Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation

Abstract

Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages.

Cerebral blood volume and apparent diffusion coefficient – Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma

“…These were then registered to the T1 weighted post-contrast images using ANTs [19]. We calculated the mean, median, 95th and 20th percentile values for ADC pixels corresponding to the CEROI, referred to as ADCmn, ADCmed, ADC95, ADC20 [20].…”

“…We computed relative cerebral blood volume (rCBV), K2, and Peak Signal Recovery (PSR) images from the dynamic susceptibility contrast (DSC) source images [20]. The dynamic susceptibility contrast (DSC) images were acquired with a 128 × 128 matrix to cover the brain, and with 10-12 slices that were 5 mm thick with 1 mm gap, to cover the tumor.…”

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation

“…For example, the accuracy of rCBV depends on acquisition parameters such as TE and flip angle 14 . The calculation of leakage correction is software dependent 24 , 25 , and the percentage signal recovery is also influenced by contrast agent preload and pulse sequence parameters 8 . Indeed, when we compared the average rCBV values, the rCBVs of glioblastoma were 5.52 ± 2.33 (mean ± standard deviation) vs. 7.34 ± 1.51 in the internal and external validation sets, respectively.…”

Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)‐MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T

“…Furthermore, studies have shown interrater variability when ROIs are manually drawn. 16,24,25 In our study, the AUROC of rCBV for a manually drawn NAWM was 0.78, and is similar to ROC results published by Tietze et al, where their AUROC for rCBV was 0.78. 26 Here we demonstrate that using an automatically segmented ROI for rCBV increases the AUROC to 0.86.…”