Journal of magnetic resonance imaging : JMRI · 2015
Automated estimation of salvageable tissue: Comparison with expert readers.
Сравнительное исследованиеОценочное исследованиеСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Hansen Mikkel B, Nagenthiraja Kartheeban, Ribe Lars R, Dupont Kristina H, Østergaard Leif, Mouridsen Kim
Аннотация
PURPOSE: To assess the performance of an automatic perfusion-diffusion mismatch outlining algorithm, in a cohort of acute ischemic stroke patients imaged as part of a multicenter study.
MATERIALS AND METHODS: Magnetic resonance imaging (MRI) from 167 patients with anterior circulation strokes scanned at either 3T or 1.5T systems were analyzed retrospectively through an automatic perfusion-diffusion mismatch detection algorithm. In addition, four expert raters manually outlined perfusion lesions on time-to-peak (TTP) maps and diffusion lesions on diffusion-weighted images (DWI), and reference perfusion-diffusion mismatch masks were obtained as the areas where at least three experts were in agreement that tissue was part of the perfusion-weighted imaging (PWI) lesion, but not the diffusion lesion. Per-subject analyses of mismatch volumes and mismatch overlap were subsequently performed.
RESULTS: The use of the automatic perfusion-diffusion mismatch detection algorithm resulted in a 4.0 ml mean (standard deviation 28.7 ml) difference in mismatch volume compared to the reference expert consensus (Pearson correlation, r = 0.91, P < 0.0001). The median spatial agreement was 0.71, with an interquartile range of 0.28.
CONCLUSION: We demonstrated excellent agreement between the perfusion-diffusion mismatch masks estimated by our proposed automatic algorithm and those achieved by expert consensus.
MATERIALS AND METHODS: Magnetic resonance imaging (MRI) from 167 patients with anterior circulation strokes scanned at either 3T or 1.5T systems were analyzed retrospectively through an automatic perfusion-diffusion mismatch detection algorithm. In addition, four expert raters manually outlined perfusion lesions on time-to-peak (TTP) maps and diffusion lesions on diffusion-weighted images (DWI), and reference perfusion-diffusion mismatch masks were obtained as the areas where at least three experts were in agreement that tissue was part of the perfusion-weighted imaging (PWI) lesion, but not the diffusion lesion. Per-subject analyses of mismatch volumes and mismatch overlap were subsequently performed.
RESULTS: The use of the automatic perfusion-diffusion mismatch detection algorithm resulted in a 4.0 ml mean (standard deviation 28.7 ml) difference in mismatch volume compared to the reference expert consensus (Pearson correlation, r = 0.91, P < 0.0001). The median spatial agreement was 0.71, with an interquartile range of 0.28.
CONCLUSION: We demonstrated excellent agreement between the perfusion-diffusion mismatch masks estimated by our proposed automatic algorithm and those achieved by expert consensus.