Methods and systems for assessing image quality in modeling of patient anatomic or blood flow characteristics

• US 9,836,840 B2
• Filed: 06/17/2016
• Issued: 12/05/2017
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: Fonte, Timothy A., Grady, Leo, Wu, Zhongle, Schaap, Michiel, Hunley, Stanley C., Sengupta, Souma
• Primary Examiner(s): Harandi, Siamak
• Application Number:
  US15/185,668
• Publication Number:
  US 20160300349A1
• Time in Patent Office: 536 Days
• CPC Class Codes: A61B 2576/02 specially adapted for a particular organ or body part
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/0263 using NMR
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• 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 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/5258 involving detection or reduction of artifacts or noise
• CPC Class Codes: A61B 6/5294 involving using additional data, e.g. patient information, image labeling, acquisition parameters
• CPC Class Codes: A61B 8/0883 for diagnosis of the heart
• 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: G01R 33/5635 Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA]
• 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 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/30004 : Biomedical image processing
• CPC Class Codes: G06T 2207/30104 : Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 2207/30168 : Image quality inspection
• CPC Class Codes: G06T 2207/30172 : Centreline of tubular or elongated structure
• CPC Class Codes: G06T 2207/30196 : Human being; Person
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• 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

Abstract

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient'"'"'s anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient'"'"'s anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient'"'"'s anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient'"'"'s anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Claims

1. A method for automatically selecting image(s) to produce model(s) for a patient-specific simulation of blood flow, using a computer system, the method comprising:

receiving one or more characteristics of accuracy, precision, or performance for a simulation of a patient'"'"'s blood flow;

receiving one or more images of at least a portion of a patient'"'"'s anatomy;

receiving an image quality score associated with the one or more images; and

selecting, based on the received image quality score and the received one or more characteristics of accuracy, precision, or performance in performing the simulation, an image of the one or more received images to produce a model for use in the simulation of the patient'"'"'s blood flow.

2. The method of claim 1, further comprising:

performing a global quality assessment of the one or more images collectively and performing a local quality assessment of a portion of one image of the one or more images, wherein the image quality score is based on the global quality assessment and the local quality assessment.

3. The method of claim 1, further comprising:

estimating, using the received image quality score, one or more performance metrics, wherein the one or more performance metrics include metrics measuring time to complete at least a portion of the simulation, cost of at least a portion of the simulation, error of the simulation, percent confidence or confidence interval of the simulation, or a combination thereof; and

selecting the image based on the estimated performance metrics.

4. The method of claim 1, further comprising:

generating a prompt to correct, retake, or update the selected image.

5. The method of claim 1, further comprising:

receiving an enhanced image generated as a corrected, retaken, or updated version of the selected image; and

performing the simulation based on the enhanced image.

6. The method of claim 5, further comprising:

generating the enhanced image by combining one or more pieces of imaging data to improve the image quality score associated with the one or more images.

7. The method of claim 1, further comprising:

estimating, using the received image quality score, accuracy or confidence in simulation results performed using the selected image.

8. The method of claim 1, further comprising:

generating an anatomic model of at least a portion of the patient'"'"'s anatomy based on the selected image; and

performing the simulation based on the generated anatomic model.

9. A system for automatically selecting image(s) to produce model(s) for a patient-specific simulation of blood flow, the system comprising:

a digital memory storing instructions for automatically selecting image(s) to produce model(s) for a patient-specific simulation of blood flow; and

a processor configured to execute the instructions to perform a method including;

receiving one or more characteristics of accuracy, precision, or performance for a simulation of a patient'"'"'s blood flow;

receiving one or more images of at least a portion of a patient'"'"'s anatomy;

receiving an image quality score associated with the one or more images; and

selecting, based on the received image quality score and the received one or more characteristics of accuracy, precision, or performance in performing the simulation, an image of the one or more received images to produce a model for use in the simulation of the patient'"'"'s blood flow.

10. The system of claim 9, wherein the processor is further configured for:

performing a global quality assessment of the one or more images collectively and performing a local quality assessment of a portion of one image of the one or more images, wherein the image quality score is based on the global quality assessment and the local quality assessment.

11. The system of claim 9, wherein the processor is further configured for:

estimating, using the received image quality score, one or more performance metrics, wherein the one or more performance metrics include metrics measuring time to complete at least a portion of the simulation, cost of at least a portion of the simulation, error of the simulation, percent confidence or confidence interval of the simulation, or a combination thereof; and

selecting the image based on the estimated performance metrics.

12. The system of claim 9, wherein the processor is further configured for:

generating a prompt to correct, retake, or update the selected image.

13. The system of claim 9, wherein the processor is further configured for:

receiving an enhanced image generated as a corrected, retaken, or updated version of the selected image; and

performing the simulation based on the enhanced image.

14. The system of claim 13, wherein the processor is further configured for:

generating the enhanced image by combining one or more pieces of imaging data to improve the image quality score associated with the one or more images.

15. The system of claim 9, wherein the processor is further configured for:

estimating, using the received image quality score, accuracy or confidence in simulation results performed using the selected image.

16. The system of claim 11, wherein the processor is further configured for:

generating an anatomic model of at least a portion of the patient'"'"'s anatomy based on the selected image; and

performing the simulation based on the generated anatomic model.

17. A non-transitory computer readable medium for use on at least one computer system containing computer-executable programming instructions for automatically selecting image(s) to produce model(s) for a patient-specific simulation of blood flow, that when executed by the at least one computer system, cause the performance of a method comprising:

receiving one or more characteristics of accuracy, precision, or performance for a simulation of a patient'"'"'s blood flow;

receiving one or more images of at least a portion of a patient'"'"'s anatomy;

receiving an image quality score associated with the one or more images; and

selecting, based on the received image quality score and the received one or more characteristics of accuracy, precision, or performance in performing the simulation, an image of the one or more received images to produce a model for use in the simulation of the patient'"'"'s blood flow.

18. The non-transitory computer readable medium of claim 17, the method further comprising:

performing a global quality assessment of the one or more images collectively and performing a local quality assessment of a portion of one image of the one or more images, wherein the image quality score is based on the global quality assessment and the local quality assessment.

19. The non-transitory computer readable medium of claim 17, the method further comprising:

estimating, using the received image quality score, one or more performance metrics, wherein the one or more performance metrics include metrics measuring time to complete at least a portion of the simulation, cost of at least a portion of the simulation, error of the simulation, percent confidence or confidence interval of the simulation, or a combination thereof; and

selecting the image based on the estimated performance metrics.

20. The non-transitory computer readable medium of claim 17, the method further comprising:

generating a prompt to correct, retake, or update the selected image.