Abstract:
Coronary heart disease is the leading cause of death in developed countries and coronary artery calcification is an important predictor of cardiac events. We present two deep learning approaches that could help improve diagnosis of coronary artery calcification with limited angle spectral CT. To this end, we embed two unrolled iterative primal-dual optimisation schemes into neural networks and produce material separated reconstructed images.Both methods are compared and evaluated on simulated random ellipse and human thoracic phantoms containing atherosclerotic plaque with 3 and 5 different materials. We further investigate the influence of different neural network hyperparameters and compare against a classical material decomposition and reconstruction algorithm.
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