Obtaining robust perfusion measures from pediatric Dynamic Susceptibility Contrast (DSC-) MRI, such as cerebral blood volume (CBV), is challenging due to variability in acquisition protocols between centres and a heterogeneous patient population. Quality control (QC) is currently carried out by expert qualitative review. An automated QC pipeline was developed which used denoising to salvage data, and assessed data quality using logistic regression classification, with signal-to-noise ratio (SNR) and root mean square error (RMSE) in a gamma variate fit to the first pass as predictors. SNR was the key factor in data quality and denoising is important in assuring appropriate analysis.
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