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4 results on '"Gradient recalled echo"'

1. Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping

Author

Li, Jun, Li, Yufei, Gutierrez, Lorenzo, Xu, Wenying, Wu, Yiwen, Liu, Chunlei, Li, Dianyou, Sun, Bomin, Zhang, Chencheng, and Wei, Hongjiang

Subjects
Neurosciences, Brain Disorders, Biomedical Imaging, Bioengineering, deep brain stimulation, direct targeting, gradient recalled echo, quantitative susceptibility mapping, centromedian nucleus, Psychology, Cognitive Sciences, Experimental Psychology
Abstract

The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery.

Published
2020

2. Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping.

Author

Li, Jun, Li, Yufei, Gutierrez, Lorenzo, Xu, Wenying, Wu, Yiwen, Liu, Chunlei, Li, Dianyou, Sun, Bomin, Zhang, Chencheng, and Wei, Hongjiang

Subjects
centromedian nucleus, deep brain stimulation, direct targeting, gradient recalled echo, quantitative susceptibility mapping, Experimental Psychology, Neurosciences, Psychology, Cognitive Sciences
Abstract

The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery.

Published
2019

3. Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Author

Irimia, Andrei, Wang, Bo, Aylward, Stephen R, Prastawa, Marcel W, Pace, Danielle F, Gerig, Guido, Hovda, David A, Kikinis, Ron, Vespa, Paul M, and Van Horn, John D

Subjects
Pediatric, Traumatic Head and Spine Injury, Clinical Research, Neurosciences, Brain Disorders, Traumatic Brain Injury (TBI), Biomedical Imaging, Childhood Injury, Physical Injury - Accidents and Adverse Effects, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Trauma, Neuroimaging, MRI/fMRI, Diffusion tensor, Outcome measures, 3D, three-dimensional, AAL, Automatic Anatomical Labeling, ADC, apparent diffusion coefficient, ANTS, Advanced Normalization ToolS, BOLD, blood oxygen level dependent, CC, corpus callosum, CT, computed tomography, DAI, diffuse axonal injury, DSI, diffusion spectrum imaging, DTI, diffusion tensor imaging, DWI, diffusion weighted imaging, FA, fractional anisotropy, FLAIR, Fluid Attenuated Inversion Recovery, FSE, Functional Status Examination, GCS, Glasgow Coma Score, GM, gray matter, GOS, Glasgow Outcome Score, GRE, Gradient Recalled Echo, HARDI, high-angular-resolution diffusion imaging, IBA, Individual Brain Atlas, LDA, linear discriminant analysis, MRI, magnetic resonance imaging, NINDS, National Institute of Neurological Disorders and Stroke, PCA, principal component analysis, PROMO, PROspective MOtion Correction, SPM, Statistical Parametric Mapping, SWI, Susceptibility Weighted Imaging, TBI, traumatic brain injury, TBSS, tract-based spatial statistics, WM, white matter, fMRI, functional magnetic resonance imaging
Abstract

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Published
2012

4. Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Author

Irimia, Andrei, Wang, Bo, Aylward, Stephen R, Prastawa, Marcel W, Pace, Danielle F, Gerig, Guido, Hovda, David A, Kikinis, Ron, Vespa, Paul M, and Van Horn, John D

Subjects
3D, three-dimensional, AAL, Automatic Anatomical Labeling, ADC, apparent diffusion coefficient, ANTS, Advanced Normalization ToolS, BOLD, blood oxygen level dependent, CC, corpus callosum, CT, computed tomography, DAI, diffuse axonal injury, DSI, diffusion spectrum imaging, DTI, diffusion tensor imaging, DWI, diffusion weighted imaging, Diffusion tensor, FA, fractional anisotropy, FLAIR, Fluid Attenuated Inversion Recovery, FSE, Functional Status Examination, GCS, Glasgow Coma Score, GM, gray matter, GOS, Glasgow Outcome Score, GRE, Gradient Recalled Echo, HARDI, high-angular-resolution diffusion imaging, IBA, Individual Brain Atlas, LDA, linear discriminant analysis, MRI, magnetic resonance imaging, MRI/fMRI, NINDS, National Institute of Neurological Disorders and Stroke, Neuroimaging, Outcome measures, PCA, principal component analysis, PROMO, PROspective MOtion Correction, SPM, Statistical Parametric Mapping, SWI, Susceptibility Weighted Imaging, TBI, traumatic brain injury, TBSS, tract-based spatial statistics, Trauma, WM, white matter, fMRI, functional magnetic resonance imaging, 3D, three-dimensional, AAL, Automatic Anatomical Labeling, ADC, apparent diffusion coefficient, ANTS, Advanced Normalization ToolS, BOLD, blood oxygen level dependent, CC, corpus callosum, CT, computed tomography, DAI, diffuse axonal injury, DSI, diffusion spectrum imaging, DTI, diffusion tensor imaging, DWI, diffusion weighted imaging, FA, fractional anisotropy, FLAIR, Fluid Attenuated Inversion Recovery, FSE, Functional Status Examination, GCS, Glasgow Coma Score, GM, gray matter, GOS, Glasgow Outcome Score, GRE, Gradient Recalled Echo, HARDI, high-angular-resolution diffusion imaging, IBA, Individual Brain Atlas, LDA, linear discriminant analysis, MRI, magnetic resonance imaging, NINDS, National Institute of Neurological Disorders and Stroke, PCA, principal component analysis, PROMO, PROspective MOtion Correction, SPM, Statistical Parametric Mapping, SWI, Susceptibility Weighted Imaging, TBI, traumatic brain injury, TBSS, tract-based spatial statistics, WM, white matter, fMRI, functional magnetic resonance imaging, Neurosciences
Abstract

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Published
2012

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