Neural network-derived perfusion maps: A model-free approach to computed tomography perfusion in patients with acute ischemic stroke.

ABSTRACT:

Objective

In this study, we investigate whether a Convolutional Neural Network (CNN) can generate informative parametric maps from the pre-processed CT perfusion data in patients with acute ischemic stroke in a clinical setting.

Methods

The CNN training was performed on a subset of 100 pre-processed perfusion CT dataset, while 15 samples were kept for testing. All the data used for the training/testing of the network and for generating ground truth (GT) maps, using a state-of-the-art deconvolution algorithm, were previously pre-processed using a pipeline for motion correction and filtering. Threefold cross validation had been used to estimate the performance of the model on unseen data, reporting Mean Squared Error (MSE). Maps accuracy had been checked through manual segment

SUBMITTER: Gava UA 

PROVIDER: S-EPMC10034033 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature