# US 10,478,130 B2 · Plaque vulnerability assessment in medical imaging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,478,130 B2 - Document ID: US10478130B2 - Status: Granted - Kind: B2 - Application number: 14/973,390 - Filed: 2015-12-17 - Published: 2016-08-18 - Granted: 2019-11-19 - Earliest claimed: 2015-02-13 - Assignees: Siemens Healthcare GmbH - Applicants: Siemens Healthcare GmbH - Inventors: Puneet Sharma; Dorin Comaniciu - Web page: https://patentgrep.com/patent/US10478130B2 ## CPC - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance} - A61B 5/021: Measuring pressure in heart or blood vessels - A61B 5/026: Measuring blood flow {(A61B3/1233, A61B3/1241 take precedence)} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - A61B 5/14546: {for measuring analytes not otherwise provided for, e.g. ions, cytochromes} - A61B 5/7267: {involving training the classification device} - A61B 6/461: {Displaying means of special interest} - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - A61B 8/06: Measuring blood flow - A61B 8/085: {for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules} - A61B 8/0891: {for diagnosis of blood vessels} - A61B 8/12: in body cavities or body tracts, e.g. by using catheters - A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)} - G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis - G16H 30/40: for processing medical images, e.g. editing - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/50: for simulation or modelling of medical disorders - A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)} - A61B 6/032: {Transmission computed tomography [CT]} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Rather than rely on variation from physician to physician and limited imaging information for assessing plaque vulnerability of a patient, medical imaging and other information are used by a machine-implemented classifier to predict plaque rupture. Anatomical, morphological, hemodynamic, and biochemical features are used in combination to classify plaque. ## Claims ### Claim 1 A method for assessing plaque vulnerability of a patient in medical imaging, the method comprising: extracting an anatomical feature of a vessel or plaque and a morphological feature of the plaque from a scan of the patient by a medical imaging scanner; obtaining a hemodynamic feature of the patient from a hemodynamic sensor or personalized modeling of the vessel based on the scan; receiving at an interface a biochemical feature from a blood test of the patient, the biochemical feature including a level of high sensitive troponin; calculating by a machine-implemented classifier a risk score for plaque rupture for the patient and a risk stratification, both the risk score and risk stratification calculated by the machine-implemented classifier from the anatomical, morphological, hemodynamic, and biochemical features; and transmitting the risk score for the patient to a display. ### Claim 2 (depends on 1) The method of claim 1 wherein extracting the morphological feature comprises extracting a type of the plaque from an angiography as the scan. ### Claim 3 (depends on 1) The method of claim 1 wherein extracting the anatomical feature comprises extracting a shape, geometry, inlet angle, outlet angle, location of the plaque relative to the vessel, stenosis of the vessel, vessel size, vessel shape, or combinations thereof. ### Claim 4 (depends on 1) The method of claim 1 wherein extracting comprises extracting automatically with a processor or semi-automatically with the processor and a user input. ### Claim 5 (depends on 1) The method of claim 1 wherein obtaining comprises obtaining a pressure, flow, or pressure and flow and calculating stress from the pressure, flow, or pressure and flow. ### Claim 6 (depends on 1) The method of claim 1 wherein the level of high sensitive troponin is a level of high sensitive troponin T. ### Claim 7 (depends on 1) The method of claim 1 wherein calculating comprises calculating by the machine-implemented classifier as a weighted sum of ranks mapped from each of the anatomical, morphological, hemodynamic, and biochemical features, the weights based on clinical literature. ### Claim 8 (depends on 1) The method of claim 1 wherein calculating comprises calculating by the machine-implemented classifier comprises a mechanistic model. ### Claim 9 (depends on 1) The method of claim 1 wherein calculating comprises calculating by the machine-implemented classifier comprising a machine-learnt classifier. ### Claim 10 (depends on 1) The method of claim 1 wherein calculating comprises calculating the risk score as part of the risk stratification and a plaque vulnerability assessment for the patient in a coronary artery disease analysis. ### Claim 11 (depends on 1) The method of claim 1 wherein calculating comprises calculating the risk score for the patient in an acute coronary syndrome analysis. ### Claim 12 (depends on 1) The method of claim 1 further comprising repeating extracting, obtaining, and calculating for another plaque. ### Claim 13 (depends on 1) The method of claim 1 further comprising performing the scan with the medical imaging scanner comprising a non-invasive modality, wherein the anatomical, morphological, and hemodynamic features are all obtained without invasion of the patient. ### Claim 14 A method for assessing plaque vulnerability of a patient in medical imaging, the method comprising: loading a morphological characteristic of plaque of a patient, a hemodynamic characteristic, an anatomy characteristic of a vessel with the plaque, and a biochemical characteristic of the plaque as an input feature vector, where a biochemical characteristic is a level of high sensitive troponin; classifying a vulnerability of the plaque of the patient by a machine-trained classifier in response to the input feature vector, the classifying being part of a risk stratification based on the hemodynamic, biochemical, and anatomy characteristics; and signaling a result of the classifying to a user. ### Claim 15 A method for assessing plaque vulnerability of a patient in medical imaging, the method comprising: extracting an anatomical feature of a vessel or plaque and a morphological feature of the plaque from a scan of the patient by a medical imaging scanner; obtaining a hemodynamic feature of the patient from a hemodynamic sensor or personalized modeling of the vessel based on the scan; receiving at an interface a biochemical feature from a blood test of the patient, the receiving comprising receiving a level of high sensitive troponin; calculating by a machine-implemented classifier a risk score for plaque rupture for the patient and a risk stratification, both the risk score and risk stratification calculated by the machine-implemented classifier from the anatomical, morphological, hemodynamic, and biochemical features; and transmitting the risk score for the patient to a display.