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

• US 8,861,820 B2
• Filed: 02/04/2014
• Issued: 10/14/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: Fonte, Timothy A., Grady, Leo J., Wu, Zhongle, Schaap, Michiel, Sengupta, Souma, Hunley, Stanley C.
• Primary Examiner(s): Mehta, Bhavesh
• Assistant Examiner(s): Harandi, Siamak
• Application Number:
  US14/172,554
• Publication Number:
  US 20140270427A1
• Time in Patent Office: 252 Days
• Field of Search: None
• US Class Current: 382/128
• 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 assessing the quality of medical images of at least a portion of a patient'"'"'s anatomy, using a computer system, the method comprising:

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

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, for a selected one of the received images, a plurality of image characteristics of the selected one of the received images using the processor of the computer system, wherein the plurality of image characteristics include two or more different types of image characteristics and each of the plurality of image characteristics is associated with an anatomic feature of the patient'"'"'s anatomy based on the anatomic localization or modeling; and

calculating, using the processor of the computer system, an image quality score for the selected one of the received images based on the plurality of image characteristics of the selected one of the received images.

2. The method of claim 1, wherein the plurality of image characteristics include two or more of:

a local contrast level, a local noise level, a misregistration, a misalignment, a local motion anomaly, a local blurring anomaly, a partial volume effect, a blooming effect, and an artifact.

3. The method of claim 1, further comprising determining, using a processor of the computer system, one or more image properties of the received images;

wherein the one or more image properties include one or more of;

an image resolution, a medical anatomy slice thickness, a number of scanner slices, a medication parameter, and a patient characteristic.

4. The method of claim 1, further comprising:

calculating a local score in relation to each of the plurality of image characteristics; and

calculating the image quality score based on the local scores calculated for each of the plurality of image characteristics.

5. The method of claim 4, further comprising:

determining one or more associations between the one or more local scores and one or more of;

a modeling or simulation accuracy, a modeling or simulation precision, and a modeling or simulation performance.

6. The method of claim 3, wherein the one or more image properties are global image properties.

7. The method of claim 1, further comprising:

using a processor of the computer system to extract imaging data from the received images;

determining a range, an accept/reject criteria, or an absence associated with the extracted imaging data; and

determining a subset of the received images to use for the anatomic localization or modeling based on the range, the accept/reject criteria, or the absence associated with the extracted imaging data.

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 anatomic localization or modeling;

predicting a local uncertainty value of the anatomic model based on the plurality of image characteristics;

computing a confidence interval of a simulated blood flow calculation based on the predicted local uncertainty value; and

calculating a total uncertainty value for the anatomic model as a quantitative metric of image quality of the one or more images.

9. The method of claim 1, further comprising:

generating an anatomic model of at least a portion of the patient'"'"'s anatomy based on the anatomic localization or modeling;

creating a computational model of a blood flow characteristic based on the anatomic model; and

determining a blood flow characteristic within the patient'"'"'s anatomy based on the anatomic model and the computational model of the blood flow characteristic of the patient'"'"'s vasculature.

10. The method of claim 1, wherein the patient'"'"'s anatomy includes a portion of the patient'"'"'s vasculature, the method further comprising:

creating a computational model of a blood flow characteristic based on the anatomic localization or modeling; and

determining a location of a functionally significant narrowing in a vessel of the patient'"'"'s vasculature based on the determined blood flow characteristic.

11. The method of claim 10, wherein:

the blood flow characteristic is a fractional flow reserve value;

the fractional flow reserve indicates a ratio between a pressure or flow rate in a first location and a pressure or flow rate at a second location in the patient'"'"'s vasculature; and

the fractional flow reserve is determined at a plurality of locations in the patient'"'"'s vasculature.

12. A system for assessing the quality of medical images of at least a portion of a patient'"'"'s anatomy, the system comprising:

a digital storage device storing instructions for assessing the quality of medical images of at least a portion of a patient'"'"'s anatomy; and

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

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

performing anatomic localization or modeling of at least a portion of the patient'"'"'s anatomy based on the received images;

obtaining, for a selected one of the received images, a plurality of image characteristics of the selected one of the received images using the processor of the computer system, wherein the plurality of image characteristics include two or more different types of image characteristics and each of the plurality of image characteristics is associated with an anatomic feature of the patient'"'"'s anatomy based on the anatomic localization or modeling; and

calculating, using the processor of the computer system, an image quality score for the selected one of the received images based on the plurality of image characteristics of the selected one of the received images.

13. The system of claim 12, wherein the plurality of image characteristics include two or more of:

a local contrast level, a local noise level, a misregistration, a misalignment, a local motion anomaly, a local blurring anomaly, a partial volume effect, a blooming effect, and an artifact.

14. The system of claim 12, wherein the processor is further configured for determining one or more image properties of the received images;

wherein the one or more image properties include one or more of;

an image resolution, a medical anatomy slice thickness, a number of scanner slices, a medication parameter, and a patient characteristic.

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

calculating a local score in relation to each of the one or more image characteristics; and

determining the image quality score based on the local scores calculated for each of the plurality of image characteristics.

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

determining one or more associations between the one or more local scores and one or more of;

a modeling or simulation accuracy, a modeling or simulation precision, and a modeling or simulation performance.

17. The system of claim 14, wherein the one or more image properties are global image properties.

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

extracting imaging data from the received images;

determining a range, an accept/reject criteria, or an absence associated with the extracted imaging data; and

determining a subset of the received images to use for the anatomic localization or modeling based on the range, the accept/reject criteria, or the absence associated with the extracted imaging data.

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

generating an anatomic model of at least a portion of the patient'"'"'s anatomy based on the anatomic localization or modeling;

predicting a local uncertainty value of the anatomic model based on the plurality of image characteristics;

computing a confidence interval of a simulated blood flow calculation based on the predicted local uncertainty value; and

calculating a total uncertainty value for the anatomic model as a quantitative metric of image quality of the one or more images.

20. The system of claim 12, wherein the patient'"'"'s anatomy includes at least a portion of the patient'"'"'s vasculature, and the processor is further configured for:

generating an anatomic model of at least a portion of the patient'"'"'s vasculature based on the anatomic localization or modeling;

creating a computational model of a blood flow characteristic based on the anatomic model; and

determining a blood flow characteristic within the patient'"'"'s vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient'"'"'s vasculature.

21. The system of claim 12, wherein the patient'"'"'s anatomy includes at least a portion of the patient'"'"'s vasculature, and wherein the processor is further configured for:

creating a computational model of a blood flow characteristic based on the anatomic localization or modeling; and

determining a location of a functionally significant narrowing in a vessel of the patient'"'"'s vasculature based on the determined blood flow characteristic.

22. A non-transitory computer readable medium for use on at least one computer system containing computer-executable programming instructions for assessing the quality of medical images of at least a portion of a patient'"'"'s anatomy, that when executed by the at least one computer system, cause the performance of a method comprising:

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, for a selected one of the received images a plurality of image characteristics of the selected one of the received images using the processor of the computer system, wherein the plurality of image characteristics include two or more different types of image characteristics and each of the plurality of image characteristics is associated with an anatomic feature of the patient'"'"'s anatomy based on the anatomic localization or modeling; and

calculating, using the processor of the computer system, an image quality score for the selected one of the received images based on the plurality of image characteristics of the selected one of the received images.