# US 10,482,215 B2 · Unified computational method and system for patient-specific hemodynamics

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- Number: US 10,482,215 B2
- Document ID: US10482215B2
- Status: Granted
- Kind: B2
- Application number: 15/520,283
- Filed: 2015-10-22
- Published: 2017-11-23
- Granted: 2019-11-19
- Earliest claimed: 2014-10-22
- Assignees: Indiana University Research and Technology Corporation; Kent State University
- Applicants: Indiana University Research and Technology Corporation; Kent State University
- Inventors: Huidan Yu; Chen Lin; Ye Zhao
- Web page: https://patentgrep.com/patent/US10482215B2

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06T 7/11: Region-based segmentation
- G06T 7/12: Edge-based segmentation
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- A61B 5/0033: {Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room}
- A61B 6/032: {Transmission computed tomography [CT]}
- A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT}
- A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data}
- A61B 8/06: Measuring blood flow
- A61B 8/488: {involving Doppler signals}
- A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)}
- A61B 2576/02: specially adapted for a particular organ or body part
- G06F 17/11: for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06T 2200/28: involving image processing hardware
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/10072: Tomographic images
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/10136: 3D ultrasound image
- G06T 2207/20161: Level set
- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- G16H 50/50: for simulation or modelling of medical disorders
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method for computing patient-specific hemodynamics. The method includes receiving three dimensional imaging data of a patent, extracting anatomical data from the three dimensional imaging data, calculating velocity and pressure fields corresponding to the extracted anatomical data, and calculating displacement and velocity of extracted solid particles corresponding to the anatomical data. The anatomical data comprises an anatomical boundary.

## Claims

### Claim 1

A method of non-invasively quantifying in vivo blood flow and flow-artery interaction in an artery, comprising:
receiving image data of anatomical features of the artery;
processing the image data on a GPU parallel-computation framework employing mesoscale models; and
causing display of information representing the flow-artery interaction;
wherein processing comprises
using a simplified lattice Boltzmann method (“SLBM”) to model anatomical segmentation of the artery,
using a volumetric lattice Boltzmann method (“VLBM”) to model fluid dynamics of the flow-artery interaction,
using a lattice spring method (“LSM”) to model structure mechanics of the flow-artery interaction, and
using the SLBM to model interface tracking of the flow-artery interaction.

### Claim 2 (depends on 1)

The method of claim 1 wherein receiving image data comprises receiving image data from at least one of a CT scanner, an MRI imager and an ultrasound Doppler imager.

### Claim 3 (depends on 1)

The method of claim 1 wherein displaying information representing the flow-artery interaction comprises displaying at least one of a 3D blood flow velocity map and a wall shear stress map.

### Claim 4 (depends on 1)

The method of claim 1 wherein the GPU parallel-computation framework is a GPU-equipped workstation.

### Claim 5 (depends on 1)

The method of claim 1 wherein using an SLBM includes performing a collision calculation, performing a streaming calculation and updating a distance function.

### Claim 6 (depends on 5)

The method of claim 5 wherein performing a collision calculation comprises solving f′i({right arrow over (r)},t)=fi({right arrow over (r)},t)−[fi({right arrow over (r)},t)−fieq({right arrow over (r)},t)]/τ+FiΔt.

### Claim 7 (depends on 5)

The method of claim 5 wherein performing a streaming calculation comprises solving fi({right arrow over (r)}+{right arrow over (e)}iΔt,t+Δt)=f′i({right arrow over (r)},t).

### Claim 8 (depends on 5)

The method of claim 5 wherein updating a distance function comprises calculating φ=Σifi.

### Claim 9 (depends on 1)

The method of claim 1 wherein using a VLBM includes performing a collision calculation and performing a streaming calculation.

### Claim 10 (depends on 9)

The method of claim 9 wherein performing a collision calculation comprises solving n′i({right arrow over (r)},t)=ni({right arrow over (r)},t)−[ni({right arrow over (r)},t)−nieq({right arrow over (r)},t)]/τ.

### Claim 11 (depends on 9)

The method of claim 9 wherein performing a streaming calculation comprises solving
ni({right arrow over (r)}+{right arrow over (e)}iΔt,t+Δt)=n′i({right arrow over (r)},t).

### Claim 12 (depends on 9)

The method of claim 9 wherein using a VLBM includes determining boundary induced fluid migration by solving N=Σini, N{right arrow over (u)}=Σini{right arrow over (e)}i.