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Atchut KA, Shetty L, Ravichandran K. Role of diffusion tensor imaging in stenotic and non-stenotic spinal canal. J Med Imaging Radiat Sci 2023; 54:699-706. [PMID: 37891147 DOI: 10.1016/j.jmir.2023.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/28/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND AND PURPOSE Cervical Spondylotic Myelopathy (CSM) is a gradually escalating spinal cord disturbance set in motion by the degenerative narrowing of the vertebral canal. Routine MRI may fail to detect the subtle early alterations of the cord. MRI Diffusion Tensor Imaging (DTI) possesses the potential to detect these changes. This study intends to estimate the potential of the DTI technique in non-stenotic & stenotic spinal canals in individuals affected with CSM. METHODOLOGY Sixty-four subjects who met the requirements of the inclusion criteria were incorporated into the investigation. All subjects underwent routine MRI sequences in addition to DTI of the cervical spine region. Scalars such as Fractional Anisotropy (FA), besides Apparent Diffusion Coefficient (ADC), were computed at each cervical intervertebral fibrocartilaginous disc level for all subjects. DTI fiber tractography was then performed to qualitatively assess the microstructural integrity of the tracts. RESULTS A noteworthy difference (p<0.05) was seen in the FA parameter and ADC parameter values between the stenotic and non-stenotic groups, with the non-stenotic group having a higher mean FA and a lower ADC than the stenotic group (at the level of stenosis). A significant difference in age was seen between both groups, with most of the patients in the stenotic group belonging to 40 years and above. Tractography helped in demonstrating the morphology of the fiber tracts. CONCLUSION DTI parameters, namely FA and ADC, are sensitive to damage to the white matter and can be used to detect microstructural changes in the cord. However, standardization of the protocol is necessary when imaging the spinal canal.
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Affiliation(s)
- Kauthankar Akshada Atchut
- Department of Radiodiagnosis and Imaging, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, Karnataka, India
| | - Lathika Shetty
- Department of Radiodiagnosis and Imaging, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, Karnataka, India
| | - Kayalvizhi Ravichandran
- Medical Imaging Technology, Department of Radiodiagnosis and Imaging, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, Karnataka, India.
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Ahmad A, Parker D, Dheer S, Samani ZR, Verma R. 3D-QCNet - A pipeline for automated artifact detection in diffusion MRI images. Comput Med Imaging Graph 2023; 103:102151. [PMID: 36502764 PMCID: PMC10494975 DOI: 10.1016/j.compmedimag.2022.102151] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/27/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022]
Abstract
Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying and removing them is essential to ensure the accuracy and viability of any post-processing carried out on these scans. This makes quality control (QC) a crucial first step prior to any analysis of dMRI data. Several QC methods for artifact detection exist, however they suffer from problems like requiring manual intervention and the inability to generalize across different artifacts and datasets. In this paper, we propose an automated deep learning (DL) pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion volumes for automatic artifact detection. Our method is validated on 9000 volumes sourced from 7 large clinical datasets spanning different acquisition protocols (with different gradient directions, high and low b-values, single-shell and multi-shell acquisitions) from multiple scanners. Additionally, they represent diverse subject demographics including age, sex and the presence or absence of pathologies. Our QC method is found to accurately generalize across this heterogenous data by correctly detecting 92% artifacts on average across our test set. This consistent performance over diverse datasets underlines the generalizability of our method, which currently is a significant barrier hindering the widespread adoption of automated QC techniques. Thus, 3D-QCNet can be integrated into diffusion pipelines to effectively automate the arduous and time-intensive process of artifact detection.
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Affiliation(s)
- Adnan Ahmad
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Suhani Dheer
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zahra Riahi Samani
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
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Safri AA, Nassir CMNCM, Iman IN, Mohd Taib NH, Achuthan A, Mustapha M. Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects. World J Clin Cases 2022; 10:8450-8462. [PMID: 36157806 PMCID: PMC9453345 DOI: 10.12998/wjcc.v10.i24.8450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/20/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023] Open
Abstract
Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.
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Affiliation(s)
- Amanina Ahmad Safri
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Che Mohd Nasril Che Mohd Nassir
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Ismail Nurul Iman
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Nur Hartini Mohd Taib
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Anusha Achuthan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Muzaimi Mustapha
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Neurosciences, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
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Shin BK, Park HY, Rim H, Jung JY, Paek S, Yoo YJ, Yoon MJ, Hong BY, Lim SH. Preservation of Cerebellar Afferent Pathway May Be Related to Good Hand Function in Patients with Stroke. Life (Basel) 2022; 12:959. [PMID: 35888049 PMCID: PMC9318318 DOI: 10.3390/life12070959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 11/28/2022] Open
Abstract
Many chronic stroke patients suffer from worsened hand function, and functional recovery of the hand does not occur well after six months of stroke. Therefore, predicting final hand function after stroke through acute phase imaging would be an important issue in counseling with the patients or their family. Thus, we investigated the remaining white matter integrity in the corticospinal tract (CST) and cortico-ponto-cerebellar tract (CPCT) at the acute stage of stroke and chronic hand function after stroke, and present the cut-off value of fiber number (FN) and fractional anisotropy (FA) of CST and CPCT at the acute stage for predicting final hand function after the recovery period. This retrospective case-control study included 18 stroke patients who were classified into two groups: poor hand function with stroke (n = 11) and good hand function with stroke (n = 7). DTI was done within two months ± 15 days after onset, and the Jebson's Hand Function test was conducted 6-12 months after onset. The investigation of white matter was focused on the values of FN and FA for CST and CPCT, which were measured separately. The normalized (affected/non-affected) FA and FN values in the CPCT in the good hand function group were higher than those in the poor hand function group. The normalized FN and FA values in the CST were not significantly different between the poor hand function group and the good hand function group. The normalized cut-off value that distinguished the good hand function group from the poor hand function group was 0.8889 for FA in the CPCT. The integrity of the CPCT in the acute stage was associated with hand function in the chronic stage after a stroke. Ultimately, the integrity of the CPCT in the early stage after onset can be used to predict chronic hand function. Based on these results, cerebellar afferent fiber measurements may be a useful addition to predict hand function and plan specific rehabilitation strategies in stroke patients.
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Affiliation(s)
- Bo Kyung Shin
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Hae-Yeon Park
- Department of Rehabilitation Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
| | - Hanee Rim
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Ji Yoon Jung
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Sungwoo Paek
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Yeun Jie Yoo
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Mi-Jeong Yoon
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Bo Young Hong
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
| | - Seong Hoon Lim
- Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (B.K.S.); (H.R.); (J.Y.J.); (S.P.); (Y.J.Y.); (M.-J.Y.); (B.Y.H.)
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Alms C, Eseonu CI. Comparative Quantification of Diffusion Tensor Tractography Using Automated Whole Brain MRI Tractography for Intracranial Tumor Surgery: Technical Note. Cureus 2022; 14:e25546. [PMID: 35800828 PMCID: PMC9246502 DOI: 10.7759/cureus.25546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2022] [Indexed: 11/05/2022] Open
Abstract
With the improvement of diffusion tensor imaging (DTI) and algorithms, diffusion tensor tractography (DTT) may provide quantitative information on white matter tracts (WMT) that may help quantitatively assess WMT integrity and distortion, which may help with correlations of neurologic function or prognosis. This manuscript is the first to describe a technical method for quantitative analysis of clinically relevant white matter tracts during intracranial tumor surgery. The authors quantitatively analyzed relevant proximal WMT, pre and postoperatively, in a patient undergoing cranial surgery using DTT software to evaluate fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), geodesic anisotropy (GA), tract count, and tract volume. A method was then established to formulate quantitative comparisons between pre and postoperative WMT. Quantitative assessment of the corticospinal and optic radiation tracts revealed significant increases in the FA, GA, and tract count in the corticospinal and optic radiations postoperatively (p<.0001). MD, RD, and AD were found to be significantly diminished postoperatively (p<.0001). The postoperative optic radiations showed diminished volume as a result of damage to the tract pathway. To conclude, the utilization of white matter tractography provides a technical advancement that allows for quantitative comparative assessments of white matter tracts, which could assess the degree of brain changes following tumor surgery.
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Affiliation(s)
- Cindy Alms
- Neurological Surgery, University of Pittsburgh Medical Center (UPMC) Central Pennsylvania, Harrisburg, USA
| | - Chikezie I Eseonu
- Neurological Surgery, University of Pittsburgh Medical Center (UPMC) Central Pennsylvania, Harrisburg, USA
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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
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Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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Ettehadi N, Kashyap P, Zhang X, Wang Y, Semanek D, Desai K, Guo J, Posner J, Laine AF. Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks. Front Hum Neurosci 2022; 16:877326. [PMID: 35431841 PMCID: PMC9005752 DOI: 10.3389/fnhum.2022.877326] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/07/2022] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification ("poor" vs. "good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or "slabs" extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.
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Affiliation(s)
- Nabil Ettehadi
- Heffner Biomedical Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Pratik Kashyap
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - Xuzhe Zhang
- Heffner Biomedical Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Yun Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - David Semanek
- Department of Psychiatry, Columbia University Medical Center, New York, NY, United States
| | - Karan Desai
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - Jia Guo
- Department of Psychiatry, Columbia University Medical Center, New York, NY, United States
- Zuckerman Institute, Columbia University, New York, NY, United States
| | - Jonathan Posner
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - Andrew F. Laine
- Heffner Biomedical Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, United States
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Short SJ, Jang DK, Steiner RJ, Stephens RL, Girault JB, Styner M, Gilmore JH. Diffusion Tensor Based White Matter Tract Atlases for Pediatric Populations. Front Neurosci 2022; 16:806268. [PMID: 35401073 PMCID: PMC8985548 DOI: 10.3389/fnins.2022.806268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 01/27/2022] [Indexed: 01/14/2023] Open
Abstract
Diffusion Tensor Imaging (DTI) is a non-invasive neuroimaging method that has become the most widely employed MRI modality for investigations of white matter fiber pathways. DTI has proven especially valuable for improving our understanding of normative white matter maturation across the life span and has also been used to index clinical pathology and cognitive function. Despite its increasing popularity, especially in pediatric research, the majority of existing studies examining infant white matter maturation depend on regional or white matter skeleton-based approaches. These methods generally lack the sensitivity and spatial specificity of more advanced functional analysis options that provide information about microstructural properties of white matter along fiber bundles. DTI studies of early postnatal brain development show that profound microstructural and maturational changes take place during the first two years of life. The pattern and rate of these changes vary greatly throughout the brain during this time compared to the rest of the life span. For this reason, appropriate image processing of infant MR imaging requires the use of age-specific reference atlases. This article provides an overview of the pre-processing, atlas building, and the fiber tractography procedures used to generate two atlas resources, one for neonates and one for 1- to 2-year-old populations. Via the UNC-NAMIC DTI Fiber Analysis Framework, our pediatric atlases provide the computational templates necessary for the fully automatic analysis of infant DTI data. To the best of our knowledge, these atlases are the first comprehensive population diffusion fiber atlases in early pediatric ages that are publicly available.
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Affiliation(s)
- Sarah J. Short
- Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, United States
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, United States
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dae Kun Jang
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, United States
| | - Rachel J. Steiner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Rebecca L. Stephens
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jessica B. Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - John H. Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children. Neurosci Bull 2022; 38:607-621. [PMID: 35092576 DOI: 10.1007/s12264-022-00820-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).
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Ettehadi N, Zhang X, Wang Y, Semanek D, Guo J, Posner J, Laine AF. Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2756-2760. [PMID: 34891820 DOI: 10.1109/embc46164.2021.9630834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diffusion Tensor Imaging (DTI) is widely used to find brain biomarkers for various stages of brain structural and neuronal development. Processing DTI data requires a detailed Quality Assessment (QA) to detect artifactual volumes amongst a large pool of data. Since large cohorts of brain DTI data are often used in different studies, manual QA of such images is very labor-intensive. In this paper, a deep learning-based tool is developed for quick automatic QA of 3D raw diffusion MR images. We propose a 2-step framework to automate the process of binary (i.e., 'good' vs 'poor') quality classification of diffusion MR images. In the first step, using two separately trained 3D convolutional neural networks with different input sizes, quality labels for individual Regions of Interest (ROIs) sampled from whole DTI volumes are predicted. In the second step, two distinct novel voting systems are designed and fine-tuned to predict the quality label of whole brain DTI volumes using the individual ROI labels predicted in the previous step. Our results demonstrate the validity and practicality of our tool. Specifically, using a balanced dataset of 6,940 manually-labeled 3D DTI volumes from 85 unique subjects for training, validation, and testing, our model achieves 100% accuracy via one voting system, and 98% accuracy via another voting system on the same test set.
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McGee KP, Hwang KP, Sullivan DC, Kurhanewicz J, Hu Y, Wang J, Li W, Debbins J, Paulson E, Olsen JR, Hua CH, Warner L, Ma D, Moros E, Tyagi N, Chung C. Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294. Med Phys 2021; 48:e697-e732. [PMID: 33864283 PMCID: PMC8361924 DOI: 10.1002/mp.14884] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/24/2021] [Accepted: 03/28/2021] [Indexed: 12/16/2022] Open
Abstract
A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.
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Affiliation(s)
- Kiaran P McGee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, Division of Diagnostic Imaging, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Daniel C Sullivan
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - John Kurhanewicz
- Department of Radiology, University of California, San Francisco, California, USA
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Jihong Wang
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Wen Li
- Department of Radiation Oncology, University of Arizona, Tucson, Arizona, USA
| | - Josef Debbins
- Department of Radiology, Barrow Neurologic Institute, Phoenix, Arizona, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado Denver - Anschutz Medical Campus, Denver, Colorado, USA
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - Daniel Ma
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eduardo Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
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12
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Dowdle LT, Ghose G, Ugurbil K, Yacoub E, Vizioli L. Clarifying the role of higher-level cortices in resolving perceptual ambiguity using ultra high field fMRI. Neuroimage 2021; 227:117654. [PMID: 33333319 PMCID: PMC10614695 DOI: 10.1016/j.neuroimage.2020.117654] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/17/2020] [Accepted: 12/05/2020] [Indexed: 12/17/2022] Open
Abstract
The brain is organized into distinct, flexible networks. Within these networks, cognitive variables such as attention can modulate sensory representations in accordance with moment-to-moment behavioral requirements. These modulations can be studied by varying task demands; however, the tasks employed are often incongruent with the postulated functions of a sensory system, limiting the characterization of the system in relation to natural behaviors. Here we combine domain-specific task manipulations and ultra-high field fMRI to study the nature of top-down modulations. We exploited faces, a visual category underpinned by a complex cortical network, and instructed participants to perform either a stimulus-relevant/domain-specific or a stimulus-irrelevant task in the scanner. We found that 1. perceptual ambiguity (i.e. difficulty of achieving a stable percept) is encoded in top-down modulations from higher-level cortices; 2. the right inferior-temporal lobe is active under challenging conditions and uniquely encodes trial-by-trial variability in face perception.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research, University of Minnesota 2021 6th St SE, Minneapolis, MN 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN 55455.
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research, University of Minnesota 2021 6th St SE, Minneapolis, MN 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN 55455
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota 2021 6th St SE, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota 2021 6th St SE, Minneapolis, MN 55455, United States
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota 2021 6th St SE, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN 55455.
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13
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Gong T, Tong Q, Li Z, He H, Zhang H, Zhong J. Deep learning-based method for reducing residual motion effects in diffusion parameter estimation. Magn Reson Med 2020; 85:2278-2293. [PMID: 33058279 DOI: 10.1002/mrm.28544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 09/14/2020] [Accepted: 09/15/2020] [Indexed: 11/08/2022]
Abstract
PURPOSE Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI.
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Affiliation(s)
- Ting Gong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Computer Science & Centre for Medical Image Computing, University College London, London, UK
| | - Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Zhiwei Li
- Department of Instrument Science & Technology, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, London, UK
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Imaging Sciences, University of Rochester, Rochester, NY, USA
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14
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The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics. Neuroimage 2020; 222:117252. [PMID: 32800991 DOI: 10.1016/j.neuroimage.2020.117252] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/24/2020] [Accepted: 08/04/2020] [Indexed: 12/27/2022] Open
Abstract
Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 294), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.
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15
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Samani ZR, Alappatt JA, Parker D, Ismail AAO, Verma R. QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images. Front Neurosci 2020; 13:1456. [PMID: 32038150 PMCID: PMC6987246 DOI: 10.3389/fnins.2019.01456] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 12/31/2019] [Indexed: 12/04/2022] Open
Abstract
Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator, for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone, and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ∼332,000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters.
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Affiliation(s)
- Zahra Riahi Samani
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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16
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Zhang H, Feng Y, Cheng L, Liu J, Li H, Jiang H. Application of diffusion tensor tractography in the surgical treatment of brain tumors located in functional areas. Oncol Lett 2020; 19:615-622. [PMID: 31897177 PMCID: PMC6924117 DOI: 10.3892/ol.2019.11167] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 06/14/2019] [Indexed: 12/21/2022] Open
Abstract
The present study aimed to explore the application of diffusion tensor tractography (DTT) in the preoperative planning and prognostic evaluation of tumors located in the functional areas of the brain. A total of 42 patients diagnosed with intracranial tumors were randomly assigned to either the trial or the control group. DT imaging (DTI) was performed on the basis of preoperative conventional magnetic resonance imaging (MRI) and analyzed for patients in the trial group. Patients in the control group underwent only routine MRI scans. The effect of DTT on the prognosis of patients was evaluated by tumor resection rate and quality of life evaluation using Karnofsky performance score (KPS) comparison between the trial and control groups. There were no significant differences for total tumor removal rate in the trial group (85.71%) compared with that in the control group (71.43%) (P>0.05). The rate of postoperative symptom improvement in the trial group (85.71%) was significantly higher compared with that in the control group (47.62%) (P<0.05). The KPS value of the trial group was significantly higher postoperatively (78.57±17.40) compared with that preoperatively (66.67±16.23) (P<0.05). The KPS value of the control group postoperatively (72.38±19.21) was significantly higher compared with that preoperatively (66.67±16.00) (P<0.05). The postoperative KPS improvement rate [postoperative value-preoperative value)/preoperative value] of the trial group was significantly higher compared with that in the control group. In conclusion, the use of DTT is an effective supplement to traditional MRI, with particular relevance in preoperative planning, particularly for tumors in the functional area of the brain, and can significantly improve the prognostic function of patients.
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Affiliation(s)
- Hongliang Zhang
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266005, P.R. China
| | - Yugong Feng
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266005, P.R. China
| | - Lei Cheng
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266005, P.R. China
| | - Jie Liu
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266005, P.R. China
| | - Huanting Li
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266005, P.R. China
| | - Hong Jiang
- Department of Physiology, Shandong Key Laboratory of Pathogenesis and Prevention of Neurological Disorders and State Key Disciplines: Physiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong 266071, P.R. China
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17
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Kim H, Irimia A, Hobel SM, Pogosyan M, Tang H, Petrosyan P, Blanco REC, Duffy BA, Zhao L, Crawford KL, Liew SL, Clark K, Law M, Mukherjee P, Manley GT, Van Horn JD, Toga AW. The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data. Front Neuroinform 2019; 13:60. [PMID: 31555116 PMCID: PMC6722229 DOI: 10.3389/fninf.2019.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 08/12/2019] [Indexed: 12/15/2022] Open
Abstract
Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.
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Affiliation(s)
- Hosung Kim
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Andrei Irimia
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
- Department of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Samuel M. Hobel
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Mher Pogosyan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Haoteng Tang
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Petros Petrosyan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Rita Esquivel Castelo Blanco
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Ben A. Duffy
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Lu Zhao
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Karen L. Crawford
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Sook-Lei Liew
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Kristi Clark
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Meng Law
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Pratik Mukherjee
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Geoffrey T. Manley
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - John D. Van Horn
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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18
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Phan TV, Smeets D, Talcott JB, Vandermosten M. Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods. Dev Cogn Neurosci 2018; 33:206-223. [PMID: 29033222 PMCID: PMC6969273 DOI: 10.1016/j.dcn.2017.08.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/28/2017] [Accepted: 08/17/2017] [Indexed: 11/25/2022] Open
Abstract
The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.
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Affiliation(s)
- Thanh Vân Phan
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; icometrix, Research and Development, Leuven, Belgium.
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Joel B Talcott
- Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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19
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Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage 2018; 184:801-812. [PMID: 30267859 PMCID: PMC6264528 DOI: 10.1016/j.neuroimage.2018.09.073] [Citation(s) in RCA: 171] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts. Two tools to automatically perform QC of diffusion MRI data. Automated generation of single subject reports for visual inspection and database. Group databases and reports allow to compare subjects within and between studies. Categorical and continuous variables can be used to update the reports.
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Affiliation(s)
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Sana Suri
- Department of Psychiatry, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
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20
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Baum GL, Roalf DR, Cook PA, Ciric R, Rosen AFG, Xia C, Elliott MA, Ruparel K, Verma R, Tunç B, Gur RC, Gur RE, Bassett DS, Satterthwaite TD. The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 2018; 173:275-286. [PMID: 29486323 PMCID: PMC5911236 DOI: 10.1016/j.neuroimage.2018.02.041] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/19/2018] [Accepted: 02/21/2018] [Indexed: 12/27/2022] Open
Abstract
Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.
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Affiliation(s)
- Graham L Baum
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Rastko Ciric
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Adon F G Rosen
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Cedric Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Mark A Elliott
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Birkan Tunç
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
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21
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Chavez S, Viviano J, Zamyadi M, Kingsley PB, Kochunov P, Strother S, Voineskos A. A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom. Magn Reson Imaging 2018; 46:28-39. [PMID: 29054737 PMCID: PMC5800507 DOI: 10.1016/j.mri.2017.07.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 07/21/2017] [Accepted: 07/21/2017] [Indexed: 10/18/2022]
Abstract
PURPOSE To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. METHODS A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. RESULTS The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. CONCLUSIONS A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.
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Affiliation(s)
- Sofia Chavez
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.
| | | | | | - Peter B Kingsley
- Department of Radiology, North Shore University Hospital, Manhasset, USA
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland, School of Medicine, Baltimore, USA
| | - Stephen Strother
- Rotman Research Institute, Baycrest, Toronto, Canada; Medical Biophysics Department, University of Toronto, Toronto, Canada
| | - Aristotle Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Canada
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22
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Abstract
Diffusion Tensor Imaging is an MRI technique that allows in vivo noninvasive measurement of the translational motion of water, providing information about its anisotropy (or lack of it) in different tissues. DTI has been commonly used to quantitatively measure the integrity of tissues which may be compromised by neurological disease, such as white matter tracks of the brain, which normally impart significant anisotropy to water motion in healthy brains. However, this anisotropic effect is diminished when axonal or neuronal damage is present. This chapter describes a standard protocol for DTI data acquisition in preclinical studies.
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Affiliation(s)
- Silvia Lope-Piedrafita
- Servei de Ressonància Magnètica Nuclear, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain.
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain.
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23
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Tamnes CK, Roalf DR, Goddings AL, Lebel C. Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress. Dev Cogn Neurosci 2017; 33:161-175. [PMID: 29229299 PMCID: PMC6969268 DOI: 10.1016/j.dcn.2017.12.002] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/18/2017] [Accepted: 12/04/2017] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.
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Affiliation(s)
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Catherine Lebel
- Department of Radiology, Cumming School of Medicine, and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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24
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Abstract
OBJECTIVE Outline effects of functional neuroimaging on neuropsychology over the past 25 years. METHOD Functional neuroimaging methods and studies will be described that provide a historical context, offer examples of the utility of neuroimaging in specific domains, and discuss the limitations and future directions of neuroimaging in neuropsychology. RESULTS Tracking the history of publications on functional neuroimaging related to neuropsychology indicates early involvement of neuropsychologists in the development of these methodologies. Initial progress in neuropsychological application of functional neuroimaging has been hampered by costs and the exposure to ionizing radiation. With rapid evolution of functional methods-in particular functional MRI (fMRI)-neuroimaging has profoundly transformed our knowledge of the brain. Its current applications span the spectrum of normative development to clinical applications. The field is moving toward applying sophisticated statistical approaches that will help elucidate distinct neural activation networks associated with specific behavioral domains. The impact of functional neuroimaging on clinical neuropsychology is more circumscribed, but the prospects remain enticing. CONCLUSIONS The theoretical insights and empirical findings of functional neuroimaging have been led by many neuropsychologists and have transformed the field of behavioral neuroscience. Thus far they have had limited effects on the clinical practices of neuropsychologists. Perhaps it is time to add training in functional neuroimaging to the clinical neuropsychologist's toolkit and from there to the clinic or bedside. (PsycINFO Database Record
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Affiliation(s)
- David R. Roalf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine Philadelphia, Philadelphia, PA, 19104
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine Philadelphia, Philadelphia, PA, 19104
- Lifespan Brain Institute (LiBI) at the University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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25
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Atkinson-Clement C, Pinto S, Eusebio A, Coulon O. Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis. Neuroimage Clin 2017; 16:98-110. [PMID: 28765809 PMCID: PMC5527156 DOI: 10.1016/j.nicl.2017.07.011] [Citation(s) in RCA: 169] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 07/13/2017] [Accepted: 07/14/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Neuroimaging studies help us better understand the pathophysiology and symptoms of Parkinson's disease (PD). In several of these studies, diffusion tensor imaging (DTI) was used to investigate structural changes in cerebral tissue. Although data have been provided as regards to specific brain areas, a whole brain meta-analysis is still missing. METHODS We compiled 39 studies in this meta-analysis: 14 used fractional anisotropy (FA), 1 used mean diffusivity (MD), and 24 used both indicators. These studies comprised 1855 individuals, 1087 with PD and 768 healthy controls. Regions of interest were classified anatomically (subcortical structures; white matter; cortical areas; cerebellum). Our statistical analysis considered the disease effect size (DES) as the main variable; the heterogeneity index (I2) and Pearson's correlations between the DES and co-variables (demographic, clinical and MRI parameters) were also calculated. RESULTS Our results showed that FA-DES and MD-DES were able to distinguish between patients and healthy controls. Significant differences, indicating degenerations, were observed within the substantia nigra, the corpus callosum, and the cingulate and temporal cortices. Moreover, some findings (particularly in the corticospinal tract) suggested opposite brain changes associated with PD. In addition, our results demonstrated that MD-DES was particularly sensitive to clinical and MRI parameters, such as the number of DTI directions and the echo time within white matter. CONCLUSIONS Despite some limitations, DTI appears as a sensitive method to study PD pathophysiology and severity. The association of DTI with other MRI methods should also be considered and could benefit the study of brain degenerations in PD.
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Affiliation(s)
| | - Serge Pinto
- Aix Marseille Univ, CNRS, LPL, Aix-en-Provence, France
- Brain and Language Research Institute, Aix Marseille Univ, Aix-en-Provence, France
| | - Alexandre Eusebio
- Aix Marseille Univ, APHM, Hôpital de la Timone, Service de Neurologie et Pathologie du Mouvement, Marseille, France
- Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille France
| | - Olivier Coulon
- Brain and Language Research Institute, Aix Marseille Univ, Aix-en-Provence, France
- Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille France
- Aix Marseille Univ, CNRS, LSIS lab, UMR 7296, Marseille, France
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26
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Chow HM, Chang S. White matter developmental trajectories associated with persistence and recovery of childhood stuttering. Hum Brain Mapp 2017; 38:3345-3359. [PMID: 28390149 PMCID: PMC5632574 DOI: 10.1002/hbm.23590] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 03/08/2017] [Accepted: 03/20/2017] [Indexed: 12/16/2022] Open
Abstract
Stuttering affects the fundamental human ability of fluent speech production, and can have a significant negative impact on an individual's psychosocial development. While the disorder affects about 5% of all preschool children, approximately 80% of them recover naturally within a few years of stuttering onset. The pathophysiology and neuroanatomical development trajectories associated with persistence and recovery of stuttering are still largely unknown. Here, the first mixed longitudinal diffusion tensor imaging (DTI) study of childhood stuttering has been reported. A total of 195 high quality DTI scans from 35 children who stutter (CWS) and 43 controls between 3 and 12 years of age were acquired, with an average of three scans per child, each collected approximately a year apart. Fractional anisotropy (FA), a measure reflecting white matter structural coherence, was analyzed voxel-wise to examine group and age-related differences using a linear mixed-effects (LME) model. Results showed that CWS exhibited decreased FA relative to controls in the left arcuate fasciculus, underlying the inferior parietal and posterior temporal areas, and the mid body of corpus callosum. Further, white matter developmental trajectories reflecting growth rate of these tract regions differentiated children with persistent stuttering from those who recovered from stuttering. Specifically, a reduction in FA growth rate (i.e., slower FA growth with age) in persistent children relative to fluent controls in the left arcuate fasciculus and corpus callosum was found, which was not evident in recovered children. These findings provide first glimpses into the possible neural mechanisms of onset, persistence, and recovery of childhood stuttering. Hum Brain Mapp 38:3345-3359, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Ho Ming Chow
- Department of PsychiatryUniversity of MichiganAnn ArborMichigan
| | - Soo‐Eun Chang
- Department of PsychiatryUniversity of MichiganAnn ArborMichigan
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27
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Aoki Y, Inokuchi R. Diffusion tensor imaging in non-traumatic brain injury. Dev Med Child Neurol 2017; 59:121-122. [PMID: 27917471 DOI: 10.1111/dmcn.13336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yuta Aoki
- The Child Study Center at New York University Langone Medical Center, New York, NY, USA
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28
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Shi Y, Budin F, Yapuncich E, Rumple A, Young JT, Payne C, Zhang X, Hu X, Godfrey J, Howell B, Sanchez MM, Styner MA. UNC-Emory Infant Atlases for Macaque Brain Image Analysis: Postnatal Brain Development through 12 Months. Front Neurosci 2017; 10:617. [PMID: 28119564 PMCID: PMC5222830 DOI: 10.3389/fnins.2016.00617] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 12/26/2016] [Indexed: 12/20/2022] Open
Abstract
Computational anatomical atlases have shown to be of immense value in neuroimaging as they provide age appropriate reference spaces alongside ancillary anatomical information for automated analysis such as subcortical structural definitions, cortical parcellations or white fiber tract regions. Standard workflows in neuroimaging necessitate such atlases to be appropriately selected for the subject population of interest. This is especially of importance in early postnatal brain development, where rapid changes in brain shape and appearance render neuroimaging workflows sensitive to the appropriate atlas choice. We present here a set of novel computation atlases for structural MRI and Diffusion Tensor Imaging as crucial resource for the analysis of MRI data from non-human primate rhesus monkey (Macaca mulatta) data in early postnatal brain development. Forty socially-housed infant macaques were scanned longitudinally at ages 2 weeks, 3, 6, and 12 months in order to create cross-sectional structural and DTI atlases via unbiased atlas building at each of these ages. Probabilistic spatial prior definitions for the major tissue classes were trained on each atlas with expert manual segmentations. In this article we present the development and use of these atlases with publicly available tools, as well as the atlases themselves, which are publicly disseminated to the scientific community.
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Affiliation(s)
- Yundi Shi
- Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
| | | | - Eva Yapuncich
- Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
| | - Ashley Rumple
- Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
| | - Jeffrey T Young
- Department of Psychiatry, University of North CarolinaChapel Hill, NC, USA; Department of Computer Science, University of North CarolinaChapel Hill, NC, USA
| | - Christa Payne
- Division of Autism and Related Developmental Disabilities, Department of Pediatrics, Marcus Autism Center, Children's Healthcare of Atlanta, Emory School of Medicine Atlanta, GA, USA
| | - Xiaodong Zhang
- Yerkes National Primate Research Center, Emory University Atlanta, GA, USA
| | - Xiaoping Hu
- Department of Bioengineering, University of California, Riverside Riverside, CA, USA
| | - Jodi Godfrey
- Yerkes National Primate Research Center, Emory University Atlanta, GA, USA
| | - Brittany Howell
- Department of Child Psychology, Institute of Child Development, University of Minnesota Minneapolis, MN, USA
| | - Mar M Sanchez
- Yerkes National Primate Research Center, Emory UniversityAtlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory UniversityAtlanta, GA, USA
| | - Martin A Styner
- Department of Psychiatry, University of North CarolinaChapel Hill, NC, USA; Department of Computer Science, University of North CarolinaChapel Hill, NC, USA
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29
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Roalf DR, Quarmley M, Elliott MA, Satterthwaite TD, Vandekar SN, Ruparel K, Gennatas ED, Calkins ME, Moore TM, Hopson R, Prabhakaran K, Jackson CT, Verma R, Hakonarson H, Gur RC, Gur RE. The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort. Neuroimage 2016; 125:903-919. [PMID: 26520775 PMCID: PMC4753778 DOI: 10.1016/j.neuroimage.2015.10.068] [Citation(s) in RCA: 151] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 10/19/2015] [Accepted: 10/24/2015] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics. METHODS All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep. RESULTS TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images. CONCLUSION Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.
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Affiliation(s)
- David R Roalf
- Neuropsychiatry Section, Department of Psychiatry, USA.
| | | | - Mark A Elliott
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA
| | | | - Simon N Vandekar
- Neuropsychiatry Section, Department of Psychiatry, USA; Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Neuropsychiatry Section, Department of Psychiatry, USA
| | | | | | - Tyler M Moore
- Neuropsychiatry Section, Department of Psychiatry, USA
| | - Ryan Hopson
- Neuropsychiatry Section, Department of Psychiatry, USA
| | | | | | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA; Section of Biomedical Image Analysis, University of Pennsylvania, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruben C Gur
- Neuropsychiatry Section, Department of Psychiatry, USA; Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA
| | - Raquel E Gur
- Neuropsychiatry Section, Department of Psychiatry, USA; Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA
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