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Ware AL, Shukla A, Guo S, Onicas A, Geeraert BL, Goodyear BG, Yeates KO, Lebel C. Participant factors that contribute to magnetic resonance imaging motion artifacts in children with mild traumatic brain injury or orthopedic injury. Brain Imaging Behav 2021; 16:991-1002. [PMID: 34694520 DOI: 10.1007/s11682-021-00582-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/01/2021] [Indexed: 10/20/2022]
Abstract
Motion can compromise image quality and confound results, especially in pediatric research. This study evaluated qualitative and quantitative approaches to motion artifacts detection and correction, and whether motion artifacts relate to injury history, age, or sex in children with mild traumatic brain injury or orthopedic injury relative to typically developing children. The concordance between qualitative and quantitative motion ratings was also examined. Children aged 8-16 years with mild traumatic brain injury (n = 141) or orthopedic injury (n = 73) were recruited from the emergency department and completed an MRI scan roughly 2 weeks post-injury. Typically developing children (n = 41) completed a single MRI scan. T1- and diffusion-weighted images were visually inspected and rated for motion artifacts by trained examiners. Quantitative estimates of motion artifacts were derived from FreeSurfer and FSL. Age (younger > older) and sex (boys > girls) were significantly associated with motion artifacts on both T1- and diffusion-weighted images. Children with mild traumatic brain or orthopedic injury had significantly more motion-corrupted diffusion-weighted volumes than typically developing children, but mild traumatic brain injury and orthopedic injury groups did not differ from each other. The exclusion of motion-corrupted volumes did not significantly change diffusion tensor imaging metrics. Results indicate that automated quantitative estimates of motion artifacts, which are less labour-intensive than manual methods, are appropriate. Results have implications for the reliability of structural MRI research and highlight the importance of considering motion artifacts in studies of pediatric mild traumatic brain injury.
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Affiliation(s)
- Ashley L Ware
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada. .,Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. .,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada. .,Department of Neurology, University of Utah, Salt Lake City, UT, USA.
| | - Ayushi Shukla
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.,Department of Radiology, University of Calgary, Calgary, Canada
| | - Sunny Guo
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
| | - Adrian Onicas
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.,IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Bryce L Geeraert
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.,Department of Radiology, University of Calgary, Calgary, Canada
| | - Bradley G Goodyear
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.,Department of Radiology, University of Calgary, Calgary, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
| | - Catherine Lebel
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.,Department of Radiology, University of Calgary, Calgary, Canada
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2
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Marami B, Scherrer B, Khan S, Afacan O, Prabhu SP, Sahin M, Warfield SK, Gholipour A. Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition. Magn Reson Med 2019; 81:3314-3329. [PMID: 30443929 PMCID: PMC6414287 DOI: 10.1002/mrm.27613] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 10/25/2018] [Accepted: 10/25/2018] [Indexed: 12/16/2022]
Abstract
PURPOSE To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI. METHODS Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. RESULTS We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion. CONCLUSION The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.
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Affiliation(s)
- Bahram Marami
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Icahn School of Medicine at Mount Sinai New York, New York
| | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Shadab Khan
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Sanjay P Prabhu
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Mustafa Sahin
- Harvard Medical School, Boston, Massachusetts
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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3
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Wegmann B, Eklund A, Villani M. Bayesian Rician Regression for Neuroimaging. Front Neurosci 2017; 11:586. [PMID: 29104529 PMCID: PMC5655010 DOI: 10.3389/fnins.2017.00586] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 10/04/2017] [Indexed: 11/13/2022] Open
Abstract
It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central χ (NC-χ) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed to capture full model uncertainty by simulating from the joint posterior distribution of all model parameters and the binary variable selection indicators. Simulated regression data is used to demonstrate that the Rician model is able to detect the signal much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the Rician model.
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Affiliation(s)
- Bertil Wegmann
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden.,Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Mattias Villani
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
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Marami B, Mohseni Salehi SS, Afacan O, Scherrer B, Rollins CK, Yang E, Estroff JA, Warfield SK, Gholipour A. Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. Neuroimage 2017; 156:475-488. [PMID: 28433624 DOI: 10.1016/j.neuroimage.2017.04.033] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 04/14/2017] [Indexed: 01/29/2023] Open
Abstract
Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in-vivo diffusion-tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration-based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra-slice motion and reconstruct DTI from motion-corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in-utero at 22-38 weeks gestation. Significantly higher fractional anisotropy values in fiber-rich regions, and the analysis of whole-brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice-level motion correction and robust reconstruction is necessary for reliable in-vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small-worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.
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Affiliation(s)
- Bahram Marami
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Seyed Sadegh Mohseni Salehi
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Department of Electrical Engineering, Northeastern University, Boston, MA, USA
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Edward Yang
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Judy A Estroff
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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Marami B, Scherrer B, Afacan O, Erem B, Warfield SK, Gholipour A. Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2258-2269. [PMID: 27834639 PMCID: PMC5108524 DOI: 10.1109/tmi.2016.2555244] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.
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Affiliation(s)
- Bahram Marami
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Burak Erem
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Simon K. Warfield
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
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Marami B, Scherrer B, Afacan O, Warfield SK, Gholipour A. Motion-Robust Reconstruction based on Simultaneous Multi-Slice Registration for Diffusion-Weighted MRI of Moving Subjects. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9902:544-552. [PMID: 28127590 DOI: 10.1007/978-3-319-46726-9_63] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Simultaneous multi-slice (SMS) echo-planar imaging has had a huge impact on the acceleration and routine use of diffusion-weighted MRI (DWI) in neuroimaging studies in particular the human connectome project; but also holds the potential to facilitate DWI of moving subjects, as proposed by the new technique developed in this paper. We present a novel registration-based motion tracking technique that takes advantage of the multi-plane coverage of the anatomy by simultaneously acquired slices to enable robust reconstruction of neural microstructure from SMS DWI of moving subjects. Our technique constitutes three main components: 1) motion tracking and estimation using SMS registration, 2) detection and rejection of intra-slice motion, and 3) robust reconstruction. Quantitative results from 14 volunteer subject experiments and the analysis of motion-corrupted SMS DWI of 6 children indicate robust reconstruction in the presence of continuous motion and the potential to extend the use of SMS DWI in very challenging populations.
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Affiliation(s)
- Bahram Marami
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Benoit Scherrer
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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7
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Hering J, Wolf I, Maier-Hein KH. Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2280-2291. [PMID: 27116735 DOI: 10.1109/tmi.2016.2557580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm-2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.
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8
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Taylor PA, Alhamud A, van der Kouwe A, Saleh MG, Laughton B, Meintjes E. Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction. Hum Brain Mapp 2016; 37:4405-4424. [PMID: 27436169 DOI: 10.1002/hbm.23318] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 06/16/2016] [Accepted: 07/05/2016] [Indexed: 11/07/2022] Open
Abstract
Diffusion tensor imaging (DTI) is susceptible to several artifacts due to eddy currents, echo planar imaging (EPI) distortion and subject motion. While several techniques correct for individual distortion effects, no optimal combination of DTI acquisition and processing has been determined. Here, the effects of several motion correction techniques are investigated while also correcting for EPI distortion: prospective correction, using navigation; retrospective correction, using two different popular packages (FSL and TORTOISE); and the combination of both methods. Data from a pediatric group that exhibited incidental motion in varying degrees are analyzed. Comparisons are carried while implementing eddy current and EPI distortion correction. DTI parameter distributions, white matter (WM) maps and probabilistic tractography are examined. The importance of prospective correction during data acquisition is demonstrated. In contrast to some previous studies, results also show that the inclusion of retrospective processing also improved ellipsoid fits and both the sensitivity and specificity of group tractographic results, even for navigated data. Matches with anatomical WM maps are highest throughout the brain for data that have been both navigated and processed using TORTOISE. The inclusion of both prospective and retrospective motion correction with EPI distortion correction is important for DTI analysis, particularly when studying subject populations that are prone to motion. Hum Brain Mapp 37:4405-4424, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Paul A Taylor
- Department of Human Biology, Faculty of Health Sciences, MRC/UCT Medical Imaging Research Unit, University of Cape Town, South Africa.,African Institute for Mathematical Sciences, Muizenberg, Western Cape, South Africa.,Scientific and Statistical Computing Core, National Institutes of Health, Bethesda, Maryland
| | - A Alhamud
- Department of Human Biology, Faculty of Health Sciences, MRC/UCT Medical Imaging Research Unit, University of Cape Town, South Africa
| | - Andre van der Kouwe
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Muhammad G Saleh
- Department of Human Biology, Faculty of Health Sciences, MRC/UCT Medical Imaging Research Unit, University of Cape Town, South Africa
| | - Barbara Laughton
- Department of Paediatrics and Child Health, Stellenbosch University, Children's Infection Diseases Clinical Research Unit, South Africa
| | - Ernesta Meintjes
- Department of Human Biology, Faculty of Health Sciences, MRC/UCT Medical Imaging Research Unit, University of Cape Town, South Africa
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Elhabian S, Vachet C, Piven J, Styner M, Gerig G. Compressive Sensing Based Q-Space Resampling for Handling Fast Bulk Motion in Hardi Acquisitions. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2016; 2016:907-910. [PMID: 29492184 DOI: 10.1109/isbi.2016.7493412] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas slow bulk motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, fast bulk motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data.
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Affiliation(s)
- Shireen Elhabian
- Scientific Computing and Imaging Institute, Salt Lake City, UT, USA
| | - Clement Vachet
- Scientific Computing and Imaging Institute, Salt Lake City, UT, USA
| | - Joseph Piven
- Dept. of Psychiatry, University of North Carolina, NC, USA
| | | | - Martin Styner
- Dept. of Psychiatry, University of North Carolina, NC, USA.,Dept. of Computer Science, University of North Carolina, NC, USA
| | - Guido Gerig
- Tandon School of Engineering, Department of Computer Science & Engineering, NYU, USA
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Interpreting Intervention Induced Neuroplasticity with fMRI: The Case for Multimodal Imaging Strategies. Neural Plast 2015; 2016:2643491. [PMID: 26839711 PMCID: PMC4709757 DOI: 10.1155/2016/2643491] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/27/2015] [Indexed: 12/03/2022] Open
Abstract
Direct measurement of recovery from brain injury is an important goal in neurorehabilitation, and requires reliable, objective, and interpretable measures of changes in brain function, referred to generally as “neuroplasticity.” One popular imaging modality for measuring neuroplasticity is task-based functional magnetic resonance imaging (t-fMRI). In the field of neurorehabilitation, however, assessing neuroplasticity using t-fMRI presents a significant challenge. This commentary reviews t-fMRI changes commonly reported in patients with cerebral palsy or acquired brain injuries, with a focus on studies of motor rehabilitation, and discusses complexities surrounding their interpretations. Specifically, we discuss the difficulties in interpreting t-fMRI changes in terms of their underlying causes, that is, differentiating whether they reflect genuine reorganisation, neurological restoration, compensation, use of preexisting redundancies, changes in strategy, or maladaptive processes. Furthermore, we discuss the impact of heterogeneous disease states and essential t-fMRI processing steps on the interpretability of activation patterns. To better understand therapy-induced neuroplastic changes, we suggest that researchers utilising t-fMRI consider concurrently acquiring information from an additional modality, to quantify, for example, haemodynamic differences or microstructural changes. We outline a variety of such supplementary measures for investigating brain reorganisation and discuss situations in which they may prove beneficial to the interpretation of t-fMRI data.
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