Open AccessPrediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images
Author(s) -
Keisuke Matsubara,
Masanobu Ibaraki,
Yuki Shinohara,
Noriyuki Takahashi,
Hideto Toyoshima,
Toshibumi Kinoshita
Publication year - 2021
Publication title -
international journal of computer assisted radiology and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.701
H-Index - 49
eISSN - 1861-6429
pISSN - 1861-6410
DOI - 10.1007/s11548-021-02356-7
Subject(s) - convolutional neural network , cerebral blood flow , artificial intelligence , positron emission tomography , computer science , nuclear medicine , magnetic resonance imaging , pattern recognition (psychology) , deep learning , medicine , radiology , cardiology
Oxygen extraction fraction (OEF) is a biomarker for the viability of brain tissue in ischemic stroke. However, acquisition of the OEF map using positron emission tomography (PET) with oxygen-15 gas is uncomfortable for patients because of the long fixation time, invasive arterial sampling, and radiation exposure. We aimed to predict the OEF map from magnetic resonance (MR) and PET images using a deep convolutional neural network (CNN) and to demonstrate which PET and MR images are optimal as inputs for the prediction of OEF maps.