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This study aimed to evaluate morphologic patterns and the delineation of deeply seated collateral networks using ultra-high-field MRA in comparison with conventional DSA in 15 patients. Sequences acquired at 7T were TOF-MRA with 0.22 X 0.22 X 0.41 mm³ resolution and MPRAGE with 0.7 X 0.7 X 0.7 mm³ resolution. The relevant deeply seated collateral networks were classified into 2 categories and 6 pathways. A total of 100 collateral networks were detected on DSA; 106, on TOF-MRA; and 73, on MPRAGE. Delineation of deeply seated collateral networks was comparable between TOF-MRA and DSA. The authors demonstrate excellent delineation of 6 distinct deeply seated collateral network pathways in Moyamoya angiopathy.

MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify IDH1 mutation status, 1p/19q codeletion, and MGMT promotor methylation status. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. The authors conclude that this shows the feasibility of a deep-learning CNN approach for the accurate classification of individual genetic mutations of both low- and high-grade gliomas and that the relevant MR imaging features acquired from an added dimensionality-reduction technique are concordant with existing literature, showing that neural networks are capable of learning key imaging components without prior feature selection or human directed training.