Deep learning medical systems and methods for image acquisition

Patent metadata

Field	Value
Publication number	US-10628943-B2
Application number	US-201816154870-A
Country	US
Kind code	B2
Filing date	Oct 9, 2018
Priority date	Nov 23, 2016
Publication date	Apr 21, 2020
Grant date	Apr 21, 2020

Abstract

Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

First claim

What is claimed is:

1. A data acquisition system configuration apparatus comprising:
a training learning device including a first processor to implement a first deep learning network to learn a first set of data acquisition system configuration parameters based on a first set of inputs from a plurality of prior medical data acquisitions to configure at least one data acquisition system for medical data acquisition, the training learning device to receive and process feedback including operational data from the plurality of medical data acquisitions by the at least one data acquisition system; and
a deployed learning device including a second processor to implement a second deep learning network model, the second deep learning network model generated from the first deep learning network of the training learning device, the deployed learning device configured to provide a second data acquisition system configuration parameter to the data acquisition system in response to receiving a second input for medical data acquisition.

2. The apparatus of claim 1 , further including an interface to connect to the data acquisition system, the interface, once connected, enabling automated exchange of data between the data acquisition system and at least one of the training learning device and the deployed learning device.

3. The apparatus of claim 1 , wherein the second input includes patient information for a subject of the medical data acquisition.

4. The apparatus of claim 1 , wherein the feedback includes medical data quality information.

5. The apparatus of claim 4 , wherein the deployed learning device is to generate a recommendation for next action when the medical data quality information fails to satisfy a threshold.

6. The apparatus of claim 4 , wherein the first processor is to set the first set of data acquisition system configuration parameters as a default set of parameters for the data acquisition system when the medical data quality information satisfies a threshold.

7. The apparatus of claim 1 , wherein the second input includes a purpose of a medical exam associated with the medical data acquisition.

8. A non-transitory computer readable medium comprising instructions which, when executed, configure a machine to implement a data acquisition system configuration apparatus, the apparatus including:
a training learning device including a first processor to implement a first deep learning network to learn a first set of data acquisition system configuration parameters based on a first set of inputs from a plurality of prior medical data acquisitions to configure at least one data acquisition system for medical data acquisition, the training learning device to receive and process feedback including operational data from the plurality of medical data acquisitions by the at least one data acquisition system; and
a deployed learning device including a second processor to implement a second deep learning network model, the second deep learning network model generated from the first deep learning network of the training learning device, the deployed learning device configured to provide a second data acquisition system configuration parameter to the data acquisition system in response to receiving a second input for medical data acquisition.

9. The computer readable medium of claim 8 , wherein the instructions, when executed, further configure the machine to include an interface to connect to the data acquisition system, the interface, once connected, enabling automated exchange of data between the data acquisition system and at least one of the training learning device and the deployed learning device.

10. The computer readable medium of claim 8 , wherein the second input includes patient information for a subject of the medical data acquisition.

11. The computer readable medium of claim 8 , wherein the feedback includes medical data quality information.

12. The computer readable medium of claim 11 , wherein the first deep learning network is to generate a recommendation for next action when the medical data quality information fails to satisfy a threshold.

13. The computer readable medium of claim 11 , wherein the first set of data acquisition system configuration parameters is set as a default set of parameters for the data acquisition system when the medical data quality information satisfies a threshold.

14. The computer readable medium of claim 8 , wherein the second input includes a purpose of a medical exam associated with the medical data acquisition.

15. A method comprising:
training a first deep learning network at a training learning device to learn a first set of data acquisition system configuration parameters based on a first set of inputs from a plurality of prior medical data acquisitions to configure at least one data acquisition system for medical data acquisition via a data acquisition configuration device, the training learning device to receive and process feedback including operational data from the plurality of medical data acquisitions by the at least one data acquisition system;
generating a second deep learning network model at a deployed learning device using the first deep learning network;
deploying the deployed learning device with the second deep learning network model to provide a second data acquisition system configuration parameter to the data acquisition system in response to receiving a second input for medical data acquisition;
receiving feedback from the data acquisition system, the feedback including operational data from the medical data acquisition by the data acquisition system; and
updating the first deep learning network of the training learning device based on the received feedback.

16. The method of claim 15 , further including connecting the data acquisition configuration device to the data acquisition system via an interface, wherein the interface, once connected, enables automated exchange of data between the data acquisition system and at least one of the training learning device and the deployed learning device.

17. The method of claim 15 , wherein the second input includes patient information for a subject of the medical data acquisition.

18. The method of claim 15 , wherein the feedback includes medical data quality information.

19. The method of claim 18 , wherein the second deep learning network model is to generate a recommendation for next action when the medical data quality information fails to satisfy a threshold.

20. The method of claim 18 , further including wherein the data acquisition configuration device is to set the first set of data acquisition system configuration parameters as a default set of parameters when the medical data quality information satisfies a threshold.

Assignees

• Gen Electric

Inventors

• Hsieh Jiang
• Avinash Gopal
• Sirohey Saad

Classifications

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G16H40/40

  for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title

• G06T7/0012 Primary

  Biomedical image inspection · CPC title