- Record: found
- Abstract: found
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Freewater estimatoR using iNtErpolated iniTialization (FERNET): Characterizing peritumoral edema using clinically feasible diffusion MRI data
research-article
Author(s):
Drew Parker
1
,
2
,
3 ,
Abdol Aziz Ould Ismail
1
,
2
,
3 ,
Ronald Wolf
3 ,
Steven Brem
4 ,
Simon Alexander
5 ,
Wes Hodges
5 ,
Ofer Pasternak
6 ,
Emmanuel Caruyer
7 ,
Ragini Verma
1
,
2
,
3
,
4
,
*
Publication date (Electronic):
29 May 2020
Journal:
PLoS ONE
Publisher:
Public Library of Science
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Abstract
Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.
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Most cited references23
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- Abstract: found
- Article: not found
Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants.
Kenichi Oishi, Andreia Faria, Hangyi Jiang … (2009)
The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.
- Record: found
- Abstract: found
- Article: not found
Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors.
The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.
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Limits to anatomical accuracy of diffusion tractography using modern approaches
Kurt Schilling, Vishwesh Nath, Colin Hansen … (2019)
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography’s inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
Author and article information
Contributors
Drew Parker: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole:
MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing
– review & editing
Abdol Aziz Ould Ismail: Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original
draft
Ronald Wolf: Role: Writing – review & editing
Steven Brem: Role: Writing – review & editing
Simon Alexander: Role: ConceptualizationRole: Writing – review & editing
Wes Hodges: Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
Ofer Pasternak: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review
& editing
Emmanuel Caruyer: Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – review
& editing
Ragini Verma: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole:
ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
Pew-Thian Yap: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, CA USA
)
ISSN
(Electronic):
1932-6203
Publication date
(Electronic):
29
May
2020
Publication date Collection: 2020
Volume: 15
Issue: 5
Electronic Location Identifier: e0233645
Affiliations
[1
]
DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of
America
[2
]
Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA, United States of America
[3
]
Department of Radiology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, United States of America
[4
]
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, United States of America
Author notes
Article
Publisher ID: PONE-D-19-22990DOI: 10.1371/journal.pone.0233645
PMC ID: 7259683
PubMed ID: 32469944
SO-VID: 4037547f-586d-49a9-9ae4-cc5362b99747
Copyright © © 2020 Parker et al
License:
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 21
August
2019
Date
accepted
: 10
May
2020
Page count
Figures: 6,
Tables: 0,
Pages: 19
Funding
Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
Award ID: R01NS096606
Award Recipient
:
Ragini Verma
Funded by: Synaptive Medical Inc.
Award ID: 30071788
Award Recipient
:
Drew Parker
Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
Award ID: R01MH108574
Award Recipient
:
Ofer Pasternak
This research was supported by National Institutes of Health (NIH) grants R01NS096606
(PI: Ragini Verma) and R01MH108574 (PI: Ofer Pasternak), and research grant from Synaptive
Medical 30071788 (PI: Ragini Verma). Synaptive Medical provided support in the form
of percent effort towards salaries for authors [RV, DP, AAOI]. They were involved
in design of experiments, planning analysis, and application to brain tumor data.
They contributed to writing of the manuscript.
Categories
Subject:
Research Article
Subject:
Medicine and Health Sciences
Subject:
Diagnostic Medicine
Subject:
Signs and Symptoms
Subject:
Edema
Subject:
Medicine and Health Sciences
Subject:
Pathology and Laboratory Medicine
Subject:
Signs and Symptoms
Subject:
Edema
Subject:
Biology and Life Sciences
Subject:
Neuroscience
Subject:
Brain Mapping
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Tractography
Subject:
Medicine and Health Sciences
Subject:
Diagnostic Medicine
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Tractography
Subject:
Research and Analysis Methods
Subject:
Imaging Techniques
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Tractography
Subject:
Medicine and Health Sciences
Subject:
Radiology and Imaging
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Tractography
Subject:
Research and Analysis Methods
Subject:
Imaging Techniques
Subject:
Neuroimaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Tractography
Subject:
Biology and Life Sciences
Subject:
Neuroscience
Subject:
Neuroimaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Tractography
Subject:
Medicine and Health Sciences
Subject:
Oncology
Subject:
Cancers and Neoplasms
Subject:
Metastatic Tumors
Subject:
Research and Analysis Methods
Subject:
Simulation and Modeling
Subject:
Biology and Life Sciences
Subject:
Neuroscience
Subject:
Brain Mapping
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Medicine and Health Sciences
Subject:
Diagnostic Medicine
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Research and Analysis Methods
Subject:
Imaging Techniques
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Medicine and Health Sciences
Subject:
Radiology and Imaging
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Research and Analysis Methods
Subject:
Imaging Techniques
Subject:
Neuroimaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Biology and Life Sciences
Subject:
Neuroscience
Subject:
Neuroimaging
Subject:
Brain Morphometry
Subject:
Diffusion Tensor Imaging
Subject:
Biology and Life Sciences
Subject:
Anatomy
Subject:
Body Fluids
Subject:
Cerebrospinal Fluid
Subject:
Medicine and Health Sciences
Subject:
Anatomy
Subject:
Body Fluids
Subject:
Cerebrospinal Fluid
Subject:
Biology and Life Sciences
Subject:
Physiology
Subject:
Body Fluids
Subject:
Cerebrospinal Fluid
Subject:
Medicine and Health Sciences
Subject:
Physiology
Subject:
Body Fluids
Subject:
Cerebrospinal Fluid
Subject:
Biology and Life Sciences
Subject:
Anatomy
Subject:
Nervous System
Subject:
Cerebrospinal Fluid
Subject:
Medicine and Health Sciences
Subject:
Anatomy
Subject:
Nervous System
Subject:
Cerebrospinal Fluid
Subject:
Biology and Life Sciences
Subject:
Neuroscience
Subject:
Brain Mapping
Subject:
Brain Morphometry
Subject:
Diffusion Magnetic Resonance Imaging
Subject:
Medicine and Health Sciences
Subject:
Diagnostic Medicine
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Magnetic Resonance Imaging
Subject:
Research and Analysis Methods
Subject:
Imaging Techniques
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Magnetic Resonance Imaging
Subject:
Medicine and Health Sciences
Subject:
Radiology and Imaging
Subject:
Diagnostic Radiology
Subject:
Magnetic Resonance Imaging
Subject:
Brain Morphometry
Subject:
Diffusion Magnetic Resonance Imaging
Subject:
Research and Analysis Methods
Subject:
Imaging Techniques
Subject:
Neuroimaging
Subject:
Brain Morphometry
Subject:
Diffusion Magnetic Resonance Imaging
Subject:
Biology and Life Sciences
Subject:
Neuroscience
Subject:
Neuroimaging
Subject:
Brain Morphometry
Subject:
Diffusion Magnetic Resonance Imaging
Subject:
Computer and Information Sciences
Subject:
Data Acquisition
Custom metadata
Data Availability We have uploaded the data set necessary to replicate our study findings. The healthy
controls used in the paper are part of the Philadelphia Neurodevelopmental Cohort
dataset which is available at
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2. The healthy controls from Dataset 1 and simulated data can be found at
https://github.com/DiCIPHRLab/ Fernet/tree/paper_data. The brain tumor data in the paper is restricted. It requires
an agreement with Penn, and can only be released once the said had been executed.
The contact person for that isRagini Verma,
ragini@
123456pennmedicine.upenn.edu
.
ScienceOpen disciplines: Uncategorized
Data availability:
ScienceOpen disciplines: Uncategorized