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      Is Open Access

      ADC, D, f dataset calculated through the simplified IVIM model, with MGMT promoter methylation, age, and ECOG, in 38 patients with wildtype IDH glioblastoma

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          Abstract

          Patients undergoing standard chemoradiation post-resection had MRIs at radiation planning and fractions 10 and 20 of chemoradiation. MRIs were 1.5T and 3D T2-FLAIR, pre- and post-contrast 3D T1-weighted (T1) and echo planar DWI with three b-values (0, 500, and 1000s/mm 2) were acquired. T2-FLAIR was coregistered to T1C images. Non-overlapping T1 contrast-enhancing (T1C) and nonenhancing T2-FLAIR hyperintense regions were segmented, with necrotic/cystic regions, the surgical cavity, and large vessels excluded. The simplified IVIM model was used to calculate voxelwise diffusion coefficient ( D) and perfusion fraction ( f) maps; ADC was calculated using the natural logarithm of b = 1000 over b = 0 images. T1C and T2-FLAIR segmentations were brought into this space, and medians calculated. MGMT promoter methylation status (MGMT PMS), age at diagnosis, and Eastern Cooperative Oncology Group (ECOG) performance status were extracted from electronic medical records. The data were presented, analyzed, and described in the article, “Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype Glioblastoma”, published in Radiotherapy and Oncology [1].

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          elastix: a toolbox for intensity-based medical image registration.

          Stefan Klein, Marius Staring, Keelin Murphy … (2010)
          Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.
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            Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging.

            D Le Bihan, E Breton, D Lallemand … (1988)
            Intravoxel incoherent motion (IVIM) imaging is a method the authors developed to visualize microscopic motions of water. In biologic tissues, these motions include molecular diffusion and microcirculation of blood in the capillary network. IVIM images are quantified by an apparent diffusion coefficient (ADC), which integrates the effects of both diffusion and perfusion. The aim of this work was to demonstrate how much perfusion contributes to the ADC and to present a method for obtaining separate images of diffusion and perfusion. Images were obtained at 0.5 T with high-resolution multisection sequences and without the use of contrast material. Results in a phantom made of resin microspheres demonstrated the ability of the method to separately evaluate diffusion and perfusion. The method was then applied in patients with brain and bone tumors and brain ischemia. Clinical results showed significant promise of the method for tissue characterization by perfusion patterns and for functional studies in the evaluation of the microcirculation in physiologic and pathologic conditions, as, for instance, in brain ischemia.
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              Quantitative measurement of brain perfusion with intravoxel incoherent motion MR imaging.

              Patrick Browaeys, Philippe Maeder, Christian Federau … (2012)
              To evaluate the sensitivity of the perfusion parameters derived from Intravoxel Incoherent Motion (IVIM) MR imaging to hypercapnia-induced vasodilatation and hyperoxygenation-induced vasoconstriction in the human brain. This study was approved by the local ethics committee and informed consent was obtained from all participants. Images were acquired with a standard pulsed-gradient spin-echo sequence (Stejskal-Tanner) in a clinical 3-T system by using 16 b values ranging from 0 to 900 sec/mm(2). Seven healthy volunteers were examined while they inhaled four different gas mixtures known to modify brain perfusion (pure oxygen, ambient air, 5% CO(2) in ambient air, and 8% CO(2) in ambient air). Diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and blood flow-related parameter (fD*) maps were calculated on the basis of the IVIM biexponential model, and the parametric maps were compared among the four different gas mixtures. Paired, one-tailed Student t tests were performed to assess for statistically significant differences. Signal decay curves were biexponential in the brain parenchyma of all volunteers. When compared with inhaled ambient air, the IVIM perfusion parameters D*, f, and fD* increased as the concentration of inhaled CO(2) was increased (for the entire brain, P = .01 for f, D*, and fD* for CO(2) 5%; P = .02 for f, and P = .01 for D* and fD* for CO(2) 8%), and a trend toward a reduction was observed when participants inhaled pure oxygen (although P > .05). D remained globally stable. The IVIM perfusion parameters were reactive to hyperoxygenation-induced vasoconstriction and hypercapnia-induced vasodilatation. Accordingly, IVIM imaging was found to be a valid and promising method to quantify brain perfusion in humans. © RSNA, 2012.
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                Author and article information

                Contributors
                Pejman Jabehdar Maralani
                Journal
                Journal ID (nlm-ta): Data Brief
                Journal ID (iso-abbrev): Data Brief
                Title: Data in Brief
                Publisher: Elsevier
                ISSN (Electronic): 2352-3409
                Publication date PMC-release: 15 March 2021
                Publication date Collection: April 2021
                Publication date (Electronic): 15 March 2021
                Volume: 35
                Electronic Location Identifier: 106950
                Affiliations
                [a ]Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Toronto, ON, Canada
                [b ]Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Toronto, ON, Canada
                [c ]Department of Radiology, Stanford University, Stanford, CA, United States
                [d ]Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
                [e ]Department of Radiological Sciences and Psychiatry, University of California Los Angeles, Los Angeles, CA, United States
                [f ]Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
                [g ]Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
                [h ]Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
                [i ]Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, ON, Canada
                [j ]Department of Biostatistics, University Health Network, Toronto, ON, Canada
                [k ]Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
                Author notes
                [* ]Corresponding author. pejman.maralani@ 123456sunnybrook.ca
                Article
                Publisher Item ID: S2352-3409(21)00234-1 Publisher ID: 106950
                DOI: 10.1016/j.dib.2021.106950
                PMC ID: 8039816
                SO-VID: ce8ea523-18b1-47c1-bfe1-e3e7e66a8881
                Copyright © © 2021 The Author(s). Published by Elsevier Inc.
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 8 January 2021
                Date revision received : 25 February 2021
                Date accepted : 8 March 2021
                Related
                Categories
                Subject: Data Article

                Keywords: glioblastoma,overall survival,progression free survival,recurrence,simplified ivim,adc,diffusion coefficient,perfusion fraction
                Data availability:
                Keywords: glioblastoma, overall survival, progression free survival, recurrence, simplified ivim, adc, diffusion coefficient, perfusion fraction

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