from a different scanner, removing variability present in the source scanner in the process. We use the publicly available database from an international challenge to evaluate the method, which was acquired on three different scanners and with two different protocols, and propose a new mapping towards a scanner-agnostic space. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the effect size induced by the alterations is also preserved after harmonization. The algorithm is freely available and could help multicenter studies in pooling their data, while removing scanner specific confounds, and increase statistical power in the process., Comment: v5 Peer review for Human Brain Mapping v4: Peer review round 2 v3: Peer reviewed version v2: Fix minor text issue + add supp materials v1: To be submitted to Neuroimage

Published
2019
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3. Automated characterization of noise distributions in diffusion MRI data

Author

St-Jean, Samuel, De Luca, Alberto, Tax, Chantal M. W., Viergever, Max A., and Leemans, Alexander

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Applications, Statistics - Computation
Abstract

Knowledge of the noise distribution in diffusion MRI is the centerpiece to quantify uncertainties arising from the acquisition process. Accurate estimation beyond textbook distributions often requires information about the acquisition process, which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations were created for two diffusion weightings with parallel imaging. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired. Finally, experiments on freely available datasets are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps., Comment: v3: Peer reviewed version v2: Manuscript as submitted to Medical image analysis v1: Manuscript as submitted to Magnetic resonance in medicine

Published
2019
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4. Reducing variability in along-tract analysis with diffusion profile realignment

Author

St-Jean, Samuel, Chamberland, Maxime, Viergever, Max A., and Leemans, Alexander

Subjects
Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g. disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the HCP, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Pairwise Student's t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows., Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before peer-review which was mistakenly included v2 : peer-reviewed version v1: preprint as submitted to journal NeuroImage

Published
2019
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5. Automatic, fast and robust characterization of noise distributions for diffusion MRI

Author

St-Jean, Samuel, De Luca, Alberto, Viergever, Max A., and Leemans, Alexander

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications
Abstract

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g. coils sensitivity maps or reconstruction coefficients), which is not usually available. We introduce a new method where a change of variable naturally gives rise to a particular form of the gamma distribution for background signals. The first moments and maximum likelihood estimators of this gamma distribution explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the method automatic and robust to artifacts. Experiments on synthetic datasets show that the proposed method can reliably estimate both the degrees of freedom and the standard deviation. The worst case errors range from below 2% (spatially uniform noise) to approximately 10% (spatially variable noise). Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variances than compared methods., Comment: v2: added publisher DOI statement, fixed text typo in appendix A2

Published
2018
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6. Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising

Author

St-Jean, Samuel, Coupé, Pierrick, and Descoteaux, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Additionally, the usage of in-plane acceleration techniques during the acquisition leads to a spatially varying noise distribution, which depends on the parallel acceleration method implemented on the scanner. This paper proposes a novel diffusion MRI denoising technique that can be used on all existing data, without adding to the scanning time. We first apply a statistical framework to convert the noise to Gaussian distributed noise, effectively removing the bias. We then introduce a spatially and angular adaptive denoising technique, the Non Local Spatial and Angular Matching (NLSAM) algorithm. Each volume is first decomposed in small 4D overlapping patches to capture the structure of the diffusion data and a dictionary of atoms is learned on those patches. A local sparse decomposition is then found by bounding the reconstruction error with the local noise variance. We compare against three other state-of-the-art denoising methods and show quantitative local and connectivity results on a synthetic phantom and on an in-vivo high resolution dataset. Overall, our method restores perceptual information, removes the noise bias in common diffusion metrics, restores the extracted peaks coherence and improves reproducibility of tractography. Our work paves the way for higher spatial resolution acquisition of diffusion MRI datasets, which could in turn reveal new anatomical details that are not discernible at the spatial resolution currently used by the diffusion MRI community., Comment: Code available : https://github.com/samuelstjean/nlsam Datasets available : https://github.com/samuelstjean/nlsam_data, Medical Image Analysis, 2016

Published
2016
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7. Obtaining Representative Core Streamlines for White Matter Tractometry of the Human Brain

Author

Chamberland, Maxime, St-Jean, Samuel, Tax, Chantal M. W., Jones, Derek K., Hege, Hans-Christian, Series Editor, Hoffman, David, Series Editor, Johnson, Christopher R., Series Editor, Polthier, Konrad, Series Editor, Rumpf, Martin, Series Editor, Bonet-Carne, Elisenda, editor, Grussu, Francesco, editor, Ning, Lipeng, editor, Sepehrband, Farshid, editor, and Tax, Chantal M. W., editor

Published
2019
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8. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms

Author

Tax, Chantal MW., Grussu, Francesco, Kaden, Enrico, Ning, Lipeng, Rudrapatna, Umesh, John Evans, C., St-Jean, Samuel, Leemans, Alexander, Koppers, Simon, Merhof, Dorit, Ghosh, Aurobrata, Tanno, Ryutaro, Alexander, Daniel C., Zappalà, Stefano, Charron, Cyril, Kusmia, Slawomir, Linden, David EJ., Jones, Derek K., and Veraart, Jelle

Published
2019
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10. Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?

Author

Ning, Lipeng, Laun, Frederik, Gur, Yaniv, DiBella, Edward V.R., Deslauriers-Gauthier, Samuel, Megherbi, Thinhinane, Ghosh, Aurobrata, Zucchelli, Mauro, Menegaz, Gloria, Fick, Rutger, St-Jean, Samuel, Paquette, Michael, Aranda, Ramon, Descoteaux, Maxime, Deriche, Rachid, O’Donnell, Lauren, and Rathi, Yogesh

Published
2015
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12. Author Correction: The challenge of mapping the human connectome based on diffusion tractography

Author

Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl-Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M. W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H. Ertan, Odry, Benjamin L., Mailhe, Boris, Nadar, Mariappan S., Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E., Galvis, Justin, Thompson, Paul M., De Santiago Requejo, Francisco, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell’Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B., Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, and Descoteaux, Maxime

Published
2019
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14. The Construction Legal Hypothec: One Must Have 'Taken Part' In The Construction

Author

St-Jean, Samuel

Subjects
Pledges (Law) -- Cases -- Laws, regulations and rules, Construction industry -- Contracts -- Cases, Company legal issue, Government regulation, Contract agreement, Business, international, Quebec. Code of Civil Procedure
Abstract

The Civil Code of Québec [Code] provides special protection to businesses and workers in the construction industry: > The hypothec is created without the need to take any steps, although, [...]

Published
2022

15. The challenge of mapping the human connectome based on diffusion tractography

Author

Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl-Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M. W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H. Ertan, Odry, Benjamin L., Mailhe, Boris, Nadar, Mariappan S., Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E., Galvis, Justin, Thompson, Paul M., Requejo, Francisco De Santiago, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell’Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B., Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, and Descoteaux, Maxime

Published
2017
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16. Presentation for ISMRM 2022 - Voxelwise estimation of multiple response functions and fiber orientation distribution functions in diffusion MRI

Author

St-Jean, Samuel, Leemans, Alexander, Szczepankiewicz, Filip, Beaulieu, Christian, and Nilsson, Markus

Abstract

Voxelwise estimation of multiple response functions and fiber orientation distribution functions in diffusion MRI Samuel St-Jean, Alexander Leemans, Filip Szczepankiewicz, Christian Beaulieu, and Markus Nilsson ISMRM 2022, Abstract no. 1424

Published
2022
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18. Consensus-based guidance for conducting and reporting multi-analyst studies

Author

Aczel, Balazs, primary, Szaszi, Barnabas, primary, Nilsonne, Gustav, additional, van den Akker, Olmo R, additional, Albers, Casper J, additional, van Assen, Marcel ALM, additional, Bastiaansen, Jojanneke A, additional, Benjamin, Daniel, additional, Boehm, Udo, additional, Botvinik-Nezer, Rotem, additional, Bringmann, Laura F, additional, Busch, Niko A, additional, Caruyer, Emmanuel, additional, Cataldo, Andrea M, additional, Cowan, Nelson, additional, Delios, Andrew, additional, van Dongen, Noah NN, additional, Donkin, Chris, additional, van Doorn, Johnny B, additional, Dreber, Anna, additional, Dutilh, Gilles, additional, Egan, Gary F, additional, Gernsbacher, Morton Ann, additional, Hoekstra, Rink, additional, Hoffmann, Sabine, additional, Holzmeister, Felix, additional, Huber, Juergen, additional, Johannesson, Magnus, additional, Jonas, Kai J, additional, Kindel, Alexander T, additional, Kirchler, Michael, additional, Kunkels, Yoram K, additional, Lindsay, D Stephen, additional, Mangin, Jean-Francois, additional, Matzke, Dora, additional, Munafò, Marcus R, additional, Newell, Ben R, additional, Nosek, Brian A, additional, Poldrack, Russell A, additional, van Ravenzwaaij, Don, additional, Rieskamp, Jörg, additional, Salganik, Matthew J, additional, Sarafoglou, Alexandra, additional, Schonberg, Tom, additional, Schweinsberg, Martin, additional, Shanks, David, additional, Silberzahn, Raphael, additional, Simons, Daniel J, additional, Spellman, Barbara A, additional, St-Jean, Samuel, additional, Starns, Jeffrey J, additional, Uhlmann, Eric Luis, additional, Wicherts, Jelte, additional, and Wagenmakers, Eric-Jan, additional

Published
2021
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19. Author response: Consensus-based guidance for conducting and reporting multi-analyst studies

Author

Aczel, Balazs, primary, Szaszi, Barnabas, primary, Nilsonne, Gustav, additional, van den Akker, Olmo R, additional, Albers, Casper J, additional, van Assen, Marcel ALM, additional, Bastiaansen, Jojanneke A, additional, Benjamin, Daniel, additional, Boehm, Udo, additional, Botvinik-Nezer, Rotem, additional, Bringmann, Laura F, additional, Busch, Niko A, additional, Caruyer, Emmanuel, additional, Cataldo, Andrea M, additional, Cowan, Nelson, additional, Delios, Andrew, additional, van Dongen, Noah NN, additional, Donkin, Chris, additional, van Doorn, Johnny B, additional, Dreber, Anna, additional, Dutilh, Gilles, additional, Egan, Gary F, additional, Gernsbacher, Morton Ann, additional, Hoekstra, Rink, additional, Hoffmann, Sabine, additional, Holzmeister, Felix, additional, Huber, Juergen, additional, Johannesson, Magnus, additional, Jonas, Kai J, additional, Kindel, Alexander T, additional, Kirchler, Michael, additional, Kunkels, Yoram K, additional, Lindsay, D Stephen, additional, Mangin, Jean-Francois, additional, Matzke, Dora, additional, Munafò, Marcus R, additional, Newell, Ben R, additional, Nosek, Brian A, additional, Poldrack, Russell A, additional, van Ravenzwaaij, Don, additional, Rieskamp, Jörg, additional, Salganik, Matthew J, additional, Sarafoglou, Alexandra, additional, Schonberg, Tom, additional, Schweinsberg, Martin, additional, Shanks, David, additional, Silberzahn, Raphael, additional, Simons, Daniel J, additional, Spellman, Barbara A, additional, St-Jean, Samuel, additional, Starns, Jeffrey J, additional, Uhlmann, Eric Luis, additional, Wicherts, Jelte, additional, and Wagenmakers, Eric-Jan, additional

Published
2021
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20. Guidance for Multi-Analyst Studies

Author

St-Jean, Samuel, Dreber , Anna, Matzke , Dora, Schonberg , Tom, Spellman , Bobbie, Wagenmakers , Eric-Jan, Starns , Jeffrey, Benjamin , Daniel, Caruyer, Emmanuel, Newell , Ben, van Doorn , Johnny, Uhlmann , Eric, Van den Akker , Olmo, Mangin, Jan-Francois, Busch , Niko, Kindel, Alexander, Delios , Andrew, Munafo , Marcus, Kunkels , Yoram, Jonas , Kai, Schweinsberg , Martin, Wicherts , Jelte, Hoffmann , Sabine, Simons , Daniel, Sarafoglou , Alexandra, Gernsbacher , Morton Ann, van Assen , Marcel, Shanks , David, Hoekstra , Rink, Albers , Casper, Lindsay , D., Szaszi , Barnabas, van Dongen , Noah, Bastiaansen , Jojanneke, Kirchler , Michael, Dutilh , Gilles, Cowan , Nelson, Holzmeister, Felix, Cataldo , Andrea, Boehm , Udo, Johannesson , Magnus, Botvinik-Nezer , Rotem, Rieskamp , Jörg, Nilsonne , Gustav, Salganik , Matthew, Poldrack , Russell, Egan, Gary, van Ravenzwaaij , Don, Silberzahn , Raphael, Bringmann , Laura, Nosek , Brian, Donkin , Chris, and Aczel , Balazs

Subjects
bepress|Social and Behavioral Sciences|Other Social and Behavioral Sciences, MetaArXiv|Social and Behavioral Sciences, MetaArXiv|Social and Behavioral Sciences|Other Social and Behavioral Sciences, bepress|Social and Behavioral Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

We present consensus-based guidance for conducting and documenting multi-analyst studies. We discuss why broader adoption of the multi-analyst approach will strengthen the robustness of results and conclusions in empirical sciences.

Published
2021

21. Restoration and characterization of diffusion MRI data

Author

St-Jean, Samuel, Viergever, Max A., Leemans, Alexander, and University Utrecht

Subjects
Denoising, Along-tract analysis, Harmonization, White matter, Dictionary learning, Magnetic resonance imaging (MRI), Noise estimation, Microstructure, Tractography, Diffusion MRI
Abstract

Diffusion magnetic resonance imaging (dMRI) is an imaging technique to obtain information about the microstructural properties of tissues non-invasively, such as white matter in the human brain. The properties of the dMRI signal can be used to extract scalar maps of tissues and their orientation based on the displacement of water molecules, revealing their abnormalities in the process. However, scanning protocols for dMRI datasets are oftentimes limited by their lengthy acquisition time, lower signal-to-noise ratio (SNR) and lower spatial resolution when compared to the acquisition of traditional T1-weighted images. Due to the variability in scanning protocols and the hardware used during acquisition (e.g. in multicenter studies), variation in the signal properties are ubiquitous and preclude direct comparison of the datasets. To alleviate these issues, this thesis presents new algorithms to enhance the analysis of diffusion MRI datasets. In chapter 2, a new denoising algorithm exploiting the spatial and angular redundancy of dMRI datasets is introduced. Results show that the SNR and tractography of major pathways in the brain are improved on a 1.2 mm dataset of a single subject when compared to a 1.8 mm dataset of the same subject for matched acquisition time. Chapter 3 presents a new algorithm for extracting core representation of the fiber bundles based on the assignment of values to locally orthogonal planes. Caveats of the traditional Cartesian averaging technique are also shown in the presence of crossing or fanning fiber configurations. Chapter 4 follows in the idea of chapter 3 and presents a new realignment algorithm for along-tract analysis which can be applied in the 1D space of common coordinates. Experiments on 100 in vivo subjects show that the variability due to truncation and misalignment between subjects is reduced after realignment. Alterations of the studied metrics in specific regions are averaged out when there is a misalignment but can be identified after realignment in most cases. Chapter 5 presents an automated algorithm to identify the noise distribution of diffusion MRI datasets. Experiments using datasets acquired in three different centers show that the noise distribution depends on the reconstruction algorithm which is used and varies between vendors, but can be measured from the datasets themselves or from noise maps. Chapter 6 combines these previous ideas by presenting an algorithm to harmonize datasets acquired on different scanners with dictionary learning. Features present in all datasets are automatically extracted and subsequently used to reconstruct the datasets with an adaptive regularization value. This discards unwanted variability which is associated with a particular scanner while preserving individual subjects’ natural variability amongst the group. Experiments with an altered version of the datasets show that the natural variability is preserved while decreasing variability across scanners, even if alterations are not part of the initial training datasets. Taken together, these new ideas facilitate and enhance the analyses of dMRI datasets and could help to uncover new biomarkers for earlier identification of neurodegenerative diseases such as Parkinson’s or Alzheimer’s disease.

Published
2020

22. Presentation for ISMRM 2020 - Automated feature extraction across scanners for harmonization of diffusion MRI datasets

Author

St-Jean, Samuel, A. Viergever, Max, and Leemans, Alexander

Abstract

Video presentation for my ISMRM 2020 digital poster titled 'Automated feature extraction across scanners for harmonization of diffusion MRI datasets', session Diffusion Acquisition, Reconstruction & Signal AnalysisReference: Samuel St-Jean, Max A. Viergever and Alexander Leemans Automated feature extraction across scanners for harmonization of diffusion MRI datasets, ISMRM 2020, Abstract no. 4371

Published
2020
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28. Author Correction : The challenge of mapping the human connectome based on diffusion tractography (Nature Communications, (2017), 8, 1, (1349), 10.1038/s41467-017-01285-x)

Author

Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean Christophe, Côté, Marc Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang Cheng, Lin, Ying Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean Christophe, Côté, Marc Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang Cheng, Lin, Ying Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, and Barakovic, Muhamed

Published
2019

29. Cross-scanner and cross-protocol diffusion MRI data harmonisation : A benchmark database and evaluation of algorithms

Author

Tax, Chantal Mw, Grussu, Francesco, Kaden, Enrico, Ning, Lipeng, Rudrapatna, Umesh, Evans, John, St-Jean, Samuel, Leemans, Alexander, Koppers, Simon, Merhof, Dorit, Ghosh, Aurobrata, Tanno, Ryutaro, Alexander, Daniel C, Zappalà, Stefano, Charron, Cyril, Kusmia, Slawomir, Linden, David Ej, Jones, Derek K, Veraart, Jelle, Tax, Chantal Mw, Grussu, Francesco, Kaden, Enrico, Ning, Lipeng, Rudrapatna, Umesh, Evans, John, St-Jean, Samuel, Leemans, Alexander, Koppers, Simon, Merhof, Dorit, Ghosh, Aurobrata, Tanno, Ryutaro, Alexander, Daniel C, Zappalà, Stefano, Charron, Cyril, Kusmia, Slawomir, Linden, David Ej, Jones, Derek K, and Veraart, Jelle

Published
2019

31. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms

Author

Beeldverwerking ISI, Brain, Tax, Chantal Mw, Grussu, Francesco, Kaden, Enrico, Ning, Lipeng, Rudrapatna, Umesh, Evans, John, St-Jean, Samuel, Leemans, Alexander, Koppers, Simon, Merhof, Dorit, Ghosh, Aurobrata, Tanno, Ryutaro, Alexander, Daniel C, Zappalà, Stefano, Charron, Cyril, Kusmia, Slawomir, Linden, David Ej, Jones, Derek K, Veraart, Jelle, Beeldverwerking ISI, Brain, Tax, Chantal Mw, Grussu, Francesco, Kaden, Enrico, Ning, Lipeng, Rudrapatna, Umesh, Evans, John, St-Jean, Samuel, Leemans, Alexander, Koppers, Simon, Merhof, Dorit, Ghosh, Aurobrata, Tanno, Ryutaro, Alexander, Daniel C, Zappalà, Stefano, Charron, Cyril, Kusmia, Slawomir, Linden, David Ej, Jones, Derek K, and Veraart, Jelle

Published
2019

32. Author Correction: The challenge of mapping the human connectome based on diffusion tractography (Nature Communications, (2017), 8, 1, (1349), 10.1038/s41467-017-01285-x)

Author

Beeldverwerking ISI, Brain, Imago ISI, Onderzoek Radiotherapie, Highfield Research Group, Regenerative Medicine and Stem Cells, Cancer, Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean Christophe, Côté, Marc Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang Cheng, Lin, Ying Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Beeldverwerking ISI, Brain, Imago ISI, Onderzoek Radiotherapie, Highfield Research Group, Regenerative Medicine and Stem Cells, Cancer, Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean Christophe, Côté, Marc Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang Cheng, Lin, Ying Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, and Barakovic, Muhamed

Published
2019

33. Obtaining representative core streamlines for white matter tractometry of the human brain

Author

Beeldverwerking ISI, Brain, Bonet-Carne, Elisenda, Sepehrband, Farshid, Ning, Lipeng, Grussu, Francesco, Tax, Chantal M.W., Chamberland, Maxime, St-jean, Samuel, Tax, Chantal M W, Jones, Derek K., Beeldverwerking ISI, Brain, Bonet-Carne, Elisenda, Sepehrband, Farshid, Ning, Lipeng, Grussu, Francesco, Tax, Chantal M.W., Chamberland, Maxime, St-jean, Samuel, Tax, Chantal M W, and Jones, Derek K.

Published
2019

38. The challenge of mapping the human connectome based on diffusion tractography

Author

Maier-Hein, Klaus H, Neher, Peter F, Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E, Glass, John O, Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, Renjie, H, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H Ertan, Odry, Benjamin L, Mailhe, Boris, Nadar, Mariappan S, Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E, Galvis, Justin, Thompson, Paul M., Requejo, Francisco De Santiago, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell'Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B, Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, Descoteaux, Maxime, Maier-Hein, Klaus H, Neher, Peter F, Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E, Glass, John O, Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, Renjie, H, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H Ertan, Odry, Benjamin L, Mailhe, Boris, Nadar, Mariappan S, Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E, Galvis, Justin, Thompson, Paul M., Requejo, Francisco De Santiago, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell'Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B, Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, and Descoteaux, Maxime

Published
2017

39. The challenge of mapping the human connectome based on diffusion tractography

Author

Beeldverwerking ISI, Brain, UMC Utrecht, Highfield Research Group, Maier-Hein, Klaus H, Neher, Peter F, Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E, Glass, John O, Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, Renjie, H, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H Ertan, Odry, Benjamin L, Mailhe, Boris, Nadar, Mariappan S, Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E, Galvis, Justin, Thompson, Paul M., Requejo, Francisco De Santiago, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell'Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B, Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, Descoteaux, Maxime, Beeldverwerking ISI, Brain, UMC Utrecht, Highfield Research Group, Maier-Hein, Klaus H, Neher, Peter F, Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E, Glass, John O, Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, Renjie, H, Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H Ertan, Odry, Benjamin L, Mailhe, Boris, Nadar, Mariappan S, Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E, Galvis, Justin, Thompson, Paul M., Requejo, Francisco De Santiago, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell'Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B, Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, and Descoteaux, Maxime

Published
2017

43. Tractography-based connectomes are dominated by false-positive connections

Author

Maier-Hein, Klaus H., primary, Neher, Peter, additional, Houde, Jean-Christophe, additional, Côté, Marc-Alexandre, additional, Garyfallidis, Eleftherios, additional, Zhong, Jidan, additional, Chamberland, Maxime, additional, Yeh, Fang-Cheng, additional, Lin, Ying-Chia, additional, Ji, Qing, additional, Reddick, Wilburn E., additional, Glass, John O., additional, Chen, David Qixiang, additional, Feng, Yuanjing, additional, Gao, Chengfeng, additional, Wu, Ye, additional, Ma, Jieyan, additional, Renjie, H, additional, Li, Qiang, additional, Westin, Carl-Fredrik, additional, Deslauriers-Gauthier, Samuel, additional, González, J. Omar Ocegueda, additional, Paquette, Michael, additional, St-Jean, Samuel, additional, Girard, Gabriel, additional, Rheault, François, additional, Sidhu, Jasmeen, additional, Tax, Chantal M.W., additional, Guo, Fenghua, additional, Mesri, Hamed Y., additional, Dávid, Szabolcs, additional, Froeling, Martijn, additional, Heemskerk, Anneriet M., additional, Leemans, Alexander, additional, Boré, Arnaud, additional, Pinsard, Basile, additional, Bedetti, Christophe, additional, Desrosiers, Matthieu, additional, Brambati, Simona, additional, Doyon, Julien, additional, Sarica, Alessia, additional, Vasta, Roberta, additional, Cerasa, Antonio, additional, Quattrone, Aldo, additional, Yeatman, Jason, additional, Khan, Ali R., additional, Hodges, Wes, additional, Alexander, Simon, additional, Romascano, David, additional, Barakovic, Muhamed, additional, Auría, Anna, additional, Esteban, Oscar, additional, Lemkaddem, Alia, additional, Thiran, Jean-Philippe, additional, Cetingul, H. Ertan, additional, Odry, Benjamin L., additional, Mailhe, Boris, additional, Nadar, Mariappan S., additional, Pizzagalli, Fabrizio, additional, Prasad, Gautam, additional, Villalon-Reina, Julio E., additional, Galvis, Justin, additional, Thompson, Paul M., additional, Requejo, Francisco De Santiago, additional, Laguna, Pedro Luque, additional, Lacerda, Luis Miguel, additional, Barrett, Rachel, additional, Dell’Acqua, Flavio, additional, Catani, Marco, additional, Petit, Laurent, additional, Caruyer, Emmanuel, additional, Daducci, Alessandro, additional, Dyrby, Tim B., additional, Holland-Letz, Tim, additional, Hilgetag, Claus C., additional, Stieltjes, Bram, additional, and Descoteaux, Maxime, additional

Published
2016
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45. Acquisitions d'IRM de diffusion à haute résolution spatiale : nouvelles perspectives grâce au débruitage spatialement adaptatif et angulaire

Author

St-Jean, Samuel, Descoteaux, Maxime, St-Jean, Samuel, and Descoteaux, Maxime

Abstract

Le début des années 2000 a vu la cartographie du génome humain se réaliser après 13 ans de recherche. Le défi du prochain siècle réside dans la construction du connectome humain, qui consiste à cartographier les connexions du cerveau en utilisant l’imagerie par résonance magnétique (IRM) de diffusion. Cette technique permet en effet d’étudier la matière blanche du cerveau de façon complètement non invasive. Bien que le défi soit monumental, la résolution d’une image d’IRM se situe à l’échelle macroscopique et est environ 1000 fois inférieure à la taille des axones qu’il faut cartographier. Pour aider à pallier à ce problème, ce mémoire propose une nouvelle technique de débruitage spécialement conçue pour l’imagerie de diffusion. L’algorithme Non Local Spatial and Angular Matching (NLSAM) se base sur les principes du block matching et du dictionary learning pour exploiter la redondance des données d’IRM de diffusion. Un seuillage sur les voisins angulaire est aussi réalisé à l’aide du sparse coding, où l’erreur de reconstruction en norme l2 est bornée par la variance locale du bruit. L’algorithme est aussi conçu pour gérer le biais du bruit Ricien et Chi non centré puisque les images d’IRM contiennent du bruit non Gaussien. Ceci permet ainsi d’acquérir des données d’IRM de diffusion à une plus grande résolution spatiale que présentement disponible en milieu clinique. Ce travail ouvre donc la voie à un meilleur type d’acquisition, ce qui pourrait contribuer à révéler de nouveaux détails anatomiques non discernables à la résolution spatiale présentement utilisée par la communauté d’IRM de diffusion. Ceci pourrait aussi éventuellement contribuer à identifier de nouveaux biomarqueurs permettant de comprendre les maladies dégénératives telles que la sclérose en plaques, la maladie d’Alzheimer et la maladie de Parkinson.

Published
2015

46. Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?

Author

Beeldverwerking ISI, Brain, Ning, Lipeng, Laun, Frederik, Gur, Yaniv, DiBella, Edward V. R., Deslauriers-Gauthier, Samuel, Megherbi, Thinhinane, Ghosh, Aurobrata, Zucchelli, Mauro, Menegaz, Gloria, Fick, Rutger, St-Jean, Samuel, Paquette, Michael, Aranda, Ramon, Descoteaux, Maxime, Deriche, Rachid, O'Donnell, Lauren, Rathi, Yogesh, Beeldverwerking ISI, Brain, Ning, Lipeng, Laun, Frederik, Gur, Yaniv, DiBella, Edward V. R., Deslauriers-Gauthier, Samuel, Megherbi, Thinhinane, Ghosh, Aurobrata, Zucchelli, Mauro, Menegaz, Gloria, Fick, Rutger, St-Jean, Samuel, Paquette, Michael, Aranda, Ramon, Descoteaux, Maxime, Deriche, Rachid, O'Donnell, Lauren, and Rathi, Yogesh

Published
2015

47. Obtaining representative core streamlines for white matter tractometry of the human brain

Author

Bonet-Carne, E, Grussu, F, Ning, L, Sepehrband, F, Tax, C, Chamberland, Maxime, St-Jean, Samuel, Tax, Chantal M. W., Jones, Derek K., Bonet-Carne, E, Grussu, F, Ning, L, Sepehrband, F, Tax, C, Chamberland, Maxime, St-Jean, Samuel, Tax, Chantal M. W., and Jones, Derek K.

Abstract

Diffusion MRI infers information about the micro-structural architecture of the brain by probing the diffusion of water molecules. The process of virtually reconstructing brain pathways based on these measurements is called tractography. Various metrics can be mapped onto pathways to study their micro-structural properties. Tractometry is an along-tract profiling technique that often requires the extraction of a representative streamline for a given bundle. This is traditionally computed by local averaging of the spatial coordinates of the vertices, and constructing a single streamline through those averages. However, the resulting streamline can end up being highly non-representative of the shape of the individual streamlines forming the bundle. In particular, this occurs when there is variation in the topology of streamlines within a bundle (e.g., differences in length, shape or branching). We propose an envelope-based method to compute a representative streamline that is robust to these individual differences. We demonstrate that this method produces a more representative core streamline, which in turn should lead to more reliable and interpretable tractometry analyses.

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