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

• US 9,855,105 B2
• Filed: 08/25/2016
• Issued: 01/02/2018
• Est. Priority Date: 08/12/2010
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: Taylor, Charles A.
• Primary Examiner(s): Rahmjoo, Manuchehr
• Application Number:
  US15/247,543
• Publication Number:
  US 20160364861A1
• Time in Patent Office: 495 Days
• 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 18/10 : Pre-processing; Data cleansing
• 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 for determining a blood flow characteristic of a patient, comprising:

receiving patient-specific image data of the patient at a first physiological state, the first physiological state comprising a state of hyperemia, a state of rest, or a state of exercise;

determining boundary conditions based on the patient-specific image data, the boundary conditions corresponding to the first physiological state;

performing a patient-specific computational fluid dynamics simulation of blood flow based on the boundary conditions;

determining a first value of a blood flow characteristic of the patient at the first physiological state based on the patient-specific computational fluid dynamics simulation of blood flow, the blood flow characteristic representing a medical characteristic of the patient;

determining features from the patient-specific image data, wherein the determined features are determined based on the blood flow characteristic determined for the patient; and

mapping the first value of the blood flow characteristic of the patient at the first physiological state to a second value of the blood flow characteristic of the patient at a second physiological state based on the determined features, the second physiological state comprising a state of hyperemia, a state of rest, or a state of exercise, the first physiological state being a different physiological state from the second physiological state.

2. The method as recited in claim 1, wherein the second physiological state is a simulated physiological state.

3. The method as recited in claim 1, wherein the blood flow characteristic of the patient at the first physiological state is a same blood flow characteristic as the second blood flow characteristic of the patient at the second physiological state.

4. The method as recited in claim 1, wherein the blood flow characteristic is an aortic blood pressure.

5. The method as recited in claim 1, wherein determining features from the patient-specific image data comprises:

processing the patient-specific image data of the patient to determine measurements of the patient.

6. An apparatus for determining a blood flow characteristic of a patient, comprising:

receiving patient-specific image data of the patient at a first physiological state, the first physiological state comprising a state of hyperemia, a state of rest, or a state of exercise;

determining boundary conditions based on the patient-specific image data, the boundary conditions corresponding to the first physiological state;

performing a patient-specific computational fluid dynamics simulation of blood flow based on the boundary conditions;

determining a first value of a blood flow characteristic of the patient at the first physiological state based on the patient-specific computational fluid dynamics simulation of blood flow, the blood flow characteristic representing a medical characteristic of the patient;

determining features from the patient-specific image data, wherein the determined features are determined based on the blood flow characteristic determined for the patient; and

mapping the first value of the blood flow characteristic of the patient at the first physiological state to a second value of the blood flow characteristic of the patient at a second physiological state based on the determined features, the second physiological state comprising a state of hyperemia, a state of rest, or a state of exercise, the first physiological state being a different physiological state from the second physiological state.

7. The apparatus as recited in claim 6, wherein the second physiological state is a simulated physiological state.

8. The apparatus as recited in claim 6, wherein the blood flow characteristic of the patient at the first physiological state is a same blood flow characteristic as the second blood flow characteristic of the patient at the second physiological state.

9. The apparatus as recited in claim 6, wherein the blood flow characteristic is an aortic blood pressure.

10. A non-transitory computer readable medium storing computer program instructions for determining a blood flow characteristic of a patient, the computer program instructions when executed by a processor cause the processor to perform operations comprising:

receiving patient-specific image data of the patient at a first physiological state, the first physiological state comprising a state of hyperemia, a state of rest, or a state of exercise;

determining boundary conditions based on the patient-specific image data, the boundary conditions corresponding to the first physiological state;

performing a patient-specific computational fluid dynamics simulation of blood flow based on the boundary conditions;

determining a first value of a blood flow characteristic of the patient at the first physiological state based on the patient-specific computational fluid dynamics simulation of blood flow, the blood flow characteristic representing a medical characteristic of the patient;

determining features from the patient-specific image data, wherein the determined features are determined based on the blood flow characteristic determined for the patient; and

mapping the first value of the blood flow characteristic of the patient at the first physiological state to a second value of the blood flow characteristic of the patient at a second physiological state based on the determined features, the second physiological state comprising a state of hyperemia, a state of rest, or a state of exercise, the first physiological state being a different physiological state from the second physiological state.

11. The non-transitory computer readable medium as recited in claim 10, wherein the second physiological state is a simulated physiological state.

12. The non-transitory computer readable medium as recited in claim 10, wherein the blood flow characteristic of the patient at the first physiological state is a same blood flow characteristic as the second blood flow characteristic of the patient at the second physiological state.

13. The non-transitory computer readable medium as recited in claim 10, wherein the blood flow characteristic is an aortic blood pressure.