# US 12,109,061 B2 · Data-driven plaque determination 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 12,109,061 B2 - Document ID: US12109061B2 - Status: Granted - Kind: B2 - Application number: 17/195,694 - Filed: 2021-03-09 - Published: 2021-07-22 - Granted: 2024-10-08 - Earliest claimed: 2014-11-24 - Assignees: Siemens Healthineers AG - Applicants: Siemens Healthineers AG - Inventors: Lucian Mihai Itu; Tiziano Passerini; Saikiran Rapaka; Puneet Sharma; Chris Schwemmer; Max Schoebinger; Thomas Redel; Dorin Comaniciu - Web page: https://patentgrep.com/patent/US12109061B2 ## CPC - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - A61B 5/026: Measuring blood flow {(A61B3/1233, A61B3/1241 take precedence)} - A61B 5/7267: {involving training the classification device} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT} - A61B 8/06: Measuring blood flow - A61B 8/065: {to determine blood output from the heart} - A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/22: Matching criteria, e.g. proximity measures - G06F 18/2413: based on distances to training or reference patterns - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06V 10/42: Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation - G06V 10/776: Validation; Performance evaluation - G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/50: for simulation or modelling of medical disorders - A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance} - A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction} - A61B 5/0263: {using NMR} - A61B 5/743: {Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots} - A61B 6/469: {for selecting a region of interest [ROI]} - A61B 8/469: {for selection of a region of interest} - A61B 2576/00: Medical imaging apparatus involving image processing or analysis (A61B1/00009, A61B6/52 and A61B8/52 take precedence) - G06T 2200/04: involving 3D image data - G06T 2207/10072: Tomographic images - G06T 2207/10076: 4D tomography; Time-sequential 3D tomography - G06T 2207/20081: Training; Learning - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2207/30104: Vascular flow; Blood flow; Perfusion - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## 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. ## Claims ### Claim 1 A method for plaque determination in medical imaging, the method comprising: acquiring medical scan data representing an anatomical structure of a patient; extracting a set of features from the medical scan data; inputting, by a processor, the features to a first machine-trained classifier, the first machine-trained classifier trained to output a value of hemodynamic metric; inputting the value of the hemodynamic metric and additional features to a second machine-trained classifier; and outputting, by the processor with application of the first machine-trained classifier to the features, an indicator of a risk of the plaque rupture or a nature of the plaque, the indicator being a function of the value of the hemodynamic metric and being a future evolution of the patient, the future evolution of the patient based on machine training with patient evolution data, the second machine-trained classifier performing the outputting of the indicator in response to the inputting to the second machine-trained classifier. ### Claim 2 (depends on 1) The method of claim 1 wherein outputting comprises outputting the indicator as the risk of the plaque rupture or nature of the plaque as the future evolution of the patient. ### Claim 3 (depends on 1) The method of claim 1 wherein outputting comprises outputting the indicator as the risk of the plaque rupture. ### Claim 4 (depends on 1) The method of claim 1 wherein outputting comprises outputting the indicator as the nature of the plaque. ### Claim 5 (depends on 4) The method of claim 4 wherein outputting comprises outputting where the indicator of the nature of the plaque comprises indication of fibrous tissue, lipid tissue, necrotic tissue, or calcified tissue. ### Claim 6 (depends on 1) The method of claim 1 wherein outputting comprises outputting the indicator as a plaque burden. ### Claim 7 (depends on 1) The method of claim 1 further comprising: altering at least one of the features, the at least one of the features comprising a plaque-related feature; and repeating the inputting and outputting with the altered feature. ### Claim 8 (depends on 1) The method of claim 1 wherein inputting comprises inputting an amount of therapy to the second machine-trained classifier, the output of the second machine-trained classifier comprising output by the amount of therapy. ### Claim 9 (depends on 1) The method of claim 1 wherein inputting comprises inputting to the first machine-trained classifier trained from synthetic data, the synthetic data comprising (a) an in vitro model with a ground truth of the hemodynamic metric measured form the in vitro model and/or (b) an in silico model with a ground truth of the hemodynamic metric computed with computation fluid dynamics. ### Claim 10 (depends on 9) The method of claim 9 wherein the synthetic data comprises examples with variation in at least some plaque features. ### Claim 11 (depends on 1) The method of claim 1 wherein extracting the set of the features comprises: extracting geometrical features of the anatomical structure; and extracting the features of one or more abnormalities of the anatomical structure. ### Claim 12 (depends on 1) The method of claim 1 wherein extracting the set of the features comprises extracting functional features representing operation of the anatomical structure, wherein the first machine-trained classifier was trained from virtual representations of the operation of the anatomical structure. ### Claim 13 (depends on 1) The method of claim 1 wherein outputting comprises outputting the indicator on a display with an image of the anatomical structure generated from the medical scan data. ### Claim 14 A method for plaque determination in medical imaging, the method comprising: generating a plurality of examples of anatomical arrangements; storing a value for a plaque-related characteristic for each of the examples of the anatomical arrangements; training, with machine learning, a first classifier for estimating a hemodynamic metric from features extracted from the examples; and training, with machine learning, using the examples, the hemodynamic metric, and the stored value for the plaque-related characteristic for each of the examples of the anatomical arrangements, a second classifier for predicting the plaque-related characteristic. ### Claim 15 (depends on 14) The method of claim 14 wherein training the second classifier comprises training the second classifier to output the plaque-related characteristic as a likelihood of plaque rupture or a nature of the plaque. ### Claim 16 (depends on 14) The method of claim 14 wherein training the second classifier comprises training the second classifier to output a future evolution of the plaque-related characteristic. ### Claim 17 A method for plaque determination in medical imaging, the method comprising: generating a plurality of examples of anatomical arrangements; storing a value for a plaque-related characteristic for each of the examples of the anatomical arrangements; training, with machine learning, using the examples and the stored value for the plaque-related characteristic for each of the examples of the anatomical arrangements, a classifier for predicting the plaque-related characteristic; wherein generating comprises generating with synthetic data not representing any particular patient with perturbing computer modeling, physical modeling, or both in a systematic pattern.