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De Luca A, Karayumak SC, Leemans A, Rathi Y, Swinnen S, Gooijers J, Clauwaert A, Bahr R, Sandmo SB, Sochen N, Kaufmann D, Muehlmann M, Biessels GJ, Koerte I, Pasternak O. Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH). Neuroimage 2022; 259:119439. [PMID: 35788044 DOI: 10.1016/j.neuroimage.2022.119439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022] Open
Abstract
Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.
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Affiliation(s)
- Alberto De Luca
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
| | | | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Jolien Gooijers
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Amanda Clauwaert
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Roald Bahr
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Stian Bahr Sandmo
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Nir Sochen
- Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - David Kaufmann
- Radiology Department, Charite University Hospital, Berlin, Germany
| | - Marc Muehlmann
- Department of Radiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Inga Koerte
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; cBRAIN, Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Hegde RR, Kelly S, Lutz O, Guimond S, Karayumak SC, Mike L, Mesholam-Gately RI, Pasternak O, Kubicki M, Eack SM, Keshavan MS. Association of white matter microstructure and extracellular free-water with cognitive performance in the early course of schizophrenia. Psychiatry Res Neuroimaging 2020; 305:111159. [PMID: 32919288 DOI: 10.1016/j.pscychresns.2020.111159] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/26/2020] [Accepted: 08/12/2020] [Indexed: 10/23/2022]
Abstract
Schizophrenia (SZ) is proposed as a disorder of dysconnectivity underlying cognitive impairments and clinical manifestations. Although previous studies have shown extracellular changes in white matter of first-episode SZ, little is known about the transition period towards chronicity and its association with cognition. Free-water (FW) imaging was applied to 79 early course SZ participants and 29 controls to detect white matter axonal and extracellular differences during this phase of illness. Diffusion-weighted images were collected from two sites, harmonized, and processed using a pipeline separately modeling water diffusion in tissue (FAt) and extracellular space (FW). Tract-Based Spatial Statistics was performed using the ENIGMA-DTI protocols. SZ showed FAt reductions in the posterior thalamic radiation (PTR) and FW elevations in the cingulum compared to controls, suggesting FAt and FW changes in the early course of SZ. In SZ, greater FAt of the fornix & stria terminalis (FXST) was positively associated with Theory of Mind performance; average whole-brain FAt, FAt of the FXST and the PTR were positively associated with greater working memory performance; average whole-brain FAt was positively associated with visual learning. Further studies are necessary to better understand the neurobiological mechanisms of SZ for developing intervention strategies to preserve brain structure and function.
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Affiliation(s)
- Rachal R Hegde
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sinead Kelly
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Olivia Lutz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Synthia Guimond
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; The Royal's Institute of Mental Health Research, Department of Psychiatry, University of Ottawa, ON, Canada
| | - Suheyla Cetin Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Luke Mike
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Raquelle I Mesholam-Gately
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Shaun M Eack
- Department of Psychiatry and School of Social Work, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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Zhang F, Cetin Karayumak S, Hoffmann N, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation. Med Image Anal 2020; 65:101761. [PMID: 32622304 PMCID: PMC7483951 DOI: 10.1016/j.media.2020.101761] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 02/07/2023]
Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Nico Hoffmann
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Ning L, Bonet-Carne E, Grussu F, Sepehrband F, Kaden E, Veraart J, Blumberg SB, Khoo CS, Palombo M, Kokkinos I, Alexander DC, Coll-Font J, Scherrer B, Warfield SK, Karayumak SC, Rathi Y, Koppers S, Weninger L, Ebert J, Merhof D, Moyer D, Pietsch M, Christiaens D, Gomes Teixeira RA, Tournier JD, Schilling KG, Huo Y, Nath V, Hansen C, Blaber J, Landman BA, Zhylka A, Pluim JPW, Parker G, Rudrapatna U, Evans J, Charron C, Jones DK, Tax CMW. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results. Neuroimage 2020; 221:117128. [PMID: 32673745 DOI: 10.1016/j.neuroimage.2020.117128] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 06/26/2020] [Accepted: 06/29/2020] [Indexed: 01/26/2023] Open
Abstract
Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.
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Affiliation(s)
- Lipeng Ning
- Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States.
| | | | | | - Farshid Sepehrband
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States
| | - Enrico Kaden
- University College London, London, United Kingdom
| | | | | | - Can Son Khoo
- University College London, London, United Kingdom
| | | | | | | | - Jaume Coll-Font
- Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Benoit Scherrer
- Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Simon K Warfield
- Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Suheyla Cetin Karayumak
- Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Yogesh Rathi
- Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | | | | | | | | | - Daniel Moyer
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Rui Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Kurt G Schilling
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States
| | - Yuankai Huo
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Vishwesh Nath
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Colin Hansen
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Justin Blaber
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States; Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Andrey Zhylka
- Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Greg Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Cyril Charron
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom; School of Psychology, Australian Catholic University, Melbourne, Australia
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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Zhang F, Hoffmann N, Karayumak SC, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions. Med Image Comput Comput Assist Interv 2019; 11766:599-608. [PMID: 32558816 DOI: 10.1007/978-3-030-32248-9_67] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classification accuracy of 90.99%. The method is evaluated on a test dataset of 374 dMRI scans from three independently acquired populations across health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art white matter tract segmentation methods. Experimental results show that our method obtains a highly consistent segmentation result, where over 99% of the fiber tracts are successfully detected across all subjects under study, most importantly, including patients with space occupying brain tumors. The proposed method leverages deep learning techniques and provides a much faster and more efficient tool for large data analysis than methods using traditional machine learning techniques.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nico Hoffmann
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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Cetin Karayumak S, Bouix S, Ning L, James A, Crow T, Shenton M, Kubicki M, Rathi Y. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage 2018; 184:180-200. [PMID: 30205206 DOI: 10.1016/j.neuroimage.2018.08.073] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/17/2018] [Accepted: 08/29/2018] [Indexed: 01/17/2023] Open
Abstract
A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we propose a dMRI harmonization method that is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions. We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8-22 years) and Brigham and Women's Hospital (BWH) with 70 healthy subjects (ages 14-54 years). In particular, we show that gender and age-related maturation differences in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Since we use matched control subjects from different scanners to estimate scanner-specific effects, our goal in this work is also to determine the minimum number of well-matched subjects needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.
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Affiliation(s)
- Suheyla Cetin Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA.
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Lipeng Ning
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Anthony James
- Highfield Family and Adolescent Unit, Warneford Hospital, Oxford, UK
| | - Tim Crow
- Sane Powic, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Martha Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA; VA Boston Healthcare System, Brockton Division, Brockton, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
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Cetin Karayumak S, Özarslan E, Unal G. Asymmetric Orientation Distribution Functions (AODFs) revealing intravoxel geometry in diffusion MRI. Magn Reson Imaging 2018; 49:145-158. [PMID: 29550369 DOI: 10.1016/j.mri.2018.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 03/08/2018] [Indexed: 10/17/2022]
Abstract
Characterization of anisotropy via diffusion MRI reveals fiber crossings in a substantial portion of voxels within the white-matter (WM) regions of the human brain. A considerable number of such voxels could exhibit asymmetric features such as bends and junctions. However, widely employed reconstruction methods yield symmetric Orientation Distribution Functions (ODFs) even when the underlying geometry is asymmetric. In this paper, we employ inter-voxel directional filtering approaches through a cone model to reveal more information regarding the cytoarchitectural organization within the voxel. The cone model facilitates a sharpening of the ODFs in some directions while suppressing peaks in other directions, thus yielding an Asymmetric ODF (AODF) field. We also show that a scalar measure of AODF asymmetry can be employed to obtain new contrast within the human brain. The feasibility of the technique is demonstrated on in vivo data obtained from the MGH-USC Human Connectome Project (HCP) and Parkinson's Progression Markers Initiative (PPMI) Project database. Characterizing asymmetry in neural tissue cytoarchitecture could be important for localizing and quantitatively assessing specific neuronal pathways.
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Affiliation(s)
- Suheyla Cetin Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA; Faculty of Engineering and Natural Sciences, Sabanci University, Turkey
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Gozde Unal
- Department of Computer Engineering, Istanbul Technical University, Turkey.
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