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AssigneeSiemens Healthineers AG
InventorsLucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.
A method for plaque determination in medical imaging, the method comprising:
A method for plaque determination in medical imaging, the method comprising:
A method for plaque determination in medical imaging, the method comprising:
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