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Taylor EN, Huang N, Wisco J, Wang Y, Morgan KG, Hamilton JA. The brains of aged mice are characterized by altered tissue diffusion properties and cerebral microbleeds. J Transl Med 2020; 18:277. [PMID: 32641073 PMCID: PMC7346388 DOI: 10.1186/s12967-020-02441-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 06/29/2020] [Indexed: 12/29/2022] Open
Abstract
Background Brain aging is a major risk factor in the progression of cognitive diseases including Alzheimer’s disease (AD) and vascular dementia. We investigated a mouse model of brain aging up to 24 months old (mo). Methods A high field (11.7T) MRI protocol was developed to characterize specific features of brain aging including the presence of cerebral microbleeds (CMBs), morphology of grey and white matter, and tissue diffusion properties. Mice were selected from age categories of either young (3 mo), middle-aged (18 mo), or old (24 mo) and fed normal chow over the duration of the study. Mice were imaged in vivo with multimodal MRI, including conventional T2-weighted (T2W) and T2*-weighted (T2*W) imaging, followed by ex vivo diffusion-weighted imaging (DWI) and T2*W MR-microscopy to enhance the detection of microstructural features. Results Structural changes observed in the mouse brain with aging included reduced cortical grey matter volume and enlargement of the brain ventricles. A remarkable age-related change in the brains was the development of CMBs found starting at 18 mo and increasing in total volume at 24 mo, primarily in the thalamus. CMBs presence was confirmed with high resolution ex vivo MRI and histology. DWI detected further brain tissue changes in the aged mice including reduced fractional anisotropy, increased radial diffusion, increased mean diffusion, and changes in the white matter fibers visualized by color-coded tractography, including around a large cortical CMB. Conclusions The mouse is a valuable model of age-related vascular contributions to cognitive impairment and dementia (VCID). In composite, these methods and results reveal brain aging in older mice as a multifactorial process including CMBs and tissue diffusion alterations that can be well characterized by high field MRI.
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Affiliation(s)
- Erik N Taylor
- Department of Radiology, University of New Mexico, Albuquerque, NM, USA. .,Department of Physiology & Biophysics, Boston University School of Medicine, Boston, MA, USA. .,Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Nasi Huang
- Department of Physiology & Biophysics, Boston University School of Medicine, Boston, MA, USA.,Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jonathan Wisco
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Yandan Wang
- Department of Health Sciences, Boston University, Boston, MA, USA
| | | | - James A Hamilton
- Department of Physiology & Biophysics, Boston University School of Medicine, Boston, MA, USA. .,Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI. Sci Rep 2020; 10:8063. [PMID: 32415137 PMCID: PMC7228927 DOI: 10.1038/s41598-020-64912-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 04/24/2020] [Indexed: 11/16/2022] Open
Abstract
Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.
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Wang Q, Pérez-Carrillo GJG, Ponisio MR, LaMontagne P, Dahiya S, Marcus DS, Milchenko M, Shimony J, Liu J, Chen G, Salter A, Massoumzadeh P, Miller-Thomas MM, Rich KM, McConathy J, Benzinger TLS, Wang Y. Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation. PLoS One 2019; 14:e0225093. [PMID: 31725772 PMCID: PMC6855653 DOI: 10.1371/journal.pone.0225093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/29/2019] [Indexed: 12/05/2022] Open
Abstract
Objectives Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan. Methods Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction. Results The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV. Conclusions Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.
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Affiliation(s)
- Qing Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | | | - Maria Rosana Ponisio
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Sonika Dahiya
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Daniel S. Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Mikhail Milchenko
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Joshua Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jingxia Liu
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Gengsheng Chen
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Amber Salter
- Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Parinaz Massoumzadeh
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Michelle M. Miller-Thomas
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Keith M. Rich
- Department of Neurosurgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jonathan McConathy
- Department of Radiology, Division of Molecular Imaging and Therapeutics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Yong Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri, United States of America
- * E-mail:
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Whole-tumor histogram analysis of DWI and QSI for differentiating between meningioma and schwannoma: a pilot study. Jpn J Radiol 2019; 37:694-700. [DOI: 10.1007/s11604-019-00862-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/30/2019] [Indexed: 02/06/2023]
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Henker C, Hiepel MC, Kriesen T, Scherer M, Glass Ä, Herold-Mende C, Bendszus M, Langner S, Weber MA, Schneider B, Unterberg A, Piek J. Volumetric assessment of glioblastoma and its predictive value for survival. Acta Neurochir (Wien) 2019; 161:1723-1732. [PMID: 31254065 DOI: 10.1007/s00701-019-03966-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 05/29/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND The objective of this study was to evaluate the morphology of glioblastoma on structural pretreatment magnetic resonance imaging (MRI), defining imaging prognostic factors. METHOD We conducted a retrospective analysis of MR images from 114 patients harboring a primary glioblastoma, derived from two neurosurgical departments. Tumor segmentation was carried out in a semi-automated fashion. Tumor compartments comprised contrast-enhancing volume (CEV+), perifocal hyperintensity on fluid-attenuated inversion recovery (FLAIR) images (FLAIR+) excluding CEV+, and a non-enhancing area within the CEV+ lesion (CEV-). Additionally, two ratios were calculated from these volumes, the edema-tumor ratio (ETR) and necrosis-tumor ratio (NTR). All patients received surgical resection, followed by concomitant radiation and chemotherapy. RESULTS Tumor segmentation revealed the strongest correlation between the CEV+ volume and the CEV-, presenting intratumoral necrosis (p < 0.001). The relation between the tumor surrounding the FLAIR+ area and the CEV+ volume and the ETR is inversely correlated (p = 0.001). The most important prognostic factor in multivariable analysis was NTR (HR 2.63, p = 0.016). The cut-off value in our cohort for NTR was 0.33, equivalent to a decrease in survival if the necrotic core of the tumor (CEV-) accounts for more than 33% of the tumor mass itself (CEV+). CONCLUSIONS Our data emphasizes the importance of the necrosis-tumor ratio as a biomarker in glioblastoma imaging, rather than single tumor compartment volumes. NTR can help to identify a subset of tumors with a higher resistance to therapy and a dismal prognosis.
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Affiliation(s)
- Christian Henker
- Department of Neurosurgery, University Medicine of Rostock, Schillingallee 35, 18055, Rostock, Germany.
| | - Marie Cristin Hiepel
- Department of Neurosurgery, University Medicine of Rostock, Schillingallee 35, 18055, Rostock, Germany
| | - Thomas Kriesen
- Department of Neurosurgery, University Medicine of Rostock, Schillingallee 35, 18055, Rostock, Germany
| | - Moritz Scherer
- Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Änne Glass
- Institute for Biostatistics and Informatics in Medicine, University Medicine of Rostock, Rostock, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Sönke Langner
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medicine of Rostock, Rostock, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medicine of Rostock, Rostock, Germany
| | - Björn Schneider
- Institute for Pathology, University Medicine of Rostock, Rostock, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Piek
- Department of Neurosurgery, University Medicine of Rostock, Schillingallee 35, 18055, Rostock, Germany
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Tian Q, Yang G, Leuze C, Rokem A, Edlow BL, McNab JA. Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator. Neuroimage 2019; 189:497-515. [PMID: 30684636 DOI: 10.1016/j.neuroimage.2019.01.038] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Revised: 12/06/2018] [Accepted: 01/14/2019] [Indexed: 01/14/2023] Open
Abstract
Diffusion spectrum MRI (DSI) provides model-free estimation of the diffusion ensemble average propagator (EAP) and orientation distribution function (ODF) but requires the diffusion data to be acquired on a Cartesian q-space grid. Multi-shell diffusion acquisitions are more flexible and more commonly acquired but have, thus far, only been compatible with model-based analysis methods. Here, we propose a generalized DSI (GDSI) framework to recover the EAP from multi-shell diffusion MRI data. The proposed GDSI approach corrects for q-space sampling density non-uniformity using a fast geometrical approach. The EAP is directly calculated in a preferable coordinate system by multiplying the sampling density corrected q-space signals by a discrete Fourier transform matrix, without any need for gridding. The EAP is demonstrated as a way to map diffusion patterns in brain regions such as the thalamus, cortex and brainstem where the tissue microstructure is not as well characterized as in white matter. Scalar metrics such as the zero displacement probability and displacement distances at different fractions of the zero displacement probability were computed from the recovered EAP to characterize the diffusion pattern within each voxel. The probability averaged across directions at a specific displacement distance provides a diffusion property based image contrast that clearly differentiates tissue types. The displacement distance at the first zero crossing of the EAP averaged across directions orthogonal to the primary fiber orientation in the corpus callosum is found to be larger in the body (5.65 ± 0.09 μm) than in the genu (5.55 ± 0.15 μm) and splenium (5.4 ± 0.15 μm) of the corpus callosum, which corresponds well to prior histological studies. The EAP also provides model-free representations of angular structure such as the diffusion ODF, which allows estimation and comparison of fiber orientations from both the model-free and model-based methods on the same multi-shell data. For the model-free methods, detection of crossing fibers is found to be strongly dependent on the maximum b-value and less sensitive compared to the model-based methods. In conclusion, our study provides a generalized DSI approach that allows flexible reconstruction of the diffusion EAP and ODF from multi-shell diffusion data and data acquired with other sampling patterns.
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Affiliation(s)
- Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States.
| | - Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
| | - Christoph Leuze
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Jennifer A McNab
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
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