Real-time cloud-based virtual fractional flow reserve estimation

Abstract

A system for estimating fractional flow reserve (FFR) includes a front end application to receive image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system. A graphics processing unit is configured to represent a computed concentration time profile in the vasculature system using a Lattice-Boltzmann Method (LBM) to generate a representation of the vasculature system. A dynamic controller tunes a velocity field based on a mismatch between the observed concentration time profile and the computed concentration time profile at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.

1 . A system for estimating fractional flow reserve (FFR), comprising:
 a front end application to receive image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system; and   a dynamic controller to tune a velocity field based on a mismatch between the observed concentration time profile and a computed concentration time profile of the vasculature system at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.   
     
     
         2 . The system as recited in  claim 1 , wherein the image frames include angiographic images from an x-ray system. 
     
     
         3 . The system as recited in  claim 1 , wherein the dynamic controller dynamically tunes boundary conditions of a simulation with a Lattice-Boltzmann Method (LBM) until no mismatch between observed and simulated data is achieved. 
     
     
         4 . The system as recited in  claim 4 , wherein the boundary conditions are dynamically adjusted to minimize a cost function. 
     
     
         5 . The system as recited in  claim 1 , wherein the dynamic controller dynamically tunes a velocity flow field to use the mismatch between the observed and computed concentration profiles to update the velocity flow field directly. 
     
     
         6 . The system as recited in  claim 1 , wherein the model of the vasculature is represented as a lattice with lattice nodes. 
     
     
         7 . The system as recited in  claim 1 , wherein the observed time profile of concentration is measured using a thrombolysis in myocardial infarction (TIMI) frame count, which counts a number of cineframes needed for the contrast dye material to reach a landmark. 
     
     
         8 . The system as recited in  claim 1 , wherein the simulated time profile of concentration includes a spatially and temporally varying velocity field and concentration field, where an input time concentration profile matches that which a clinician employed when acquiring the image frames. 
     
     
         9 . The system as recited in  claim 1 , wherein the FFR measurement is provided as a service in a cloud environment. 
     
     
         10 . A method for estimating fractional flow reserve (FFR), comprising:
 receiving image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system; and   tuning a velocity field based on a mismatch between the observed concentration time profile and a computed concentration time profile of the vasculature system at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.   
     
     
         11 . The method as recited in  claim 10 , wherein the image frames include angiographic images from an x-ray system. 
     
     
         12 . The method as recited in  claim 10 , wherein tuning includes dynamically tuning boundary conditions of a simulation with a Lattice-Boltzmann Method (LBM) until no mismatch between observed and simulated data is achieved. 
     
     
         13 . The method as recited in  claim 12 , wherein the boundary conditions are dynamically adjusted to minimize a cost function. 
     
     
         14 . The method as recited in  claim 10 , wherein tuning includes dynamically tuning a velocity flow field to use the mismatch between the observed and computed concentration profiles to update the velocity flow field directly. 
     
     
         15 . The method as recited in  claim 10 , wherein the observed time profile of concentration is measured using a thrombolysis in myocardial infarction (TIMI) frame count, which counts a number of cineframes needed for the contrast dye material to reach a landmark. 
     
     
         16 . The method as recited in  claim 10 , wherein the simulated time profile of concentration includes a spatially and temporally varying velocity field and concentration field, where an input time concentration profile matches that which a clinician employed when acquiring the image frames. 
     
     
         17 . The method as recited in  claim 10 , wherein the FFR measurement is provided as a real-time service in a cloud environment. 
     
     
         18 . A non-transitory computer readable storage medium comprising a computer readable program for estimating fractional flow reserve (FFR), wherein the computer readable program when executed on a computer causes the computer to perform steps of:
 receiving image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system; and   tuning a velocity field based on a mismatch between the observed concentration time profile and a computed concentration time profile of the vasculature system at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.   
     
     
         19 . The non-transitory computer readable storage medium as recited in  claim 17 , wherein tuning includes dynamically tuning boundary conditions of a simulation with a Lattice-Boltzmann Method (LBM) until no mismatch between observed and simulated data is achieved. 
     
     
         20 . The non-transitory computer readable storage medium as recited in  claim 17 , wherein tuning includes dynamically tuning a velocity flow field to use the mismatch between the observed and computed concentration profiles to update the velocity flow field directly.