Systems and methods for virtual contrast agent simulation and computational fluid dynamics (CFD) to compute functional significance of stenoses
Systems and methods are disclosed for assessing a risk of disease. One method includes obtaining an anatomic model associated with a target anatomy; modeling, using a processor, an injection of one or more virtual contrast agents into the anatomic model; performing a simulation of flow of blood and the one or more virtual contrast agents through the anatomic model; and computing one or more characteristics of concentration associated with the one or more virtual contrast agents at one or more locations in the anatomic model based on the simulation.
1. A computer-implemented method of determining blood flow metrics, the method comprising:
receiving image data of a patient's vasculature;
identifying, from the image data, one or more voxels and/or pixels associated with the patient's vasculature and identifying a region of interest of the image data;
generating, based on the identified voxels and/or pixels and the region of interest, a patient-specific geometric model comprising a reconstruction of a patient's vasculature;
determining a patient-specific computational model of blood flow using the patient-specific geometric model obtained from the received image data, the patient-specific computational model of blood flow being different from the patient-specific geometric model;
determining at least one simulated patient-specific blood flow metric based on the patient-specific computational model of blood flow and the patient-specific geometric model, wherein determining at least one simulated patient-specific blood flow metric comprises discretizing the patient's vasculature of interest into finite elements based on 1) measured aortic pressure at an aortic inlet of the computational model and 2) calculated coronary resistances;
determining whether the simulated patient-specific blood flow metric is within a range between a non-functionally-significant blood flow metric value and a functionally significant blood flow metric value;
modeling, based on the patient-specific computational model of blood flow in the patient-specific geometric model and at least one of patient temperature and/or patient rheological properties of blood, a diffusivity of one or more virtual contrast agents in blood at a plurality of predetermined points in the patient-specific geometric model;
upon determining the simulated patient-specific blood flow metric is within a range between a non-functionally-significant blood flow metric value and a functionally significant blood flow metric value, determining one or more software simulated transluminal attenuation gradient (TAG) metrics, based on the diffusivity of the one or more simulated virtual contrast agents in blood in the patient-specific geometric, wherein determining one or more software simulated transluminal attenuation gradient (TAG) metrics comprises: assigning a known value of virtual contrast concentration near a source location and assigning values of virtual contrast concentration along lesions of the patient's vasculature, calculating transluminal gradients using a linear regression of the virtual contrast concentration along the lesions of the patient's vasculature, and applying a scaling factor to the virtual concentration gradient to determine a TAG metric score;
providing the simulated TAG metrics and the simulated patient-specific blood flow metric for output, and
determining a risk of heart disease for a patient using the determined TAG metric score and a threshold TAG score, the threshold TAG score being calculated based on a database of patients with measured blood flow metrics.
2. The method of claim 1 , further including:
determining a diagnostic flow metric.
3. The method of claim 2 , wherein the diagnostic flow metric is fractional flow reserve.
4. The method of claim 1 , further including:
determining a landmark of the patient-specific geometric model;
determining a time elapsed for the one or more virtual contrast agents to attenuate or reach the landmark; and
determining a functional significance of a stenosis based on the time elapsed.
5. The method of claim 1 , further including:
determining a local gradient across a lumen centerline based on the diffusivity of one or more virtual contrast agents.
6. The method of claim 1 , wherein a value of simulated patient-specific blood flow metric values below 0.7 indicates functionally significant simulated patient-specific blood flow metric values, and wherein a value of simulated patient-specific blood flow metric values above 0.9 indicates non-functionally significant simulated patient-specific blood flow metric values.
7. The method of claim 1 , wherein a value of simulated patient-specific blood flow metric values below 0.75 indicates functionally significant simulated patient-specific blood flow metric values, and wherein a value of simulated patient-specific blood flow metric values above 0.85 indicates non-functionally significant simulated patient-specific blood flow metric values.
8. A system for determining blood flow metrics, the system comprising:
at least one data storage device storing instructions for assessing risk of disease; and
at least one processor configured to execute the instructions to perform a method including:
receiving image data of a patient's vasculature;
identifying, from the image data, one or more voxels and/or pixels associated with the patient's vasculature and identifying a region of interest of the image data;
generating, based on the identified voxels and/or pixels and the region of interest, a patient-specific geometric model comprising a reconstruction of a patient's vasculature;
determining a patient-specific computational model of blood flow using the patient-specific geometric model obtained from the received image data, the patient-specific computational model of blood flow being different from the patient-specific geometric model;
determining at least one simulated patient-specific blood flow metric based on the patient-specific computational model of blood flow and the patient-specific geometric model, wherein determining at least one simulated patient-specific blood flow metric comprises discretizing the patient's vasculature of interest into finite elements based on, 1) measured aortic pressure at an aortic inlet of the computational model and 2) calculated coronary resistances;
determining whether the simulated patient-specific blood flow metric is within a range between a non-functionally-significant blood flow metric value and a functionally significant blood flow metric value;
modeling, based on the patient-specific computational model of blood flow in the patient-specific geometric model and at least one of patient temperature and/or patient rheological properties of blood, a diffusivity of one or more virtual contrast agents in blood at a plurality of predetermined points in the patient-specific geometric model;
based on determining the simulated patient-specific blood flow metric is within a range between a non-functionally-significant blood flow metric value and a functionally significant blood flow metric value, determining one or more software simulated transluminal attenuation gradient (TAG) metrics, based on the diffusivity of the one or more simulated virtual contrast agents in blood in the patient-specific geometric model, wherein determining one or more simulated transluminal attenuation gradient (TAG) metrics comprises: assigning a known value of virtual contrast concentration near a source location and assigning values of virtual contrast concentration along lesions of the patient's vasculature, calculating transluminal gradients using a linear regression of the virtual contrast concentration along the lesions of the patient's vasculature, and applying a scaling factor to the virtual concentration gradient to determine a TAG metric score;
providing the simulated TAG metrics and the simulated patient-specific blood flow metric for output, and
determining a risk of heart disease for a patient using the determined TAG metric score and a threshold TAG score, the threshold TAG score being calculated based on a database of patients with measured blood flow metrics.
9. The system of claim 8 , wherein the at least one processor is further configured for:
determining a diagnostic flow metric.
10. The system of claim 9 , wherein the diagnostic flow metric is fractional flow reserve.
11. The system of claim 8 , wherein the at least one processor is further configured for:
determining a landmark of the patient-specific geometric model;
determining a time elapsed for the one or more virtual contrast agents to attenuate or reach the landmark; and
determining a functional significance of a stenosis based on the time elapsed.
12. The system of claim 8 , wherein the at least one processor is further configured for:
determining a local gradient across a lumen centerline based on the diffusivity of one or more virtual contrast agents.
13. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for determining blood flow metrics, the programming instructions, when executed by the computer system, performing a method comprising:
receiving image data of a patient's vasculature;
identifying, from the image data, one or more voxels and/or pixels associated with the patient's vasculature and identifying a region of interest of the image data;
generating, based on the identified voxels and/or pixels and the region of interest, a patient-specific geometric model comprising a reconstruction of a patient's vasculature;
determining a patient-specific computational model of blood flow using the patient-specific geometric model obtained from the received image data, the patient-specific computational model of blood flow being different from the patient-specific geometric model;
determining at least one simulated patient-specific blood flow metric based on the patient-specific computational model of blood flow and the patient-specific geometric model, wherein determining at least one simulated patient-specific blood flow metric comprises discretizing the patient's vasculature of interest into finite elements based on, 1) measured aortic pressure at an aortic inlet of the computational model and 2) calculated coronary resistances;
determining whether the simulated patient-specific blood flow metric is within a range between a non-functionally-significant blood flow metric value and a functionally significant blood flow metric value;
modeling, based on the patient-specific computational model of blood flow in the patient-specific geometric model and at least one of patient temperature and/or patient rheological properties of blood, a diffusivity of one or more virtual contrast agents in blood at a plurality of predetermined points in the patient-specific geometric model;
upon determining the simulated patient-specific blood flow metric is within a range between a non-functionally-significant blood flow metric value and a functionally significant blood flow metric value, determining one or more software simulated transluminal attenuation gradient (TAG) metrics, based on the diffusivity of the one or more simulated virtual contrast agents in blood in the patient-specific geometric model, wherein determining one or more simulated transluminal attenuation gradient (TAG) metrics comprises: assigning a known value of virtual contrast concentration near a source location and assigning values of virtual contrast concentration along lesions of the patient's vasculature, calculating transluminal gradients using a linear regression of the virtual contrast concentration along the lesions of the patient's vasculature, and applying a scaling factor to the virtual concentration gradient to determine a TAG metric score;
providing the simulated TAG metrics and the simulated patient-specific blood flow metric for output, and
determining a risk of heart disease for a patient using the determined TAG metric score and a threshold TAG score, the threshold TAG score being calculated based on a database of patients with measured blood flow metrics.
14. The non-transitory computer readable medium of claim 13 , the method further comprising:
determining a diagnostic flow metric.
15. The non-transitory computer readable medium of claim 14 , wherein the diagnostic flow metric is fractional flow reserve.
16. The non-transitory computer readable medium of claim 13 , the method further comprising:
determining a landmark of the patient-specific geometric model;
determining a time elapsed for the one or more virtual contrast agents to attenuate or reach the landmark; and
determining a functional significance of a stenosis based on the time elapsed.
17. The non-transitory computer readable medium of claim 13 , the method further comprising:
determining a local gradient across a lumen centerline based on the diffusivity of one or more virtual contrast agents.