Planning and guidance of electrophysiology therapies
A processor acquires image data from a medical imaging system. The processor generates a first model from the image data. The processor generates a computational model which includes cardiac electrophysiology and cardiac mechanics estimated from the first model. The processor performs tests on the computational model to determine outcomes for therapies. The processor overlays the outcome on an interventional image. Using interventional imaging, the first heart model may be updated/overlaid during the therapy to visualize its effect on a patient's heart.
1. A method comprising:
acquiring, from a medical imaging system, image data representing at least one chamber of a patient's heart;
generating, using a processor, a first model from the image data, wherein the first model includes a structural model, cardiac motion, and cardiac flow;
generating, using the processor, a computational model, wherein the computational model includes cardiac electrophysiology, cardiac biomechanics, and cardiac hemodynamics estimated from the first model;
generating a virtual model including the computational model rendered as a 3D volumetric model;
placing at least one left ventricle lead on the virtual model;
placing at least one right ventricle lead on the virtual model;
applying an electrical stimulus at least one of the left ventricle lead and right ventricle lead;
simulating a response of the virtual model;
generating an outcome map of the response; and
displaying the outcome map in features of the virtual model.
2. The method of claim 1 wherein the image data is one or more 3D ultrasound images.
3. The method of claim 1 wherein generating a first model comprises:
creating the structural model of the patient's heart using the image data;
estimating the cardiac motion from the image data;
quantifying the cardiac flow from the image data; and
mapping the cardiac motion and cardiac flow to the structural model.
4. The method of claim 3 , wherein creating a structural model comprises:
segmenting the image data into one or more chambers of the patient's heart;
creating a mesh model for each chamber using the segmented image data; and
fusing the mesh models together.
5. The method of claim 1 , further comprising:
acquiring, from an interventional imaging system, an interventional image;
registering the outcome map to the interventional imaging system;
registering the first model to the interventional imaging system; and
overlaying at least one of the first model, the computational model, or the outcome map on the interventional image.
6. The method of claim 5 , further comprising:
acquiring real time data from the interventional imaging system; and
updating the first model and virtual model with the real time data.
7. The method of claim 1 , further comprising:
acquiring electrophysiological data from an electrophysiology system, wherein the electrophysiological data is used to estimate the parameters of the cardiac electrophysiology model.
8. The method of claim 1 , wherein the cardiac electrophysiology is computed using a Lattice-Boltzmann method.
9. The method of claim 1 , wherein cardiac hemodynamics is computed using a lumped parameter model coupled with the electromechanical model.
10. The method of claim 1 , further comprising:
acquiring, from a medical imaging system, updated image data that covers the at least one chamber of a patient's heart; and
generating an updated first heart model.
11. A method comprising:
acquiring, from a medical imaging system, first data representing a cardiac structure of a patient;
deriving, from the first data, flow data representing cardiac flow over time;
deriving, from the first data, motion data representing cardiac motion over time;
generating a unified heart model from the first data, flow data, and motion data;
acquiring, from an interventional imaging system, second data representing real-time cardiac structure of the patient;
overlaying the unified heart model on the second data;
generating a virtual model including the unified heart model rendered as a 3D volumetric model;
placing at least one left ventricle lead on the virtual model;
placing at least one right ventricle lead on the virtual model;
applying an electrical stimulus at least one of the left ventricle lead and right ventricle lead;
simulating a response of the virtual model;
generating an outcome map of the response; and
displaying the outcome map in features of the virtual model.
12. The method of claim 11 , wherein the second data is magnetic resonance image data.