Systems and methods for estimation of blood flow characteristics using reduced order model and/or machine learning

A system for determining blood flow characteristics of a patient, the system comprising:

• at least one memory storing instructions and a machine-learning algorithm trained, based on errors determined between at least one impedance value of at least one training reduced-order model and at least one corresponding impedance value determined by computational fluid dynamics, to reduce error in one or more impedance values of an input reduced-order model;
• and
• at least one processor operatively connected to the memory and configured to execute the instructions to perform operations, including:
• obtaining a reduced-order model that is representative of at least a portion of a patient-specific anatomic model of at least a portion of a patient's vasculature, the reduced-order model including one or more points having an impedance value based on first estimated values of blood flow characteristics at one or more locations of the portion of the patient-specific anatomic model;
• updating the reduced-order model by employing the machine-learning algorithm;
• and
• using the updated reduced-order model to determine second estimated values for the blood flow characteristic at the one or more locations of the portion of the patient-specific anatomic model.

A non-transitory computer-readable medium comprising instructions for determining blood flow characteristics of a patient that are executable by at least one processor to perform operations, the operations including:

• obtaining a reduced-order model that is representative of at least a portion of a patient-specific anatomic model of at least a portion of a patient's vasculature, the reduced-order model including one or more points having an impedance value based on first estimated values of blood flow characteristics at one or more locations of the portion of the patient-specific anatomic model;
• updating the reduced-order model by employing a machine-learning algorithm that has been trained, based on errors determined between at least one impedance value of at least one training reduced-order model and at least one corresponding impedance value determined by computational fluid dynamics, to reduce error in one or more impedance values of an input reduced-order model;
• and
• using the updated reduced-order model to determine second estimated values for the blood flow characteristic at the one or more locations of the portion of the patient-specific anatomic model.