Vol. 3 No. 1 - Mar 2017

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A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials Natenael B. Semmineh 1 , Ashley M. Stokes 1 , Laura C. Bell 1 , Jerrold L. Boxerman 2 , and C. Chad Quarles 1 1 Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona and 2 Department of Diagnostic Imaging, RI Hospital and Alpert Medical School of Brown University, Providence, Rhode Island Corresponding Author: C. Chad Quarles, PhD Department of Imaging Research, Barrow Neurological Institute, 350 W. Thomas Road, Phoenix, AZ 85013; E-mail: Key Words: dynamic susceptibility contrast MRI, digital reference object, brain tumor perfusion Abbreviations: Dynamic susceptibility contrast (DSC), magnetic resonance imaging (MRI), digital reference object (DRO), cerebral blood flow (CBF), cerebral blood volume (CBV), contrast agent (CA), repetition time (TR), echo time (TE), The Cancer Imaging Archive (TCIA), flip angle (FA), 3-dimensional (3D), finite perturber finite difference method (FPFDM), extravascular extracellular space (EES), gadopentetate dimeglumine (Gd-DTPA), arterial input function (AIF), percent signal recovery (PSR) The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing po- tential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postpro- cessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a vali- dated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas. To achieve DSC-MRI signals represen- tative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T 1 and T 2 * changes observed across multiple glioblastomas, the DRO's input parameters were trained using DSC-MRI data from 23 glioblastomas (.40 000 voxels). The DRO's ability to produce reliable signals for combina- tions of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. Representative applications of the DRO are presented, including the selection of DSC- MRI acquisition and postprocessing methods that optimize CBV accuracy, determination of the impact of DSC-MRI methodology choices on sample size requirements, and the assessment of treatment response in clinical glioblastoma trials. INTRODUCTION Dynamic susceptibility contrast (DSC)-magnetic resonance im- aging (MRI) noninvasively measures brain tumor cerebral blood flow (CBF) and cerebral blood volume (CBV), and it has found increasing clinical applications for patient management (1-18). To facilitate multi-institutional comparability and consistency, national initiatives, including National Cancer Institute's Quan- titative Imaging Network, Radiological Society of North America's Quantitative Imaging Biomarkers Alliance, and the National Brain Tumor Society's Jumpstarting Brain Tumor Drug Development Coalition, are underway to standardize acquisition and analysis protocols for DSC-MRI (19, 20). A challenge to such efforts is the relative paucity of studies systematically evaluat- ing the influence of DSC-MRI methodology on CBV accuracy. In practice, such validation studies are difficult to perform in patients because of the need for multiple contrast agent (CA) injections and lack of a noninvasive gold standard CBV measure for reference. As an alternative to in vivo validation, in silico digital reference objects (DROs) provide a means for computing synthetic MRI signals and derived kinetic parameters for a range of clinically relevant input conditions. Such a DRO was recently developed for dynamic contrast-enhanced MRI to investigate the biases and variances of algorithms used for image analysis (21). The goal of this report is to describe the development of a DSC-MRI DRO that recapitulates the heterogeneous signal char- acteristics measured in glioblastomas. In general, there are two underlying strategies that can be pursued for DROs emulating MRI data. When the primary objective is to establish multisite analysis consistency, synthetic signals can be computed using simple heuristic models approximating the underlying biophys- ics of signal formation, as the endpoint is to assess the agree- ment between software estimates of a parameter such as CBV RESEARCH ARTICLE ABSTRACT © 2017 The Authors. Published by Grapho Publications, LLC This is an open access article under the CC BY-NC-ND license ( ISSN 2379-1381 TOMOGRAPHY.ORG | VOLUME 3 NUMBER 1 | MARCH 2017 41

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