Abstract
PurposeOxygen 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.MethodsCerebral blood flow at rest (CBF) and during stress (sCBF), cerebral blood volume (CBV) maps acquired from oxygen-15 PET, and routine MR images (T1-, T2-, and T2*-weighted images) for 113 patients with steno-occlusive disease were learned with U-Net. MR and PET images acquired from the other 25 patients were used as test data. We compared the predicted OEF maps and intraclass correlation (ICC) with the real OEF values among combinations of MRI, CBF, CBV, and sCBF.ResultsAmong the combinations of input images, OEF maps predicted by the model learned with MRI, CBF, CBV, and sCBF maps were the most similar to the real OEF maps (ICC: 0.597 ± 0.082). However, the contrast of predicted OEF maps was lower than that of real OEF maps.ConclusionThese results suggest that the deep CNN learned useful features from CBF, sCBF, CBV, and MR images and predict qualitatively realistic OEF maps. These findings suggest that the deep CNN model can shorten the fixation time for 15O PET by skipping 15O2 scans. Further training with a larger data set is required to predict accurate OEF maps quantitatively.
Highlights
Oxygen extraction fraction (OEF) is a biomarker of the viability of brain tissue in ischemic stroke [1,2,3,4]
The predicted OEF maps were similar to the real maps, and the moderate intraclass correlation (ICC) values obtained by the model learned with MRI, cerebral blood flow (CBF), cerebral blood volume (CBV), and stressed CBF maps indicate that deep convolutional neural network (CNN) trained with the Positron emission tomography (PET) and magnetic resonance (MR) images can qualitatively predict OEF maps without the 15O2 scan
The best ICC for the full model and the significant effects of all binary variables for the input images suggest that all MRI, CBF, CBV, and stressed CBF maps contribute to the prediction of the OEF map
Summary
Oxygen extraction fraction (OEF) is a biomarker of the viability of brain tissue in ischemic stroke [1,2,3,4]. Calculating the OEF map requires PET scans for cerebral blood flow (CBF) with C 15O2 or H 215O and cerebral blood. Prediction of CBF [25] and cerebrovascular reserve [26] maps using the CNN learned with arterial spin labeling (ASL) maps and structural MR images have been proposed. We hypothesized that the deep CNN could predict OEF maps without the 15O2 scan from the other PET and MR images. To verify this hypothesis, we performed learning of structural MR images, CBV maps, and CBF maps at rest and under stress with acetazolamide as inputs and OEF maps as a target with U-shaped CNN with skip connections (U-Net) [27]. We performed the test using the model learned with the best combination
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