Method and system for image processing to determine patient-specific blood flow characteristics

• US 9,743,835 B2
• Filed: 12/30/2015
• Issued: 08/29/2017
• Est. Priority Date: 08/12/2010
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: Taylor, Charles A.
• Primary Examiner(s): Heidemann, Jason
• Application Number:
  US14/984,018
• Publication Number:
  US 20160110866A1
• Time in Patent Office: 608 Days
• Field of Search: None
• CPC Class Codes: A61B 2034/104 Modelling the effect of the tool, e.g. the effect of an implanted prosthesis or for predicting the effect of ablation or burring
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 2034/107 Visualisation of planned trajectories or target regions
• CPC Class Codes: A61B 2034/108 Computer aided selection or customisation of medical implants or cutting guides
• CPC Class Codes: A61B 2090/374 NMR or MRI
• CPC Class Codes: A61B 2090/3762 using computed tomography systems [CT]
• CPC Class Codes: A61B 2090/3764 with a rotating C-arm having a cone beam emitting source
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• CPC Class Codes: A61B 2576/023 for the heart
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 34/25 User interfaces for surgical systems
• CPC Class Codes: A61B 5/0035 adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
• CPC Class Codes: A61B 5/004 adapted for image acquisition of a particular organ or body part A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062
• CPC Class Codes: A61B 5/0044 for the heart
• CPC Class Codes: A61B 5/02 Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0263 : using NMR
• CPC Class Codes: A61B 5/029 : Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/055 : involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/1075 : for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/1118 : Determining activity level
• CPC Class Codes: A61B 5/22 : Ergometry; Measuring muscular strength or the force of a muscular blow exercising apparatus A63B21/00 ; measuring of work or force in general G01L
• CPC Class Codes: A61B 5/4848 : Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/6852 : Catheters
• CPC Class Codes: A61B 5/6868 : Brain
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/745 : using a holographic display
• CPC Class Codes: A61B 6/03 : Computed tomography [CT] echo-tomography A61B8/14
• CPC Class Codes: A61B 6/032 : Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/481 : involving the use of contrast agents
• CPC Class Codes: A61B 6/503 : for diagnosis of the heart
• CPC Class Codes: A61B 6/504 : for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/507 : for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: A61B 6/5205 : involving processing of raw data to produce diagnostic data
• CPC Class Codes: A61B 6/5217 : extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/5229 : combining image data of a patient, e.g. combining a functional image with an anatomical image
• CPC Class Codes: A61B 8/02 : Measuring pulse or heart rate
• CPC Class Codes: A61B 8/04 : Measuring blood pressure
• CPC Class Codes: A61B 8/06 : Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/065 : to determine blood output from the heart in general A61B5/029
• CPC Class Codes: A61B 8/481 : involving the use of contrast agent, e.g. microbubbles introduced into the bloodstream analysis of echo signal exploiting properties of a contrast enhancer in devices for short-range imaging with acoustic waves G01S7/52039
• CPC Class Codes: A61B 8/5223 : for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/5261 : combining images from different diagnostic modalities, e.g. ultrasound and X-ray
• CPC Class Codes: A61M 5/007 : for contrast media
• CPC Class Codes: G01R 33/5601 : involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
• CPC Class Codes: G01R 33/5635 : Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA]
• CPC Class Codes: G01R 33/56366 : Perfusion imaging
• CPC Class Codes: G06F 17/10 : Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 : Classification techniques
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 30/23 : using finite element methods [FEM] or finite difference methods [FDM]
• CPC Class Codes: G06F 30/28 : using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
• CPC Class Codes: G06G 7/60 : for living beings, e.g. their nervous systems ; for problems in the medical field
• CPC Class Codes: G06T 11/00 : 2D [Two Dimensional] image generation
• CPC Class Codes: G06T 11/001 : Texturing; Colouring; Generation of texture or colour inpainting G06T5/77
• CPC Class Codes: G06T 11/008 : Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 11/20 : Drawing from basic elements, e.g. lines or circles
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 15/10 : Geometric effects
• CPC Class Codes: G06T 17/00 : Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 17/005 : Tree description, e.g. octree, quadtree
• CPC Class Codes: G06T 17/20 : Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 2200/04 : involving 3D image data
• CPC Class Codes: G06T 2207/10012 : Stereo images
• CPC Class Codes: G06T 2207/10072 : Tomographic images
• CPC Class Codes: G06T 2207/10081 : Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 : Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10108 : Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/20036 : Morphological image processing
• CPC Class Codes: G06T 2207/20124 : Active shape model [ASM]
• CPC Class Codes: G06T 2207/30016 : Brain
• CPC Class Codes: G06T 2207/30048 : Heart; Cardiac
• CPC Class Codes: G06T 2207/30104 : Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 2211/404 : Angiography
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/0014 : using an image reference approach
• CPC Class Codes: G06T 7/10 : Segmentation; Edge detection motion-based segmentation G06T7/215
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/12 : Edge-based segmentation
• CPC Class Codes: G06T 7/13 : Edge detection
• CPC Class Codes: G06T 7/149 : involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/60 : Analysis of geometric attributes
• CPC Class Codes: G06T 7/62 : of area, perimeter, diameter or volume
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/73 : using feature-based methods
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/40 : Extraction of image or video features
• CPC Class Codes: G06V 10/42 : Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
• CPC Class Codes: G06V 10/44 : Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• CPC Class Codes: G06V 10/467 : Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 20/698 : Matching; Classification
• CPC Class Codes: G16B 45/00 : ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 : ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16H 10/40 : for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 : for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/00 : ICT specially adapted for the handling or processing of medical references
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient'"'"'s heart, and create a three-dimensional model representing at least a portion of the patient'"'"'s heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient'"'"'s heart and determine a fractional flow reserve within the patient'"'"'s heart based on the three-dimensional model and the physics-based model.

Claims

1. A method of image processing to assess vascular function of a cardiac vasculature having at least one stenotic segment, the method comprising:

receiving, by image processing circuitry, a first vascular model modeling the cardiac vasculature;

determining at least one characteristic of flow through the stenotic segment in the first vascular model;

modifying the first vascular model, to produce a difference in the characteristic of flow for the modified vascular model;

determining the characteristic of flow for the modified vascular model corresponding to the characteristic of flow for the first vascular model;

determining a flow index quantifying the vascular function of the cardiac vasculature modeled by the first vascular model, wherein the flow index is based on a ratio of the corresponding flow characteristics of the first and modified vascular models; and

if the determined flow index is below a predetermined threshold, determining and outputting, to an electronic storage medium or display, indicia of one or more locations for interventional or surgical revascularization treatment options based on the determined flow index.

2. The method of claim 1, wherein the at least one characteristic of flow comprises a flow rate.

3. The method of claim 2, wherein the flow index comprises an index representing a Fractional Flow Reserve index comprising a ratio of the maximal flow through a stenotic vessel, to the maximal flow through the stenotic vessel with the stenosis removed.

4. The method of claim 1, wherein the first and modified vascular models comprise connected branches of vascular segment data, each branch being associated with a corresponding vascular resistance to flow.

5. The method of claim 1, wherein the modified vascular model comprises a relatively enlarged-diameter vessel replacing the at least one stenotic segment in the first vascular model.

6. The method of claim 1, wherein the modified vascular model comprises a normalized vessel obtained by normalizing the stenotic segment.

7. The method of claim 1, wherein the at least one characteristic of flow is calculated based on properties of a plurality of vascular segments in flowing connection with the at least one stenotic segment.

8. The method of claim 1, further comprising:

identifying in the first vascular model the stenosed vessel and downstream vascular branches of the stenosed vessel, and calculating the resistance to fluid flow in the downstream vascular branches;

wherein the flow index is calculated based on a volume of the downstream vascular branches, and on a contribution of the stenosed vessel to the resistance to fluid flow.

9. The method of claim 1, wherein the first vascular model comprises paths along vascular segments of the cardiac vasculature, each of the paths being mapped along its extent to positions in the plurality of 2-D images.

10. A computer software product, comprising a non-transitory computer-readable medium in which program instructions are stored, which, when read by a computer, cause the computer to execute the method of claim 1.

11. A system for image processing to assess vascular function of a cardiac vasculature having a stenotic segment, the system comprising a computer configured to:

receive a plurality of 2-D images of the cardiac vasculature;

convert the plurality of 2-D images to a first vascular model modeling the cardiac vasculature;

determine at least one characteristic of flow in the first vascular model through the stenotic segment;

modify the first vascular model, to alter the at least one characteristic of flow;

determine the characteristic of flow for the modified vascular model corresponding to the characteristic of flow for the first vascular model;

determine a flow index quantifying the vascular function of the cardiac vasculature modeled by the first vascular model, wherein the flow index is based on a ratio of the corresponding flow characteristics of the first and modified vascular models; and

if the determined flow index is below a predetermined threshold, determine and output, to an electronic storage medium or display, indicia of one or more locations for interventional or surgical revascularization treatment options based on the determined flow index.