Hemodynamic and morphological predictors of vascular graft failure
A non-invasive method to predict post-surgery vascular graft failure is provided. Computer Tomography Angiography (CTA) images are obtained of a patient post-surgery. A personalized three-dimensional computer model of the patient is derived from the obtained CTA images. The personalized three-dimensional computer model distinguishes a Computational Fluid Dynamics (CFD) model coupled with a closed-loop Lumped Parameter Network (LPN). Post-surgery vascular graft predictors are calculated from the personalized three-dimensional computer model indicative, i.e. predictors, of the post-surgery vascular graft failure or vascular stenosis.
1. A method of non-invasively predicting post-surgery vascular graft failure, comprising:
(a) obtaining Computer Tomography Angiography (CTA) images of a patient post-surgery, wherein the surgery included a vascular graft, and wherein the images comprise at least the vascular graft of the patient; and
(b) deriving from the obtained CTA images a personalized three-dimensional computer model of the patient, wherein the personalized three-dimensional computer model comprising at least the vascular graft, wherein the personalized three-dimensional computer model comprises a Computational Fluid Dynamics (CFD) model coupled with a closed-loop Lumped Parameter Network (LPN);
(c) calculating one or more vascular graft predictors from the personalized three-dimensional computer model, wherein the one or more vascular graft predictors are indicative of the post-surgery vascular graft failure or vascular stenosis; and
(d) outputting the one or more vascular graft predictors.
2. The method as set forth in claim 1 , wherein the Computational Fluid Dynamics (CFD) model represents a coronary anatomy, an aortic anatomy and hemodynamic profiles.
3. The method as set forth in claim 1 , wherein the closed-loop Lumped Parameter Network (LPN) models a coronary physiology.
4. The method as set forth in claim 1 , wherein the closed-loop Lumped Parameter Network (LPN) represents a physiology of the four heart chambers of the patient, a systemic circulation and a coronary circulation.
5. The method as set forth in claim 1 , wherein the closed-loop Lumped Parameter Network (LPN) models the out-of-phase behavior of the coronary versus the systemic circulation.
6. The method as set forth in claim 1 , further comprising matching parameters of the closed-loop Lumped Parameter Network (LPN) model to match standard-of-care invasive clinical measurements, wherein the including clinical measurements are a cardiac output, a heart rate, a systolic blood pressure, a diastolic blood pressure, a left ventricle ejection fraction, or any combination thereof.
7. The method as set forth in claim 1 , wherein the vascular graft is a vein graft.
8. The method as set forth in claim 1 , wherein the vascular graft is a saphenous vein graft.
9. The method as set forth in claim 1 , wherein the surgery is a Coronary Artery Bypass Graft (CABG) surgery.
10. The method as set forth in claim 1 , wherein the one or more vascular graft predictors is a wall shear stress of the vascular graft.
11. The method as set forth in claim 1 , wherein the one or more vascular graft predictors is a normalized wall shear stress of the vascular graft.
12. The method as set forth in claim 1 , wherein the one or more vascular graft predictors is a curvature of the vascular graft.
13. The method as set forth in claim 1 , wherein the one or more vascular graft predictors predict and output adequacy of the vascular graft post-surgery.
14. The method as set forth in claim 1 , wherein the one or more vascular graft predictors are hemodynamic predictors, wherein the hemodynamic predictors are an oscillatory shear index, a low shear area, a flow rate or a velocity of the vascular graft.
15. The method as set forth in claim 1 , wherein the one or more vascular graft predictors are anatomic predictors, wherein the anatomic predictors are a tortuosity, a length, an area or an area ratio of the vascular graft.
16. The method as set forth in claim 1 , wherein the method is a computer processing pipeline executed in an automatic fashion by a computer processor.