Predictive use of quantitative imaging

• US 10,682,098 B2
• Filed: 03/22/2018
• Issued: 06/16/2020
• Est. Priority Date: 03/22/2018
• Status: Active Grant

• Current Assignee: Shenzhen Mindray Bio-Medical Electronics Co., Ltd. (Excelsior Union Ltd.)
• Original Assignee: Shenzhen Mindray Bio-Medical Electronics Co., Ltd. (Excelsior Union Ltd.)
• Inventors: McLaughlin, Glen W.
• Primary Examiner(s): Alavi, Amir
• Application Number:
  US15/928,921
• Publication Number:
  US 20190290210A1
• Time in Patent Office: 817 Days
• 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 5/0048 Detecting, measuring or recording by applying mechanical forces or stimuli A61B5/021, A61B5/4884 and A61B9/00 take precedence
• CPC Class Codes: A61B 5/0095 by applying light and detecting acoustic waves, i.e. photoacoustic measurements
• 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 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4277 saliva secretion
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/485 involving fluorescence X-ray imaging
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 8/15 Transmission-tomography
• 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 8/488 involving Doppler signals
• 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 8/565 : involving data transmission via a network
• CPC Class Codes: A61B 8/58 : Testing, adjusting or calibrating the diagnostic device
• CPC Class Codes: G06F 18/2111 : by using evolutionary computational techniques, e.g. genetic algorithms
• CPC Class Codes: G06F 18/23 : Clustering techniques
• CPC Class Codes: G06F 18/2411 : based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/24323 : Tree-organised classifiers
• CPC Class Codes: G06F 18/28 : Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06F 18/29 : Graphical models, e.g. Bayesian networks
• 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 2207/10132 : Ultrasound image
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/20084 : Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30056 : Liver; Hepatic
• CPC Class Codes: G06T 2207/30068 : Mammography; Breast
• CPC Class Codes: G06T 2207/30081 : Prostate
• CPC Class Codes: G06T 7/0014 : using an image reference approach
• CPC Class Codes: G06V 10/774 : Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 10/84 : using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
• CPC Class Codes: G06V 2201/031 : of internal organs
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging and ancillary information to the ultrasound imaging. At least two quantitative measurements of a subject, including at least one measurement taken using ultrasound imaging, as part of quantified information can be identified. One of the quantitative measurements can be compared to a first predetermined standard, included as part of ancillary information to the quantified information, in order to identify a first initial value. Further, another of the quantitative measurements can be compared to a second predetermined standard, included as part of the ancillary information, in order to identify a second initial value. Subsequently, the quantitative information can be correlated with the ancillary information using the first initial value and the second initial value to determine a final value that is predictive of a disease state of the subject.

Claims

1. A method comprising:

identifying at least two quantitative measurements of a subject, at least one of the at least two quantitative measurement taken using ultrasound imaging, the at least two quantitative measurements included as part of quantitative information of the subject gathered based on the ultrasound imaging;

comparing one of the at least two quantitative measurements to a first predetermined standard to determine a first initial value, the first predetermined standard included as part of ancillary information to the quantitative information of the subject and falling within a first range of quantities;

comparing another of the at least two quantitative measurements to a second predetermined standard to determine a second initial value, the second predetermined standard included as part of the ancillary information to the quantitative information of the subject and falling within a second range of quantities; and

correlating the quantitative information with the ancillary information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

2. The method of claim 1, wherein the at least one of the at least two quantitative measurements included as part of the quantitative information is an ultrasonic quantitative measurement formed from contrast-enhanced ultrasound, photo-acoustic imaging, acoustic radiation force impulse imaging, ultrasonic characterization of non-linear properties, ultrasonic attenuation, ultrasonic sound speed characterizations and/or ultrasonic speckle statistics.

3. The method of claim 1, wherein the at least two quantitative measurements includes a measurement of at least one of a core biopsy, blood pressure, metabolic rate, pulse, BMI, body fat, angiography, blood analysis, saliva analysis, photo-acoustic imaging, protein screening and genetic screening.

4. The method of claim 1, further comprising using other imaging modalities to determine the final value that is used in predicting the disease state of the subject.

5. The method of claim 4, wherein the other imaging modalities include one of core biopsy, X-rays, fluoroscopy, computed tomography or MRI.

6. The method of claim 1, further comprising using raw data to determine the final value that is used in predicting the disease state of the subject.

7. The method of claim 1, further comprising using a machine-learning algorithm to predict the disease state of the subject.

8. The method of claim 7, wherein the machine-learning algorithm is maintained using at least one of decision tree learning, association rule learning, artificial neural networks, inductive logic, a support vector, clustering, Baysian networks, reinforcement learning, representation learning, similarity and/or metric learning, sparse dictionary learning and/or protein and/or genetic algorithms.

9. The method of claim 1, wherein the quantitative information is correlated with the ancillary information by combining the first initial value with the second initial value to identify the final value that is used in predicting the disease state of the subject.

10. The method of claim 1, wherein the quantitative information is correlated with the ancillary information by comparing the quantitative information to the ancillary information to determine the final value.

11. A system comprising:

one or more processors; and

at least one computer-readable storage medium having stored therein instructions, when executed by the one or more processors, cause the one or more processors to perform operations comprising;

identifying at least two quantitative measurements of a subject, at least one of the at least two quantitative measurement taken using ultrasound imaging, the at least two quantitative measurements included as part of quantitative information of the subject gathered based on the ultrasound imaging;

comparing one of the at least two quantitative measurements to a first predetermined standard to determine a first initial value, the first predetermined standard included as part of ancillary information to the quantitative information of the subject and falling within a first range of quantities;

comparing another of the at least two quantitative measurements to a second predetermined standard to determine a second initial value, the second predetermined standard included as part of the ancillary information to the quantitative information of the subject and falling within a second range of quantities; and

correlating the quantitative information with the ancillary information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

12. The system of claim 11, wherein the at least one of the at least two quantitative measurements included as part of the quantitative information is an ultrasonic quantitative measurement formed from contrast-enhanced ultrasound, photo-acoustic imaging, acoustic radiation force impulse imaging, ultrasonic characterization of non-linear properties, ultrasonic attenuation, ultrasonic sound speed characterizations and/or ultrasonic speckle statistics.

13. The system of claim 11, wherein the at least two quantitative measurements includes a measurement of at least one of a core biopsy, blood pressure, metabolic rate, pulse, BMI, body fat, angiography, blood analysis, saliva analysis, photo-acoustic imaging, protein screening and genetic screening.

14. The system of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform operations further comprising using other imaging modalities to determine the final value that is used in predicting the disease state of the subject.

15. The system of claim 14, wherein the other imaging modalities include one of core biopsy, X-rays, fluoroscopy, computed tomography or MRI.

16. The system of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform operations further comprising using raw data to determine the final value that is used in predicting the disease state of the subject.

17. The system of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform operations further comprising using a machine-learning algorithm to predict the disease state of the subject.

18. The system of claim 17, wherein the machine-learning algorithm is maintained using at least one of decision tree learning, association rule learning, artificial neural networks, inductive logic, a support vector, clustering, Baysian networks, reinforcement learning, representation learning, similarity and/or metric learning, sparse dictionary learning and/or protein and/or genetic algorithms.

19. The system of claim 11, wherein the quantitative information is correlated with the ancillary information by combining the first initial value with the second initial value to identify the final value that is used in predicting the disease state of the subject.

20. The system of claim 11, wherein the quantitative information is correlated with the ancillary information by comparing the quantitative information to the ancillary information to determine the final value.