A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials

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 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.

Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI

“…While the ASFNR recommendations provide guidelines for DSC-MRI acquisition and dosing protocols in brain tumors (Welker et al, 2015), there has yet to be a consensus protocol on how to analyze this data nor has there been multi-site trials to validate their clinical utility, similar to the evaluation of RAPID software in acute stroke (Lansberg et al, 2011;Warach et al, 2016). Given the challenges of optimizing these protocols over all parameter space in clinical cohorts, digital reference objects have been developed for broad-scale parameter testing and may ultimately drive protocol development (Bosca and Jackson, 2016;Semmineh et al, 2017;Zhu et al, 2015).…”

“…The relative magnitude of T 1 and T 2 * leakage effects depends on the pulse sequence parameters and dosing schemes used to collect the data, which, in turn, influences the success of postprocessing-based leakage correction. Given the difficulty of comparing the accuracy of different acquisition and postprocessing methods in an individual patient, recent efforts have turned to simulations (Leu et al, 2016) and the development of patient data driven digital reference objects (DRO) (Semmineh et al, 2017) in order to identify the optimal DSC-MRI acquisition and analysis protocols. To date, these efforts reveal that the most accurate and precise approach for DSC-MRI at 3T, when using the recent ASFNR recommendations (Welker et al, 2015).…”

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

“…However, a consensus regarding best practices for DSC-MRI data acquisition is being reached as described in a recent review [40], and includes the approach used for this study. Specifically, use of a preload of contrast agent, and a flip angle less than 90° is proving to be one of the most accurate approaches, further confirmed by two recent studies [19, 41], both incorporating sophisticated simulations of DSC-MRI data representative of brain tumor. Use of a preload might also be an important reason why greater consistency across post-processing methods was found in this study compared to previous studies (eg [32]).…”

“…Several studies report a lower SNR [35] as well as greater inaccuracy when gamma-variate fitting is used for brain tumor DSC-MRI data especially in the presence of contrast agent leakage [19, 20]. Though gamma-variate fitting suppresses the post-bolus baseline making it appear that leakage has been corrected there is no physiologic basis for this correction and it does not appropriately consider leakage that can occur during the bolus [36].…”

“…This initial Gd administration serves as a preload for the subsequent DSC-MRI acquisition. A preload results in more accurate rCBV [19] since it reduces the T1 leakage effects that can confound the DSC-MRI data analysis [9, 20]. After collecting the post-contrast T1w images (T1+C), using parameters equivalent to the pre-contrast settings, DSC-MRI data was acquired (GRE-EPI: TR=1100–1250 ms, TE=30 ms, flip angle=70–80 degrees, 12–17 slices, slice thickness=4–5 mm, interslice gap=0–1.5 mm, matrix=96×96/128×128, FOV=165–240×165–240 mm 2 ).…”