Dataset Information

Prediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images.

ABSTRACT:

Purpose

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.

Methods

Cerebral 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.

Results

Among 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.

Conclusion

These 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.

SUBMITTER: Matsubara K

PROVIDER: S-EPMC8589760 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR. Methods: We used florbetapir PET and structural MR data from the Alzheimer Disease Neuroimaging Initiative database. The generative network was trained to generate realistic structural MR images from florbetapir PET images. After the training, the model was applied to the quantification of cortical amyloid load. PET images were spatially normalized to the template space using the generated MR, and then SUV ratio (SUVR) of the target regions was measured by predefined regions of interest. A real MR-based quantification was used as the gold standard to measure the accuracy of our approach. Other MR-less methods-a normal PET template-based, a multiatlas PET template-based, and a PET segmentation-based normalization/quantification-were also tested. We compared the performance of quantification methods using generated MR with that of MR-based and MR-less quantification methods. Results: Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR. The structural similarity index between real and generated MR was 0.91 ± 0.04. The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods). Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method. Conclusion: Structural MR images were successfully generated from amyloid PET images using deep generative networks. Generated MR images could be used as templates for accurate and precise amyloid quantification. This generative method might be used to generate multimodal images of various organs for further quantitative analyses.

Project description:We analysed the importance of systemic and peripheral arteriovenous O2 difference ( a-v¯O2 difference and a-vf O2 difference, respectively) and O2 extraction fraction for maximal oxygen uptake ( V˙O2max ). Fick law of diffusion and the Piiper and Scheid model were applied to investigate whether diffusion versus perfusion limitations vary with V˙O2max . Articles (n = 17) publishing individual data (n = 154) on V˙O2max , maximal cardiac output ( Q˙max ; indicator-dilution or the Fick method), a-v¯O2 difference (catheters or the Fick equation) and systemic O2 extraction fraction were identified. For the peripheral responses, group-mean data (articles: n = 27; subjects: n = 234) on leg blood flow (LBF; thermodilution), a-vf O2 difference and O2 extraction fraction (arterial and femoral venous catheters) were obtained. Q˙max and two-LBF increased linearly by 4.9-6.0 L · min-1 per 1 L · min-1 increase in V˙O2max (R2 = .73 and R2 = .67, respectively; both P < .001). The a-v¯O2 difference increased from 118-168 mL · L-1 from a V˙O2max of 2-4.5 L · min-1 followed by a reduction (second-order polynomial: R2 = .27). After accounting for a hypoxemia-induced decrease in arterial O2 content with increasing V˙O2max (R2 = .17; P < .001), systemic O2 extraction fraction increased up to ~90% ( V˙O2max : 4.5 L · min-1 ) with no further change (exponential decay model: R2 = .42). Likewise, leg O2 extraction fraction increased with V˙O2max to approach a maximal value of ~90-95% (R2 = .83). Muscle O2 diffusing capacity and the equilibration index Y increased linearly with V˙O2max (R2 = .77 and R2 = .31, respectively; both P < .01), reflecting decreasing O2 diffusional limitations and accentuating O2 delivery limitations. In conclusion, although O2 delivery is the main limiting factor to V˙O2max , enhanced O2 extraction fraction (≥90%) contributes to the remarkably high V˙O2max in endurance-trained individuals.