Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke - Ye Jin Jeon; Hong Gee Roh; Sumin Jung; Hyun Yang; Hee Jong Ki; Jeong Jin Park; Taek-Jun Lee; Na Il Shin; Ji Sung Lee; Jin Tae Kwak; Hyun Jeong Kim (Scientific Reports)
Abstract To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. Two raters graded the collateral perfusion scores of both conventional and DL-driven MRA collateral maps and measured the grading time. They also qualitatively assessed the image quality of both collateral maps. Interrater and inter-method agreements for collateral perfusion grading between the two collateral maps were analyzed, along with a comparison of grading time and image quality. In the analysis of the 296 acute ischemic stroke patients, the inter-method agreement for collateral perfusion grading was almost perfect (κ = 0.96, 95% CI: 0.95–0.98). Compared to conventional MRA collateral maps, the time taken for collateral perfusion grading on DL-driven MRA collateral maps was shorter (P < 0.001 for rater 1 and P = 0.003 for rater 2), and the image quality of the DL-driven MRA collateral maps was superior (P < 0.001 for rater 1 and P = 0.002 for rater 2). The DL-driven MRA collateral map demonstrates clinical feasibility for collateral perfusion grading in acute ischemic stroke, with the added benefits of reduced generation and interpretation time, along with improved image quality of the MRA collateral map.