Abstract
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 potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing 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 validated 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 representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2* 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 combinations 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.
Highlights
We developed a DSCMRI digital reference object (DRO) that is driven by a validated computational strategy to compute magnetic resonance imaging (MRI) signals for realistic 3-dimensional (3D) tissue structures [22]; partially constrained by parameter inputs defined from in vivo data; and, for unknown parameters, trained using a public database of Dynamic susceptibility contrast (DSC)-MRI data in glioblastomas
The percent signal recovery (PSR) and percent relaxation drop (PRD) heterogeneity of the in vivo data was fully reflected in the DRO
This indicates that the trained DRO can accurately model the underlying contrast agent (CA)-induced T1 and T*2 effects and the associated DSC-MRI signals for different sets of pulse sequence parameters and CA dosing schemes
Summary
Dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) noninvasively measures brain tumor cerebral blood flow (CBF) and cerebral blood volume (CBV), and it has found increasing clinical applications for patient management [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. A challenge to such efforts is the relative paucity of studies systematically evaluating the influence of DSC-MRI methodology on CBV accuracy Such validation studies are difficult to perform in patients because of the need for multiple contrast agent (CA). 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]
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