Abstract Body (Do not enter title and authors here): Background: Measurements of myocardial oxygenation extraction fraction (mOEF) maps with cardiovascular magnetic resonance (CMR) has been recently developed to assess myocardial oxygen metabolism in healthy. Recent studies focus on developing supervised machine learning methods to improve the quantification of mOEF. However, these supervised learning methods may be limited by the lack of ground-truth mOEF maps because MRI cannot directly measure mOEF with the same precision as PET.
Hypothesis: Model-based self-supervised learning neural networks maybe feasible to generate mOEF maps accurately.
Aims: To develop a physical model-based self-supervised learning neural network to accurately estimate mOEF for the patients with a variety of cardiomyopathies.
Methods: An ECG triggered, two-dimensional asymmetric spin-echo (ASE) prepared sequence was employed to scan 71 patients with dilated, hypertrophic, or ischemic cardiomyopathies. At least 3 slices were scanned for each patient and each ASE scan was performed free-breathing with a spatial resolution of 1.7 × 1.7 × 8 mm³ and scan time of 18 RR-interval. Acquisition of late gadolinium enhancement (LGE) was performed in each patient after the administration of 0.15 mmol/kg gadolinium agent for the visualization of myocardial infarction regions.
A fully convolutional neural network with U-Net architecture was optimized with a physical model-based signal loss function (see Figure 1) on the ASE training dataset from 67 patients to generate mOEF maps. To evaluate if the method can accurately quantify abnormal mOEF, four patients with myocardial infarction were selected as the test set.
Results: A previous publication reported normal mOEF levels at approximately 0.6-0.7 in healthy subjects. Figure 2 shows our proposed method generated mOEF maps from two representative patients and their LGE images for comparisons. In comparison with the remote region (mOEF = 0.70±0.10), the mOEF of myocardial regions with infarction was significantly lower (mOEF = 0.36±0.12, p < 0.001). The infarction areas predicted by the network overlapped well with the LGE.
Conclusion: Quantification of mOEF using the proposed model-based self-supervised U-Net is feasible to detect abnormalities in regional myocardial oxygenation. Current study is ongoing to evaluate the proposed technique in a large cardiac patient dataset.