Deep learning prediction of stroke thrombus red blood cell content from multiparametric MRI.

ABSTRACT:

Background and purpose

Thrombus red blood cell (RBC) content has been shown to be a significant factor influencing the efficacy of acute ischemic stroke treatment. In this study, our objective was to evaluate the ability of convolutional neural networks (CNNs) to predict ischemic stroke thrombus RBC content using multiparametric MR images.

Materials and methods

Retrieved stroke thrombi were scanned ex vivo using a three-dimensional multi-echo gradient echo sequence and histologically analyzed. 188 thrombus R₂*, quantitative susceptibility mapping and late-echo GRE magnitude image slices were used to train and test a 3-layer CNN through cross-validation. Data augmentation techniques involving input equalization and random image transformation were employed t

SUBMITTER: Christiansen SD 

PROVIDER: S-EPMC11483724 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature