SYSTEMS AND METHODS FOR IMAGE PROCESSING TO DETERMINE BLOOD FLOW
Embodiments include systems and methods for determining cardiovascular information for a patient. A method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.
1 - 32 . (canceled)
33 . A method for decision support for therapy, the method comprising:
segmenting organ data representing an organ of a first patient from scan data from a medical scanner, the scan data representing a volume of the first patient;
simulating, by a processor, a plurality of different therapies with a physiological model personalized to the organ based on the segmented data, the different therapies being for a therapy device with different parameters and/or for different therapy devices;
estimating, by the processor, uncertainties in simulated outcomes of the different therapies; and
presenting on a display the simulated outcomes of the simulating of the different therapies and the estimated uncertainties.
34 . The method of claim 33 wherein simulating comprises simulating with different physiological models.
35 . The method of claim 33 wherein simulating comprises identifying past patients similar to the first patient and using past patient results for the different therapies as the results of the simulation for the first patient.
36 . The method of claim 35 wherein identifying comprises finding features of the first patient for comparison to data of the past patients with a machine learning algorithm.
37 . The method of claim 33 wherein estimating comprises estimating the uncertainties in the simulated outcomes of the simulating based on variability of the simulated outcomes.
38 . The method of claim 33 wherein presenting comprises presenting the simulated outcomes and respective estimated uncertainties in a ranked order.
39 . The method of claim 33 wherein segmenting the organ data comprises segmenting the organ data representing a vessel, wherein simulating comprises simulating with the physiological model providing biomechanical parameters with inverse modeling or computation fluid dynamics and the different therapies comprising variation in stent properties in a stent model, the simulating being interaction of the stent model with the physiological model.
40 . A method for decision support for therapy, the method comprising:
inputting patient information from different sources to a machine learning algorithm, the patient information specific to a first patient and a type of therapy device;
selecting similar patients to the first patient with an output of the machine learning algorithm, the similar patients having been treated with the type of therapy device;
determining a range of outcomes from a range of therapy devices of the type of therapy device from data for the similar patients; and
displaying the range of outcomes and the range of therapy devices for the first patient.
41 . The method of claim 40 further comprising estimating uncertainties of the outcomes, wherein displaying comprises displaying the outcomes with the uncertainties.
42 . The method of claim 40 wherein inputting comprises inputting the patient information as clinical data, hemodynamic factors, and imaging data.
43 . The method of claim 40 wherein determining comprises determining efficacy for each of the therapy devices.
44 . The method of claim 40 further comprising simulating treatment of the therapy devices with a physiological model fit with at least some of the patient information, inputting the patient information and results of the simulation of the treatment to a second deep auto-encoder, and selecting another set of similar patients based on an output of the second deep auto-encoder.
45 . The method of claim 40 wherein the type of therapy device comprises a stent, the patient information includes vessel information from a medical scanner, and the range of therapy devices comprises stents with different properties.
46 . A method for decision support for therapy, the method comprising:
inputting patient information from different sources to a machine learning algorithm, the patient information specific to a first patient and a type of therapy device;
selecting first similar patients to the first patient with an output of the machine learning algorithm, the similar patients having been treated with the type of therapy device;
determining a first range of first outcomes from a range of therapy devices of the type of therapy device from data for the first similar patients;
selecting at least one of the therapy devices based on the outcome;
simulating treatment by the selected at least one of the therapy devices using a physiological model personalized to the first patient and a model of the type of therapy device specific to the at least one of the therapy devices;
calculating hemodynamic factors resulting from the simulation of the treatment;
inputting the hemodynamic factors and at least some of the patient information to a second deep auto-encoder;
selecting second similar patients to the first patient with an output of the second deep auto-encoder;
determining at least one second outcome from the at least one of the therapy devices from data for the second similar patients; and
displaying the at least one second outcome and the at least one therapy device for the first patient.
47 . The method of claim 46 further comprising simulating as a function of reinforcement learning.