Systems and methods for estimating hemodynamic forces acting on plaque and monitoring risk
Computer-implemented methods are disclosed for estimating values of hemodynamic forces acting on plaque or lesions. One method includes: receiving one or more patient-specific parameters of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion; constructing a patient-specific geometric model of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion, using the received one or more patient-specific parameters; estimating, using one or more processors, the values of hemodynamic forces at one or more points on the patient-specific geometric model, using the patient-specific parameters and geometric model by measuring, deriving, or obtaining one or more of a pressure gradient and a radius gradient; and outputting the estimated values of hemodynamic forces to an electronic storage medium. Systems and computer readable media for executing these methods are also disclosed.
1. A computer-implemented method for monitoring hemodynamic risk in a patient, the method comprising:
acquiring a patient-specific geometric model of blood flow through at least the portion of the patient's vasculature;
obtaining current physiological and/or blood supply characteristics of the patient;
obtaining one or more patient-specific parameters of at least a portion of the patient's vasculature;
determining current hemodynamic characteristics of the patient based on the patient-specific geometric model, the current physiological and/or blood supply characteristics, and the one or more patient-specific parameters;
obtaining maximum allowable hemodynamic characteristics for the patient;
comparing the current hemodynamic characteristics with the maximum allowable hemodynamic characteristics;
determining, based on the comparison, whether the current hemodynamic characteristics include an abnormal value; and
in response to determining that the current hemodynamic characteristics include an abnormal value, producing a warning indicative of the abnormal value.
2. The computer-implemented method of claim 1 , wherein the current physiological and/or blood supply characteristics of the patient are acquired during exercise by the patient.
3. The computer-implemented method of claim 1 , wherein the current physiological and or blood supply characteristics of the patient are acquired by a mobile device associated with the patient.
4. The computer-implemented method of claim 1 , wherein determining current hemodynamic characteristics of the patient includes:
determining simplified hemodynamics characteristics for at least a portion of the patient-specific geometric model; and
based on the determined simplified hemodynamics characteristics, determining hemodynamic characteristics for a further portion of the patient-specific geometric model at which the simplified hemodynamics characteristics are unknown.
5. The computer-implemented method of claim 4 , wherein determining hemodynamic characteristics for the further portion of the patient-specific geometric model includes employing a machine learning algorithm trained, based on patient-specific parameters of other patients and hemodynamic characteristics associated with one or more portions of patient-specific geometric models of the other patients, to determine hemodynamic characteristics for various portions of a given patient-specific geometric model.
6. The computer-implemented method of claim 1 , wherein obtaining the patient's maximum allowable hemodynamic characteristics includes:
obtaining patient-specific exercise-induced physiological stress characteristics for the patient;
based on the exercise-induced physiological stress characteristics and the one or more patient specific parameters, determining the patient's maximum allowable hemodynamic characteristics.
7. The computer-implemented method of claim 6 , wherein:
determining the patient's maximum allowable hemodynamic characteristics includes inputting the patient-specific exercise-induced physiological stress characteristics and the one or more patient-specific parameters into a trained machine learning algorithm; and
the trained machine learning algorithm is obtained based on one or more patient-specific parameters of a vascular system from each of a plurality of individuals with known values of hemodynamic forces.
8. The computer-implemented method of claim 6 , wherein obtaining the patient-specific exercise-induced physiological stress characteristics includes performing an exercise test on the patient.
9. The computer-implemented method of claim 6 , wherein the patient-specific exercise-induced physiological stress characteristics are determined based on patient medical records.
10. The computer-implemented method of claim 1 , wherein the warning includes a signal provided by a mobile device associated with the patient and/or a physician.
11. The computer implemented method of claim 1 , wherein the one or more patient-specific parameter include one or more measured, derived, or obtained geometric characteristics, plaque characteristics, coronary dynamics characteristics, patient characteristics, or a combination thereof.
12. The computer implemented method of claim 1 , wherein a three-dimensional patient specific model of blood flow includes blood flow through one or more of: a coronary vascular model; a cerebral vascular model; a peripheral vascular model; a hepatic vascular model; a renal vascular model; a visceral vascular model; or any vascular model with vessels supplying blood.
13. The computer implemented method of claim 1 , wherein obtaining the one or more patient-specific parameters includes acquiring a digital representation of the one or more patient-specific parameters using imaging, scanning, or measuring modalities, including, one or more of,
cardiac computerized tomography,
mobile devices and/or smartphones,
intravascular ultrasound (IVUS) imaging,
optical coherence tomography (OCT),
pressure wire measurements,
motorized pull-back measurements,
or a combination thereof.
14. A system for monitoring hemodynamic risk in a patient, the system comprising:
at least one memory storing instructions; and
at least one processor operatively connected to the memory and configured to execute the instructions to perform operations, including:
acquiring a patient-specific geometric model of blood flow through at least the portion of the patient's vasculature;
obtaining current physiological and/or blood supply characteristics of the patient;
obtaining one or more patient-specific parameters of at least a portion of the patient's vasculature;
determining current hemodynamic characteristics of the patient based on the patient-specific geometric model, the current physiological and/or blood supply characteristics, and the one or more patient-specific parameters;
obtaining maximum allowable hemodynamic characteristics for the patient;
comparing the current hemodynamic characteristics with the maximum allowable hemodynamic characteristics;
determining, based on the comparison, whether the current hemodynamic characteristics include an abnormal value; and
in response to determining that the current hemodynamic characteristics include an abnormal value, producing a warning indicative of the abnormal value.
15. The computer-implemented method of claim 14 , wherein the current physiological and/or blood supply characteristics of the patient are acquired during exercise by the patient.
16. The computer-implemented method of claim 14 , wherein the current physiological and or blood supply characteristics of the patient are acquired by a mobile device associated with the patient.
17. The computer-implemented method of claim 14 , wherein the warning includes a signal provided by a mobile device associated with the patient and/or a physician.
18. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for monitoring hemodynamic risk in a patient, the method comprising:
acquiring a patient-specific geometric model of blood flow through at least the portion of the patient's vasculature;
obtaining current physiological and/or blood supply characteristics of the patient;
obtaining one or more patient-specific parameters of at least a portion of the patient's vasculature;
determining current hemodynamic characteristics of the patient based on the patient-specific geometric model, the current physiological and/or blood supply characteristics, and the one or more patient-specific parameters;
obtaining maximum allowable hemodynamic characteristics for the patient;
comparing the current hemodynamic characteristics with the maximum allowable hemodynamic characteristics;
determining, based on the comparison, whether the current hemodynamic characteristics include an abnormal value; and
in response to determining that the current hemodynamic characteristics include an abnormal value, producing a warning indicative of the abnormal value.
19. The non-transitory computer readable medium of claim 18 , wherein the current physiological and/or blood supply characteristics of the patient are acquired by a mobile device associated with the patient during exercise by the patient.
20. The computer-implemented method of claim 19 , wherein the warning includes a signal provided by the mobile device associated with the patient.