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.
Highlights
Relative cerebral blood volume is a magnetic resonance imaging (MRI) biomarker computed from dynamic susceptibility contrast (DSC) images, and has been used extensively in brain tumor imaging for differentiation of progression versus pseudoprogression [1, 2], tumor grading [3], survival prediction [4], and tumor differentiation [5]
The proposed system falls within 2 standard deviations (SDs) of the Food and Drug Administration (FDA)-approved software systems, which is similar to the agreement between the 2 FDA cleared systems
Here, we present a method for estimating Relative cerebral blood volume (rCBV) metrics from DSC-MRI with an automated white matter (WM) selection step using a probabilistic atlas to further standardize rCBV calculation
Summary
Perfusion analysis software to compute rCBV from DSCMRI is widely available in clinical practice. It is commonly treated as a “black box,” and broad-scale integration has been slowed by the need for defining optimal methodological conditions to maximize rCBV accuracy. The rCBV for each voxel is calculated by trapezoidal integration under the ⌬R2*(t) area curve from the start to the end of the first-pass contrast bolus on a voxel basis divided by the value calculated for normal-appearing white matter (WM). Determining the start, end, and peak of the bolus is the most critical step of the algorithm, and it will affect calculation of percent signal recovery and mean transit time, in addition to rCBV
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