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This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects. The authors recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for recurrent tumor versus posttreatment radiation effects. They recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. They measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%–100%) for normalized and standardized values. With binary cutoffs, predictive accuracies were similar for both standardized and normalized metrics and across relative CBV metrics. Standardization of relative CBV achieves similar equivalent performance compared with normalized relative CBV and offers an important step toward workflow optimization and consensus methodology.

The authors implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network. Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases.