1
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Kara D, Koenig K, Lowe M, Nguyen C, Sakaie K. Facilitating diffusion tensor imaging of the brain during continuous gross head motion with first and second order motion compensating diffusion gradients. Magn Reson Med 2024; 91:1556-1566. [PMID: 38073070 PMCID: PMC10872734 DOI: 10.1002/mrm.29924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/19/2023] [Accepted: 10/24/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE To demonstrate the feasibility of motion compensating diffusion gradient schemes in the acquisition of quality diffusion tensor images (DTI) of the brain during continuous gross head motion. METHODS Five healthy subjects were scanned using a clinical 3 T MRI with and without continuous head motion. For one volunteer, DTI data was acquired using standard (M0) diffusion-weighted (DW) gradients, and first (M1) and second (M2) order gradient schemes that were previously developed for use in cardiac DTI. In four additional volunteers, DTI data was acquired with M0 and M2 gradients. DTI parameters were calculated and compared with established retrospective motion corrections. RESULTS In the absence of motion, DTI parameters calculated from M0, M1, and M2 data were consistent. In the presence of motion, up to 44% of DW images acquired with M0 gradients were corrupted by signal dropout, compared to 0% of the M2 images. In voxelwise comparisons, DTI parameters calculated using motion-M0 data were elevated compared to reference data. Retrospective corrections for extreme motion applied to motion-M0 data did not improve consistency with reference data in cases where motion corrupted >15% of DW images. In contrast, DTI parameters calculated with motion-M2 data were consistent with reference data. CONCLUSION This proof-of-principle study demonstrates that motion compensating diffusion gradients can mitigate artifacts because of continuous motion in DTI of the brain and offers promise for improved DTI accessibility. Further study will be necessary to determine the robustness of the approach in patient populations with high susceptibility to head motion.
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Affiliation(s)
- Danielle Kara
- Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic
| | | | - Mark Lowe
- Imaging Sciences, Imaging Institute, Cleveland Clinic
| | - Christopher Nguyen
- Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic
- Imaging Sciences, Imaging Institute, Cleveland Clinic
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic and Case Western Reserve University
| | - Ken Sakaie
- Imaging Sciences, Imaging Institute, Cleveland Clinic
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2
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Rosberg A, Tuulari JJ, Kumpulainen V, Lukkarinen M, Pulli EP, Silver E, Copeland A, Saukko E, Saunavaara J, Lewis JD, Karlsson L, Karlsson H, Merisaari H. Test-retest reliability of diffusion tensor imaging scalars in 5-year-olds. Hum Brain Mapp 2022; 43:4984-4994. [PMID: 36098477 PMCID: PMC9582361 DOI: 10.1002/hbm.26064] [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] [Received: 03/15/2022] [Revised: 08/08/2022] [Accepted: 08/21/2022] [Indexed: 11/22/2022] Open
Abstract
Diffusion tensor imaging (DTI) has provided great insights into the microstructural features of the developing brain. However, DTI images are prone to several artifacts and the reliability of DTI scalars is of paramount importance for interpreting and generalizing the findings of DTI studies, especially in the younger population. In this study, we investigated the intrascan test–retest repeatability of four DTI scalars: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in 5‐year‐old children (N = 67) with two different data preprocessing approaches: a volume censoring pipeline and an outlier replacement pipeline. We applied a region of interest (ROI) and a voxelwise analysis after careful quality control, tensor fitting and tract‐based spatial statistics. The data had three subsets and each subset included 31, 32, or 33 directions thus a total of 96 unique uniformly distributed diffusion encoding directions per subject. The repeatability of DTI scalars was evaluated with intraclass correlation coefficient (ICC(3,1)) and the variability between test and retest subsets. The results of both pipelines yielded good to excellent (ICC(3,1) > 0.75) reliability for most of the ROIs and an overall low variability (<10%). In the voxelwise analysis, FA and RD had higher ICC(3,1) values compared to AD and MD and the variability remained low (<12%) across all scalars. Our results suggest high intrascan repeatability in pediatric DTI and lend confidence to the use of the data in future cross‐sectional and longitudinal studies.
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Affiliation(s)
- Aylin Rosberg
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital and University of Turku, Turku, Finland.,Department of Radiology, Turku University Hospital, Turku, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital and University of Turku, Turku, Finland.,Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - Venla Kumpulainen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Minna Lukkarinen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland.,Department of Pediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Elmo P Pulli
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Eero Silver
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Anni Copeland
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital and University of Turku, Turku, Finland
| | - John D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital and University of Turku, Turku, Finland.,Department of Pediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland.,Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital and University of Turku, Turku, Finland.,Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Centre, Department of Clinical Medicine, University of Turku, Turku, Finland.,Department of Radiology, Turku University Hospital, Turku, Finland
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3
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Koirala N, Kleinman D, Perdue MV, Su X, Villa M, Grigorenko EL, Landi N. Widespread effects of dMRI data quality on diffusion measures in children. Hum Brain Mapp 2021; 43:1326-1341. [PMID: 34799957 PMCID: PMC8837592 DOI: 10.1002/hbm.25724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 11/02/2021] [Accepted: 11/11/2021] [Indexed: 12/12/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.
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Affiliation(s)
| | | | - Meaghan V Perdue
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychological Sciences, University of Connecticut, Connecticut, USA
| | - Xing Su
- Haskins Laboratories, New Haven, Connecticut, USA
| | - Martina Villa
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychological Sciences, University of Connecticut, Connecticut, USA
| | - Elena L Grigorenko
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychology, University of Houston, Houston, Texas, USA
| | - Nicole Landi
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychological Sciences, University of Connecticut, Connecticut, USA
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4
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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5
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Dauleac C, Bannier E, Cotton F, Frindel C. Effect of distortion corrections on the tractography quality in spinal cord diffusion-weighted imaging. Magn Reson Med 2021; 85:3241-3255. [PMID: 33475180 DOI: 10.1002/mrm.28665] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/03/2020] [Accepted: 12/10/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To assess the impact of a different distortion correction (DC) method and patient geometry (sagittal balance) on the quality of spinal cord tractography rendering according to different tractography approaches. METHODS Forty-four adults free of spinal cord diseases underwent cervical diffusion-weighted imaging. The phase-encoding direction was head→foot. Sequence with opposed polarities (foot→head) was acquired to perform DC. Eddy-current, motion effects, and susceptibility artifact correction methods were used for DC, and two deterministic and one probabilistic tractography approaches were evaluated using MRtrix and DSI Studio tractography software. Fiber length and number of fibers were extracted to evaluate the quality of the tractography rendering. For each subject, cervical lordosis was measured to assess patient geometry. The angle between the main direction of the spinal cord and the orientation of the acquisition box were computed at each spine level to assess acquisition geometry and define an angle threshold for which a tractography of good quality is no longer possible. RESULTS There was a significant improvement in tractography quality after performing DC with susceptibility artifact correction using a deterministic approach based on tensor. Before DC, the angle threshold was defined at C6 (15.2°) compared with C7 (21.9°) after corrections, demonstrating the importance of spinal cord angulation for DC. CONCLUSION The impact of DC on tractography quality is greatly impacted by acquisition geometry. To obtain a good-quality tractography, we propose as a future perspective to adapt the acquisition geometry to that of the patient by automatically adjusting the acquisition box.
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Affiliation(s)
- Corentin Dauleac
- Department of Neurosurgery, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France.,Université de Lyon, Université Claude Bernard Lyon I, Lyon, France.,Laboratoire CREATIS, CNRS UMR5220, INSA-Lyon, Université de Lyon I, Inserm U1206, Lyon, France
| | - Elise Bannier
- Université de Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn, France.,Department of Radiology, CHU de Rennes, Rennes, France
| | - François Cotton
- Université de Lyon, Université Claude Bernard Lyon I, Lyon, France.,Laboratoire CREATIS, CNRS UMR5220, INSA-Lyon, Université de Lyon I, Inserm U1206, Lyon, France.,Department of Radiology, Centre Hospitalier de Lyon Sud, Hospices Civils de Lyon, Lyon, France
| | - Carole Frindel
- Université de Lyon, Université Claude Bernard Lyon I, Lyon, France.,Laboratoire CREATIS, CNRS UMR5220, INSA-Lyon, Université de Lyon I, Inserm U1206, Lyon, France
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6
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Drobinin V, Van Gestel H, Helmick CA, Schmidt MH, Bowen CV, Uher R. Reliability of multimodal MRI brain measures in youth at risk for mental illness. Brain Behav 2020; 10:e01609. [PMID: 32304355 PMCID: PMC7303399 DOI: 10.1002/brb3.1609] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 03/01/2020] [Accepted: 03/03/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION A new generation of large-scale studies is using neuroimaging to investigate adolescent brain development across health and disease. However, imaging artifacts such as head motion remain a challenge and may be exacerbated in pediatric clinical samples. In this study, we assessed the scan-rescan reliability of multimodal MRI in a sample of youth enriched for risk of mental illness. METHODS We obtained repeated MRI scans, an average of 2.7 ± 1.4 weeks apart, from 50 youth (mean age 14.7 years, SD = 4.4). Half of the sample (52%) had a diagnosis of an anxiety disorder; 22% had attention-deficit/hyperactivity disorder (ADHD). We quantified reliability with the test-retest intraclass correlation coefficient (ICC). RESULTS Gray matter measurements were highly reliable with mean ICCs as follows: cortical volume (ICC = 0.90), cortical surface area (ICC = 0.89), cortical thickness (ICC = 0.82), and local gyrification index (ICC = 0.85). White matter volume reliability was excellent (ICC = 0.98). Diffusion tensor imaging (DTI) components were also highly reliable. Fractional anisotropy was most consistently measured (ICC = 0.88), followed by radial diffusivity (ICC = 0.84), mean diffusivity (ICC = 0.81), and axial diffusivity (ICC = 0.78). We also observed regional variability in reconstruction, with some brain structures less reliably reconstructed than others. CONCLUSIONS Overall, we showed that developmental MRI measures are highly reliable, even in youth at risk for mental illness and those already affected by anxiety and neurodevelopmental disorders. Yet, caution is warranted if patterns of results cluster within regions of lower reliability.
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Affiliation(s)
- Vladislav Drobinin
- Department of Medical Neuroscience, Dalhousie University, Halifax, NS, Canada.,Nova Scotia Health Authority, Halifax, NS, Canada
| | | | - Carl A Helmick
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Matthias H Schmidt
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Chris V Bowen
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Medical Neuroscience, Dalhousie University, Halifax, NS, Canada.,Nova Scotia Health Authority, Halifax, NS, Canada.,Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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7
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Máté A, Kis D, Czigner A, Fischer T, Halász L, Barzó P. Connectivity-based segmentation of the brainstem by probabilistic tractography. Brain Res 2018; 1690:74-88. [PMID: 29555236 DOI: 10.1016/j.brainres.2018.03.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 02/16/2018] [Accepted: 03/08/2018] [Indexed: 11/26/2022]
Abstract
Diffusion magnetic resonance imaging is a non-invasive tool increasingly used for the investigation of brain connectivity in vivo. In this paper we propose a method that allows segmentation of the brainstem to four subregions (frontopontine, motor, sensory and reticular) based on connections to supratentorial structures, thereby eliminating the need for using anatomical landmarks within the brainstem for the identification of these subregions. The feasibility of connectivity-based brainstem segmentation was investigated in a group of healthy subjects (n = 20). Multifiber probabilistic tractography was performed using the FMRIB Software Library, and connections between a pontomesencephalic seed mask and four supratentorial target regions (anterior and posterior limbs of the internal capsule, sensory and medial thalamus) were used to determine connectivity maps of the brainstem. Results were compared with a neuroanatomy atlas and histological sections, confirming good anatomic correspondence. The four subregions detected by the connectivity-based segmentation showed good intersubject reproducibility. The presented method may be a potential tool to investigate brainstem connectivity in diseases that distort normal anatomy, and quantitative analyses of the diffusion-related parameters may provide additional information on the involvement of brainstem pathways in certain disease states (e.g., traumatic brain injury, demyelinating disorders, brainstem tumors). The potential clinical applicability of the method is demonstrated in two cases of severe traumatic brain injury.
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Affiliation(s)
- Adrienn Máté
- Department of Neurosurgery, Albert Szent-Györgyi Clinical Center, University of Szeged, 6 Semmelweis Street, H-6725 Szeged, Hungary.
| | - Dávid Kis
- Department of Neurosurgery, Albert Szent-Györgyi Clinical Center, University of Szeged, 6 Semmelweis Street, H-6725 Szeged, Hungary
| | - Andrea Czigner
- Department of Anatomy, Histology and Embryology, Faculty of Medicine, University of Szeged, 40 Kossuth L. Boulevard, H-6724 Szeged, Hungary
| | - Tamás Fischer
- Department of Neurosurgery, Albert Szent-Györgyi Clinical Center, University of Szeged, 6 Semmelweis Street, H-6725 Szeged, Hungary
| | - László Halász
- National Institute of Clinical Neurosciences, 44-46 Laky Adolf Street, H-1145 Budapest, Hungary
| | - Pál Barzó
- Department of Neurosurgery, Albert Szent-Györgyi Clinical Center, University of Szeged, 6 Semmelweis Street, H-6725 Szeged, Hungary
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8
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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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9
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Ma L, Cai C, Yang H, Cai S, Qian J, Xiao L, Zhong K, Chen Z. Motion-tolerant diffusion mapping based on single-shot overlapping-echo detachment (OLED) planar imaging. Magn Reson Med 2017; 80:200-210. [DOI: 10.1002/mrm.27023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 10/31/2017] [Accepted: 11/01/2017] [Indexed: 12/20/2022]
Affiliation(s)
- Lingceng Ma
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance; Xiamen University; Xiamen China
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance; Xiamen University; Xiamen China
- Department of Communication Engineering; Xiamen University; Xiamen China
| | - Hongyi Yang
- High Magnet Field Laboratory, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences; Hefei China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance; Xiamen University; Xiamen China
| | - Junchao Qian
- High Magnet Field Laboratory, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences; Hefei China
| | - Lizhi Xiao
- State Key Laboratory of Petroleum Resources and Prospecting; China University of Petroleum; Beijing China
| | - Kai Zhong
- High Magnet Field Laboratory, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences; Hefei China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance; Xiamen University; Xiamen China
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10
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Dosenbach NUF, Koller JM, Earl EA, Miranda-Dominguez O, Klein RL, Van AN, Snyder AZ, Nagel BJ, Nigg JT, Nguyen AL, Wesevich V, Greene DJ, Fair DA. Real-time motion analytics during brain MRI improve data quality and reduce costs. Neuroimage 2017; 161:80-93. [PMID: 28803940 PMCID: PMC5731481 DOI: 10.1016/j.neuroimage.2017.08.025] [Citation(s) in RCA: 168] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 08/04/2017] [Accepted: 08/07/2017] [Indexed: 11/30/2022] Open
Abstract
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.
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Affiliation(s)
- Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA; Program in Occupational Therapy, Washington University, St. Louis, MO, USA.
| | - Jonathan M Koller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric A Earl
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Oscar Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Rachel L Klein
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Bonnie J Nagel
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Joel T Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Annie L Nguyen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Victoria Wesevich
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna J Greene
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA.
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11
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Heckova E, Považan M, Strasser B, Krumpolec P, Hnilicová P, Hangel GJ, Moser PA, Andronesi OC, van der Kouwe AJ, Valkovic P, Ukropcova B, Trattnig S, Bogner W. Real-time Correction of Motion and Imager Instability Artifacts during 3D γ-Aminobutyric Acid-edited MR Spectroscopic Imaging. Radiology 2017; 286:666-675. [PMID: 28957645 DOI: 10.1148/radiol.2017170744] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To compare the involuntary head motion, frequency and B0 shim changes, and effects on data quality during real-time-corrected three-dimensional γ-aminobutyric acid-edited magnetic resonance (MR) spectroscopic imaging in subjects with mild cognitive impairment (MCI), patients with Parkinson disease (PD), and young and older healthy volunteers. Materials and Methods In this prospective study, MR spectroscopic imaging datasets were acquired at 3 T after written informed consent was obtained. Translational and rotational head movement, frequency, and B0 shim were determined with an integrated volumetric navigator. Head motion patterns and imager instability were investigated in 33 young healthy control subjects (mean age ± standard deviation, 31 years ± 5), 34 older healthy control subjects (mean age, 67 years ± 8), 34 subjects with MCI (mean age, 72 years ± 5), and 44 patients with PD (mean age, 64 years ± 8). Spectral quality was assessed by means of region-of-interest analysis. Group differences were evaluated with Bonferroni-corrected Mann-Whitney tests. Results Three patients with PD and four subjects with MCI were excluded because of excessive head motion (ie, > 0.8 mm translation per repetition time of 1.6 seconds throughout >10 minutes). Older control subjects, patients with PD, and subjects with MCI demonstrated 1.5, 2, and 2.5 times stronger head movement, respectively, than did young control subjects (1.79 mm ± 0.77) (P < .001). Of young control subjects, older control subjects, patients with PD, and subjects with MCI, 6%, 35%, 38%, and 51%, respectively, moved more than 3 mm during the MR spectroscopic imaging acquisition of approximately 20 minutes. The predominant movements were head nodding and "sliding out" of the imager. Frequency changes were 1.1- and 1.4-fold higher in patients with PD (P = .007) and subjects with MCI (P < .001), respectively, and B0 shim changes were 1.3-, 1.5-, and 1.9-fold higher in older control subjects (P = .005), patients with PD (P < .001), and patients with MCI (P < .001), respectively, compared with those of young control subjects (12.59 Hz ± 2.49, 3.61 Hz · cm-1 ± 1.25). Real-time correction provided high spectral quality in all four groups (signal-to-noise ratio >15, Cramér-Rao lower bounds < 20%). Conclusion Real-time motion and B0 monitoring provides valuable information about motion patterns and B0 field variations in subjects with different predispositions for head movement. Immediate correction improves data quality, particularly in patients who have difficulty avoiding movement. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Eva Heckova
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Michal Považan
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Bernhard Strasser
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Patrik Krumpolec
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Petra Hnilicová
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Gilbert J Hangel
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Philipp A Moser
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Ovidiu C Andronesi
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Andre J van der Kouwe
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Peter Valkovic
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Barbara Ukropcova
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Siegfried Trattnig
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Wolfgang Bogner
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria (E.H., M.P., B.S., G.J.H., P.A.M., S.T., W.B.); Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria (M.P., S.T., W.B.); Division of Neurosciences, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia (P.H.); Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.C.A., A.J.v.d.K.); Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia (P.K., B.U.); and 2nd Department of Neurology (P.V.) and Institute of Pathological Physiology (B.U.), Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
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12
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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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13
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Alhamud A, Taylor PA, van der Kouwe AJW, Meintjes EM. Real-time measurement and correction of both B0 changes and subject motion in diffusion tensor imaging using a double volumetric navigated (DvNav) sequence. Neuroimage 2015; 126:60-71. [PMID: 26584865 DOI: 10.1016/j.neuroimage.2015.11.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 09/18/2015] [Accepted: 11/09/2015] [Indexed: 11/19/2022] Open
Abstract
Diffusion tensor imaging (DTI) requires a set of diffusion weighted measurements in order to acquire enough information to characterize local structure. The MRI scanner automatically performs a shimming process by acquiring a field map before the start of a DTI scan. Changes in B0, which can occur throughout the DTI acquisition due to several factors (including heating of the iron shim coils or subject motion), cause significant signal distortions that result in warped diffusion tensor (DT) parameter estimates. In this work we introduce a novel technique to simultaneously measure, report and correct in real time subject motion and changes in B0 field homogeneity, both in and through the imaging plane. This is achieved using double volumetric navigators (DvNav), i.e. a pair of 3D EPI acquisitions, interleaved with the DTI pulse sequence. Changes in the B0 field are evaluated in terms of zero-order (frequency) and first-order (linear gradients) shim. The ability of the DvNav to accurately estimate the shim parameters was first validated in a water phantom. Two healthy subjects were scanned both in the presence and absence of motion using standard, motion corrected (single navigator, vNav), and DvNav DTI sequences. The difference in performance between the proposed 3D EPI field maps and the standard 3D gradient echo field maps of the MRI scanner was also evaluated in a phantom and two healthy subjects. The DvNav sequence was shown to accurately measure and correct changes in B0 following manual adjustments of the scanner's central frequency and the linear shim gradients. Compared to other methods, the DvNav produced DTI results that showed greater spatial overlap with anatomical references, particularly in scans with subject motion. This is largely due to the ability of the DvNav system to correct shim changes and subject motion between each volume acquisition, thus reducing shear distortion.
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Affiliation(s)
- A Alhamud
- MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, South Africa.
| | - Paul A Taylor
- MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, South Africa; African Institute for Mathematical Sciences (AIMS), South Africa
| | - Andre J W van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Brookline, MA, USA
| | - Ernesta M Meintjes
- MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, South Africa
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14
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Kreilkamp BA, Zacà D, Papinutto N, Jovicich J. Retrospective head motion correction approaches for diffusion tensor imaging: Effects of preprocessing choices on biases and reproducibility of scalar diffusion metrics. J Magn Reson Imaging 2015; 43:99-106. [DOI: 10.1002/jmri.24965] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 05/20/2015] [Indexed: 11/07/2022] Open
Affiliation(s)
- Barbara A.K. Kreilkamp
- Center for Mind/Brain Sciences (CIMEC), University of Trento; Rovereto Italy
- Institute of Translational Medicine; University of Liverpool; Liverpool UK
| | - Domenico Zacà
- Center for Mind/Brain Sciences (CIMEC), University of Trento; Rovereto Italy
| | - Nico Papinutto
- Department of Neurology; University of California San Francisco; San Francisco California USA
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMEC), University of Trento; Rovereto Italy
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