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Cieslak M, Cook PA, Shafiei G, Tapera TM, Radhakrishnan H, Elliott M, Roalf DR, Oathes DJ, Bassett DS, Tisdall MD, Rokem A, Grafton ST, Satterthwaite TD. Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme. Hum Brain Mapp 2024; 45:e26570. [PMID: 38339908 PMCID: PMC10826632 DOI: 10.1002/hbm.26570] [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: 09/19/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 02/12/2024] Open
Abstract
Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.
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Affiliation(s)
- Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Philip A. Cook
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Tinashe M. Tapera
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Mark Elliott
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Desmond J. Oathes
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Dani S. Bassett
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of BioengineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Physics and AstronomyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Electrical and Systems EngineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Sante Fe InstituteSanta FeNew MexicoUnited States
| | - M. Dylan Tisdall
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Ariel Rokem
- Department of Psychology and the eScience InstituteUniversity of WashingtonSeattleWashingtonUnited States
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of California Santa BarbaraSanta BarbaraCaliforniaUnited States
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Penn‐CHOP Lifespan Brain InstitutePhiladelphiaPennsylvaniaUnited States
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2
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DiPiero M, Rodrigues PG, Gromala A, Dean DC. Applications of advanced diffusion MRI in early brain development: a comprehensive review. Brain Struct Funct 2023; 228:367-392. [PMID: 36585970 PMCID: PMC9974794 DOI: 10.1007/s00429-022-02605-8] [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: 09/02/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023]
Abstract
Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.
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Affiliation(s)
- Marissa DiPiero
- Department of Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Alyssa Gromala
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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3
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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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4
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Zhao G, Lau WKW, Wang C, Yan H, Zhang C, Lin K, Qiu S, Huang R, Zhang R. A Comparative Multimodal Meta-analysis of Anisotropy and Volume Abnormalities in White Matter in People Suffering From Bipolar Disorder or Schizophrenia. Schizophr Bull 2021; 48:69-79. [PMID: 34374427 PMCID: PMC8781378 DOI: 10.1093/schbul/sbab093] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) share some similarities in terms of genetic-risk genes and abnormalities of gray-matter structure in the brain, but white matter (WM) abnormalities have not been studied in depth. We undertook a comparative multimodal meta-analysis to identify common and disorder-specific abnormalities in WM structure between SZ and BD. Anisotropic effect size-signed differential mapping software was used to conduct a comparative meta-analysis of 68 diffusion tensor imaging (DTI) and 34 voxel-based morphometry (VBM) studies comparing fractional anisotropy (FA) and white matter volume (WMV), respectively, between patients with SZ (DTI: N = 1543; VBM: N = 1068) and BD (DTI: N = 983; VBM: N = 518) and healthy controls (HCs). The bilateral corpus callosum (extending to the anterior and superior corona radiata) showed shared decreased WMV and FA in SZ and BD. Compared with BD patients, SZ patients showed remarkable disorder-specific WM abnormalities: decreased FA and increased WMV in the left cingulum, and increased FA plus decreased WMV in the right anterior limb of the internal capsule. SZ patients showed more extensive alterations in WM than BD cases, which may be the pathophysiological basis for the clinical continuity of both disorders. The disorder-specific regions in the left cingulum and right anterior limb of the internal capsule provided novel insights into both disorders. Our study adds value to further understanding of the pathophysiology, classification, and differential diagnosis of SZ and BD.
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Affiliation(s)
- Guorui Zhao
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Way K W Lau
- Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, China
| | - Chanyu Wang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Haifeng Yan
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chichen Zhang
- School of Management, Southern Medical University, Guangzhou, China
| | - Kangguang Lin
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Chinese traditional Medical University, Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China,To whom correspondence should be addressed; Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, tel/fax:020-62789234, e-mail:
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5
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Muller J, Alizadeh M, Li L, Thalheimer S, Matias C, Tantawi M, Miao J, Silverman M, Zhang V, Yun G, Romo V, Mohamed FB, Wu C. Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator. NEUROIMAGE-CLINICAL 2019; 25:102135. [PMID: 31901789 PMCID: PMC6948366 DOI: 10.1016/j.nicl.2019.102135] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/27/2019] [Accepted: 12/13/2019] [Indexed: 01/03/2023]
Abstract
Deep brain stimulation (DBS) for Parkinson's disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS.
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Affiliation(s)
- J Muller
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States.
| | - M Alizadeh
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - L Li
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - S Thalheimer
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - C Matias
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - M Tantawi
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - J Miao
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - M Silverman
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - V Zhang
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - G Yun
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - V Romo
- Department of Anesthesiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - F B Mohamed
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - C Wu
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
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6
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Kreilkamp BAK, Lisanti L, Glenn GR, Wieshmann UC, Das K, Marson AG, Keller SS. Comparison of manual and automated fiber quantification tractography in patients with temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2019; 24:102024. [PMID: 31670154 PMCID: PMC6831895 DOI: 10.1016/j.nicl.2019.102024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 09/05/2019] [Accepted: 09/27/2019] [Indexed: 11/25/2022]
Abstract
Tractography approaches showed moderate to good agreement for tract morphology. Along- and whole-tract diffusivity was significantly correlated across approaches. Whole-tract AFQ but not manual tract diffusivity correlated with clinical variables. Absence of excellent agreement between approaches warrants caution.
Objective To investigate the agreement between manually and automatically generated tracts from diffusion tensor imaging (DTI) in patients with temporal lobe epilepsy (TLE). Whole and along-the-tract diffusivity metrics and correlations with patient clinical characteristics were analyzed with respect to tractography approach. Methods We recruited 40 healthy controls and 24 patients with TLE who underwent conventional T1-weighted imaging and 60-direction DTI. An automated (Automated Fiber Quantification, AFQ) and manual (TrackVis) deterministic tractography approach was used to identify the uncinate fasciculus (UF) and parahippocampal white matter bundle (PHWM). Tract diffusion scalar metrics were analyzed with respect to agreement across automated and manual approaches (Dice Coefficient and Spearman correlations), to side of onset of epilepsy and patient clinical characteristics, including duration of epilepsy, age of onset and presence of hippocampal sclerosis. Results Across approaches the analysis of tract morphology similarity revealed Dice coefficients at moderate to good agreement (0.54 - 0.6) and significant correlations between diffusion values (Spearman's Rho=0.4–0.9). However, within bilateral PHWM, AFQ yielded significantly lower FA (left: Z = 4.4, p<0.001; right: Z = 5.1, p<0.001) and higher MD values (left: Z=-4.7, p<0.001; right: Z=-3.7, p<0.001) compared to the manual approach. Whole tract DTI metrics determined using AFQ were significantly correlated with patient characteristics, including age of epilepsy onset in FA (R = 0.6, p = 0.02) and MD of the ipsilateral PHWM (R=-0.6, p = 0.02), while duration of epilepsy corrected for age correlated with MD in ipsilateral PHWM (R = 0.7, p<0.01). Correlations between clinical metrics and diffusion values extracted using the manual whole tract technique did not survive correction for multiple comparisons. Both manual and automated along-the-tract analyses demonstrated significant correlations with patient clinical characteristics such as age of onset and epilepsy duration. The strongest and most widespread localized ipsi- and contralateral diffusivity alterations were observed in patients with left TLE and patients with HS compared to controls, while patients with right TLE and patients without HS did not show these strong effects. Conclusions Manual and AFQ tractography approaches revealed significant correlations in the reconstruction of tract morphology and extracted whole and along-tract diffusivity values. However, as non-identical methods they differed in the respective yield of significant results across clinical correlations and group-wise statistics. Given the absence of excellent agreement between manual and AFQ techniques as demonstrated in the present study, caution should be considered when using AFQ particularly when used without reference to benchmark manual measures.
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Affiliation(s)
- Barbara A K Kreilkamp
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; Department of Neurology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom.
| | - Lucy Lisanti
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; Royal Society, London, United Kingdom
| | - G Russell Glenn
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States
| | - Udo C Wieshmann
- Department of Neurology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Kumar Das
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Anthony G Marson
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; Department of Neurology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; Department of Neurology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
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7
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Lee DH, Lee DW, Henry D, Park HJ, Han BS, Woo DC. Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging. Eur Radiol 2018; 28:4314-4323. [PMID: 29651768 DOI: 10.1007/s00330-018-5382-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 01/30/2018] [Accepted: 02/12/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the effects of signal intensity differences between the b0 image and diffusion tensor imaging (DTI) in the image registration process. METHODS To correct signal intensity differences between the b0 image and DTI data, a simple image intensity compensation (SIMIC) method, which is a b0 image re-calculation process from DTI data, was applied before the image registration. The re-calculated b0 image (b0ext) from each diffusion direction was registered to the b0 image acquired through the MR scanning (b0nd) with two types of cost functions and their transformation matrices were acquired. These transformation matrices were then used to register the DTI data. For quantifications, the dice similarity coefficient (DSC) values, diffusion scalar matrix, and quantified fibre numbers and lengths were calculated. RESULTS The combined SIMIC method with two cost functions showed the highest DSC value (0.802 ± 0.007). Regarding diffusion scalar values and numbers and lengths of fibres from the corpus callosum, superior longitudinal fasciculus, and cortico-spinal tract, only using normalised cross correlation (NCC) showed a specific tendency toward lower values in the brain regions. CONCLUSION Image-based distortion correction with SIMIC for DTI data would help in image analysis by accounting for signal intensity differences as one additional option for DTI analysis. KEY POINTS • We evaluated the effects of signal intensity differences at DTI registration. • The non-diffusion-weighted image re-calculation process from DTI data was applied. • SIMIC can minimise the signal intensity differences at DTI registration.
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Affiliation(s)
- Dong-Hoon Lee
- Faculty of Health Sciences and Brain & Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Do-Wan Lee
- Center for Bioimaging of New Drug Development, and MR Core Lab., Asan Institute for Life Sciences, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea
| | - David Henry
- Faculty of Health Sciences and Brain & Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Hae-Jin Park
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Gyeonggi, Republic of Korea
| | - Bong-Soo Han
- Department of Radiological Science, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 220-710, Republic of Korea.
| | - Dong-Cheol Woo
- Center for Bioimaging of New Drug Development, and MR Core Lab., Asan Institute for Life Sciences, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea. .,Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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8
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Marami B, Mohseni Salehi SS, Afacan O, Scherrer B, Rollins CK, Yang E, Estroff JA, Warfield SK, Gholipour A. Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. Neuroimage 2017; 156:475-488. [PMID: 28433624 DOI: 10.1016/j.neuroimage.2017.04.033] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 04/14/2017] [Indexed: 01/29/2023] Open
Abstract
Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in-vivo diffusion-tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration-based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra-slice motion and reconstruct DTI from motion-corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in-utero at 22-38 weeks gestation. Significantly higher fractional anisotropy values in fiber-rich regions, and the analysis of whole-brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice-level motion correction and robust reconstruction is necessary for reliable in-vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small-worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.
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Affiliation(s)
- Bahram Marami
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Seyed Sadegh Mohseni Salehi
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Department of Electrical Engineering, Northeastern University, Boston, MA, USA
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Edward Yang
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Judy A Estroff
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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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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Pohl KM, Sullivan EV, Rohlfing T, Chu W, Kwon D, Nichols BN, Zhang Y, Brown SA, Tapert SF, Cummins K, Thompson WK, Brumback T, Colrain IM, Baker FC, Prouty D, De Bellis MD, Voyvodic JT, Clark DB, Schirda C, Nagel BJ, Pfefferbaum A. Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study. Neuroimage 2016; 130:194-213. [PMID: 26872408 DOI: 10.1016/j.neuroimage.2016.01.061] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Revised: 01/23/2016] [Accepted: 01/28/2016] [Indexed: 01/18/2023] Open
Abstract
Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site's MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys were higher than those of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.
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Affiliation(s)
- Kilian M Pohl
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.
| | - Torsten Rohlfing
- Center for Health Sciences, SRI International, Menlo Park, CA, United States
| | - Weiwei Chu
- Center for Health Sciences, SRI International, Menlo Park, CA, United States
| | - Dongjin Kwon
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - B Nolan Nichols
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Yong Zhang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Sandra A Brown
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Susan F Tapert
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States; Veterans Affairs San Diego Healthcare System, La Jolla, CA, United States
| | - Kevin Cummins
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Wesley K Thompson
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Ty Brumback
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Ian M Colrain
- Center for Health Sciences, SRI International, Menlo Park, CA, United States
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, United States
| | - Devin Prouty
- Center for Health Sciences, SRI International, Menlo Park, CA, United States
| | - Michael D De Bellis
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - James T Voyvodic
- Department of Radiology, Duke University School of Medicine, Durham, NC, United States
| | - Duncan B Clark
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Claudiu Schirda
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Bonnie J Nagel
- Departments of Psychiatry and Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States
| | - Adolf Pfefferbaum
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
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