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Yao T, Rheault F, Cai LY, Nath V, Asad Z, Newlin N, Cui C, Deng R, Ramadass K, Schilling K, Landman BA, Huo Y. Deep Constrained Spherical Deconvolution for Robust Harmonization. Proc SPIE Int Soc Opt Eng 2023; 12464:124640W. [PMID: 37228707 PMCID: PMC10208219 DOI: 10.1117/12.2654398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance, and sequence design), which consequently yields inferior performance on multi-site and/or longitudinal diffusion studies. In this study, we propose a novel, deep learning-based method to harmonize DW-MRI signals for a more reproducible and robust estimation of microstructure. Our method introduces a data-driven scanner-invariant regularization scheme to model a more robust fiber orientation distribution function (FODF) estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The 8th order spherical harmonics coefficients are employed as data representation. The results show that the proposed harmonization approach maintains higher angular correlation coefficients (ACC) with the ground truth signals (0.954 versus 0.942), while achieves higher consistency of FODF signals for intra-scanner data (0.891 versus 0.826), as compared with the baseline supervised deep learning scheme. Furthermore, the proposed data-driven framework is flexible and potentially applicable to a wider range of data harmonization problems in neuroimaging.
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Affiliation(s)
- Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37212, USA
| | | | - Zuhayr Asad
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Nancy Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Can Cui
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Ruining Deng
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Karthik Ramadass
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37212, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37235, USA
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Hoye J, Toyonaga T, Zakiniaeiz Y, Stanley G, Hampson M, Morris ED. Harmonization of [ 11C]raclopride brain PET images from the HR+ and HRRT: method development and validation in human subjects. EJNMMI Phys 2022; 9:27. [PMID: 35416555 PMCID: PMC9008103 DOI: 10.1186/s40658-022-00457-z] [Citation(s) in RCA: 2] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/04/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND There has been an ongoing need to compare and combine the results of new PET imaging studies conducted with [11C]raclopride with older data. This typically means harmonizing data across different scanners. Previous harmonization studies have utilized either phantoms or human subjects, but the use of both phantoms and humans in one harmonization study is not common. The purpose herein was (1) to use phantom images to develop an inter-scanner harmonization technique and (2) to test the harmonization technique in human subjects. METHODS To develop the harmonization technique (Experiment 1), the Iida brain phantom was filled with F-18 solution and scanned on the two scanners in question (HRRT, HR+, Siemens/CTI). Phantom images were used to determine the optimal isotropic Gaussian filter to harmonize HRRT and HR+ images. To evaluate the harmonization on human images (Experiment 2), inter-scanner variability was calculated using [11C]raclopride scans of 3 human subjects on both the HRRT and HR+ using percent difference (PD) in striatal non-displaceable binding potential (BPND) between HR+ and HRRT (with and without Gaussian smoothing). Finally, (Experiment 3), PDT/RT was calculated for test-retest (T/RT) variability of striatal BPND for 8 human subjects scanned twice on the HR+. RESULTS Experiment 1 identified the optimal filter as a Gaussian with a 4.5 mm FWHM. Experiment 2 resulted in 13.9% PD for unfiltered HRRT and 3.71% for HRRT filtered with 4.5 mm. Experiment 3 yielded 5.24% PDT/RT for HR+. CONCLUSIONS The PD results show that the variability of harmonized HRRT is less than the T/RT variability of the HR+. The harmonization technique makes it possible for BPND estimates from the HRRT to be compared to (and/or combined with) those from the HR+ without adding to overall variability. Our approach is applicable to all pairs of scanners still in service.
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Affiliation(s)
- Jocelyn Hoye
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA. .,Yale Positron Emission Tomography (PET) Center, Yale School of Medicine, New Haven, CT, USA.
| | - Takuya Toyonaga
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Yale Positron Emission Tomography (PET) Center, Yale School of Medicine, New Haven, CT USA
| | - Yasmin Zakiniaeiz
- grid.47100.320000000419368710Department of Psychiatry, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Yale Positron Emission Tomography (PET) Center, Yale School of Medicine, New Haven, CT USA
| | - Gelsina Stanley
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Yale Positron Emission Tomography (PET) Center, Yale School of Medicine, New Haven, CT USA
| | - Michelle Hampson
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Department of Biomedical Engineering, Yale University, New Haven, CT USA
| | - Evan D. Morris
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Yale Positron Emission Tomography (PET) Center, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Department of Biomedical Engineering, Yale University, New Haven, CT 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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Aja-Fernández S, Pieciak T, Tristán-Vega A, Vegas-Sánchez-Ferrero G, Molina V, Luis-García R. Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers. Magn Reson Imaging 2018; 54:194-213. [PMID: 30196167 DOI: 10.1016/j.mri.2018.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/18/2018] [Accepted: 03/07/2018] [Indexed: 11/23/2022]
Abstract
An imaging biomarker is a biologic feature in an image that is relevant to a patient's diagnosis or prognosis. In order to qualify as a biomarker, a measure must be robust and reproducible. However, the usual scalar measures derived from diffusion tensor imaging are known to be highly dependent on the variation of the acquisition parameters, which prevents their possible use as biomarkers. In this work, we propose a new set of quantitative measures based on diffusion magnetic resonance imaging from single-shell acquisitions that are designed to be robust to the variations of several acquisition parameters (number of gradient directions, b-value and SNR) while keeping a high discrimination power on differences in the diffusion characteristics of the tissue. These new scalar measures are analytically obtained from a generic diffusion function that does not require the calculation of a diffusion tensor. This way, on one hand, we avoid the use of a specific diffusion model and, on the other hand, we make easier the statistical characterization of the measures. Accordingly, the analysis of the measures bias is carried out and it is used to minimize their dependency with respect to the acquisition noise for different SNRs. The robustness and discrimination power of the measures are tested for different number of gradients, b-values and SNRs using a realistic phantom and three real datasets: (1) 13 control subjects and different acquisition parameters; (2) a public data set from a single subject acquired using multiple shells and (3) 32 schizophrenia patients and 32 age and sex-matched healthy controls with a varying number of gradient directions. The proposed quantitative measures exhibit low variability to the changes of the acquisition parameters, while at the same time they preserve a discrimination power that is able to detect significant changes in the anisotropy of the diffusion.
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Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, Karmacharya S, Grant G, Marx CE, Morey RA, Flashman LA, George MS, McAllister TW, Andaluz N, Shutter L, Coimbra R, Zafonte RD, Coleman MJ, Kubicki M, Westin CF, Stein MB, Shenton ME, Rathi Y. Multi-site harmonization of diffusion MRI data in a registration framework. Brain Imaging Behav 2018; 12:284-95. [PMID: 28176263 DOI: 10.1007/s11682-016-9670-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Diffusion MRI (dMRI) data acquired on different scanners varies significantly in its content throughout the brain even if the acquisition parameters are nearly identical. Thus, proper harmonization of such data sets is necessary to increase the sample size and thereby the statistical power of neuroimaging studies. In this paper, we present a novel approach to harmonize dMRI data (the raw signal, instead of dMRI derived measures such as fractional anisotropy) using rotation invariant spherical harmonic (RISH) features embedded within a multi-modal image registration framework. All dMRI data sets from all sites are registered to a common template and voxel-wise differences in RISH features between sites at a group level are used to harmonize the signal in a subject-specific manner. We validate our method on diffusion data acquired from seven different sites (two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across these sites before and after data harmonization. Validation was also done on a group oftest subjects, which were not used to "learn" the harmonization parameters. We also show results using TBSS before and after harmonization for independent validation of the proposed methodology. Using synthetic data, we show that any abnormality in diffusion measures due to disease is preserved during the harmonization process. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences in the signal can be removed using the proposed method in a model independent manner.
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Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnis M, Phillips ML, Trivedi MH, Weissman MM, Shinohara RT. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 2017; 167:104-120. [PMID: 29155184 DOI: 10.1016/j.neuroimage.2017.11.024] [Citation(s) in RCA: 578] [Impact Index Per Article: 82.6] [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: 06/02/2017] [Revised: 11/09/2017] [Accepted: 11/12/2017] [Indexed: 12/11/2022] Open
Abstract
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as "scanner effects", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
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Affiliation(s)
- Jean-Philippe Fortin
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Nicholas Cullen
- Department of Electrical and Systems Engineering, University of Pennsylvania, United States; Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Warren D Taylor
- Department of Psychiatry, Vanderbilt University, United States
| | - Irem Aselcioglu
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Philip A Cook
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Phil Adams
- Department of Psychiatry, Columbia University College of Physicians & Surgeons, United States
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, United States
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, United States
| | - Patrick J McGrath
- Department of Psychiatry, Columbia University College of Physicians & Surgeons, United States
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan School of Medicine, United States
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, United States
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, United States
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University College of Physicians & Surgeons, United States; Division of Epidemiology, New York State Psychiatric Institute, United States; Mailman School of Public Health, Columbia University, United States
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States.
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Fortin JP, Parker D, Tunç B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, Shinohara RT. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 2017; 161:149-170. [PMID: 28826946 DOI: 10.1016/j.neuroimage.2017.08.047] [Citation(s) in RCA: 563] [Impact Index Per Article: 80.4] [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: 03/17/2017] [Revised: 07/03/2017] [Accepted: 08/15/2017] [Indexed: 12/18/2022] Open
Abstract
Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.
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Affiliation(s)
- Jean-Philippe Fortin
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Drew Parker
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Birkan Tunç
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Takanori Watanabe
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Mark A Elliott
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | | | - Ruben C Gur
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Raquel E Gur
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Robert T Schultz
- Center for Autism Research, The Children's Hospital of Philadelphia, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Russell T Shinohara
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, USA.
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8
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Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, Grant G, Marx CE, Morey RA, Flashman LA, George MS, McAllister TW, Andaluz N, Shutter L, Coimbra R, Zafonte RD, Coleman MJ, Kubicki M, Westin CF, Stein MB, Shenton ME, Rathi Y. Inter-site and inter-scanner diffusion MRI data harmonization. Neuroimage 2016; 135:311-23. [PMID: 27138209 DOI: 10.1016/j.neuroimage.2016.04.041] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [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: 10/30/2015] [Revised: 03/15/2016] [Accepted: 04/18/2016] [Indexed: 11/17/2022] Open
Abstract
We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.
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Affiliation(s)
- H Mirzaalian
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA.
| | - L Ning
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - P Savadjiev
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - O Pasternak
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - S Bouix
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | | | - G Grant
- Stanford University Medical Center, Palo Alto, CA, USA (Previously Duke University)
| | - C E Marx
- Duke University Medical Center and VA Mid-Atlantic MIRECC, NC, USA
| | - R A Morey
- Duke University Medical Center and VA Mid-Atlantic MIRECC, NC, USA
| | - L A Flashman
- Dartmouth University, Hanover and Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - M S George
- Medical University of South Carolina, Charleston, SC, USA, Ralph H. Johnson VA Medical Center, Charleston
| | - T W McAllister
- Geisel School of Medicine at Dartmouth (original) and Indiana University School of Medicine (current)
| | - N Andaluz
- Department of Neurosurgery, University of Cincinnati (UC) College of Medicine; Neurotrauma Center at UC Neuroscience Institute; and Mayfield Clinic, Cincinnati, OH
| | - L Shutter
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA (Previously Duke University)
| | - R Coimbra
- Department of Surgery, University of California, San Diego
| | - R D Zafonte
- Spaulding Rehabilitation Hospital and Harvard Medical School, Boston, USA
| | - M J Coleman
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - M Kubicki
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - C F Westin
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - M B Stein
- University of California, San Diego, San Diego, CA, USA
| | - M E Shenton
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Y Rathi
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
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