Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy.

ABSTRACT: Digital subtraction angiography (DSA) is the main imaging modality used to assess reperfusion during mechanical thrombectomy (MT) when treating large vessel occlusion (LVO) ischemic strokes. To improve this visual and subjective assessment, hybrid models combining angiographic parametric imaging (API) with deep learning tools have been proposed. These models use convolutional neural networks (CNN) with single view individual API maps, thus restricting use of complementary information from multiple views and maps resulting in loss of relevant clinical information. This study investigates use of ensemble networks to combine hemodynamic information from multiple bi-plane API maps to assess level of reperfusion. Three-hundred-eighty-three anteroposterior (AP) and lateral view DSAs were retrosp

SUBMITTER: Bhurwani MMS 

PROVIDER: S-EPMC7946164 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature