GPU Lattice-Boltzmann FFR Estimation from Angiograms

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Solution Overview

Problem

Current methods for estimating fractional flow reserve (FFR) in coronary arteries are inefficient due to complex workflows, long turnaround times, and the need for patient-specific boundary conditions, which are not readily available, making real-time FFR estimation challenging in clinical settings.

Innovation Solution

A system utilizing a Lattice-Boltzmann Method (LBM) on a Graphics Processing Unit (GPU) to generate a representation of the vasculature system from angiographic images, with a dynamic controller tuning the velocity field based on observed and computed concentration time profiles to estimate FFR, allowing for real-time computation and automated mesh generation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional CFD simulation is used to estimate FFR, then measurement precision is improved, but simulation time and workflow complexity increase significantly

Engineering Contradiction:

ImproveFFR estimation accuracyVSAvoidSimulation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent changes the mathematical model from Navier-Stokes equations to Lattice-Boltzmann Method, which uses different parameters and assumptions to represent blood flow. This parameter change enables real-time computation while maintaining clinical accuracy for FFR estimation.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the traditional mechanical CFD simulation system with a cloud-based LBM system that processes angiographic images directly. This substitution eliminates the need for complex mesh generation and traditional CFD solvers, achieving real-time results.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If patient-specific boundary conditions are specified to obtain unique Navier-Stokes solutions, then measurement precision is improved, but device complexity and workflow difficulty increase

Engineering Contradiction:

ImproveFlow field accuracyVSAvoidBoundary condition specification complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system automatically extracts boundary conditions from the angiographic images themselves without requiring external input. The LBM simulation self-determines the necessary parameters by analyzing the contrast dye distribution and flow patterns directly from the imaging data, eliminating manual boundary condition specification.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces contrast dye concentration as an intermediary parameter that links the imaging data to the flow field calculation. By using the dye concentration profiles as boundary conditions instead of traditional pressure or flow rate measurements, the system simplifies the workflow while maintaining accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If automated mesh generation is implemented, then ease of operation is improved, but manufacturing precision and model accuracy may be compromised

Engineering Contradiction:

ImproveWorkflow automationVSAvoidComputational mesh accuracy

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent creates a digital copy of the vascular geometry directly from the angiographic images using image processing algorithms. This copying process automatically generates the computational mesh without manual intervention, maintaining geometric accuracy while fully automating the workflow.

Inventive Principle:

Principle #26Copying

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach significantly reduces simulation time and automates the specification of boundary conditions, enabling fast and accurate real-time estimation of FFR from x-ray angiograms without the need for additional pressure or flow rate measurements, improving clinical efficiency.

Implementation Method 1

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

Methodology Applied

Scientific EffectLattice-Boltzmann Method:

Implementation Method 2

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

Methodology Applied

Scientific EffectConcentration time profile comparison:

Data Source

PatentUS20200037978A1Real-time cloud-based virtual fractional flow reserve estimation

Publication Date: 2020.02.06 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

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.