Synthetic data-driven hemodynamic determination in medical imaging

US9349178B1 · kind B1 · utility

Assignee

• Siemens Aktiengesellschaft · DE

Inventors

• Lucian Mihai Itu · Brașov, RO
• Tiziano Passerini · Plainsboro, US
• Saikiran Rapaka · Ewing, US
• Puneet Sharma · Rahway, US
• Chris Schwemmer · Forchheim, DE
• Max Schoebinger · Hirschaid, DE
• Thomas Redel · Baiersdorf, DE
• Dorin Comaniciu · Princeton Junction, US

Key dates

• Filing date: Jul 21, 2015
• Grant date: May 24, 2016
• Expiry date: Jul 21, 2035

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H30/20
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

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.