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JP-7857284-B2 - Devices at imaging points to integrate AI algorithm training into clinical workflows.

JP7857284B2JP 7857284 B2JP7857284 B2JP 7857284B2JP-7857284-B2

Inventors

  • ブラウネイゲル アンドレ
  • カルステン リント

Assignees

  • コーニンクレッカ フィリップス エヌ ヴェ

Dates

Publication Date
20260512
Application Date
20210916
Priority Date
20200924

Claims (14)

  1. A medical imaging device for using a set of pre-image settings to collect patient images during an imaging session, (i) a user interface for receiving user annotations relating to the collected images of the patient and/or (ii) a set of pre-image settings used by the medical imaging device for collecting the images of the patient, and for creating annotated training data; (i) images of the patient collected and user annotations received, and/or (ii) a set of pre-image settings and user annotations received, for storing the created annotated training data, The system comprises a training module for training at least one data-driven model using training data obtained from the at least one training database, The present invention further comprises a first group of medical imaging devices and a second group of medical imaging devices that are different from the first group of medical imaging devices, The aforementioned user interface (i) images of a patient collected by the medical imaging devices in the first group, and/or (ii) a first user annotation relating to a set of pre-image settings used by the medical imaging devices in the first group, (i) receiving patient images collected by the medical imaging devices in the second group, and/or (ii) receiving second user annotations relating to a set of pre-image settings used by the medical imaging devices in the second group, The aforementioned at least one training database, (i) images of the patient collected by the medical imaging devices in the first group and the user annotations received, and/or (ii) a set of pre-image settings and the user annotations used by the medical imaging devices in the first group; (i) images of the patient collected by the medical imaging devices in the second group and the user annotations received, and/or (ii) a second training database for storing the set of pre-image settings used by the medical imaging devices in the second group and the user annotations received, An imaging system in which the training module trains a first data-driven model using training data obtained from the first training database, and trains a second data-driven model using training data obtained from the second training database.
  2. The first group of medical imaging devices and the second group of medical imaging devices are from different facilities and/or different user groups. The imaging system according to claim 1 .
  3. The aforementioned at least one data-driven model, A data-driven model for analyzing patient images collected to calculate medical decision-making support information, A data-driven model for analyzing the patient's camera image to calculate the set of pre-image settings, wherein the camera image is generated based on sensor data obtained from a sensor device, and the sensor device has a field of view that includes at least a portion of the area in which the patient is positioned for imaging, and the data-driven model includes one or more of these . The imaging system according to claim 1 or 2.
  4. The medical imaging device is equipped with the user interface. The imaging system according to any one of claims 1 to 3 .
  5. (i) an input channel for receiving the collected images of the patient, and/or (ii) the set of pre-image settings used by the medical imaging device for collecting the images of the patient; (i) a display for displaying the collected images of the patient, and/or (ii) a set of pre-image settings used by the medical imaging device for collecting the images of the patient; (i) the collected images of the patient, and/or (ii) the user interface for receiving user annotations relating to the set of pre-image settings used by the medical imaging device for collecting the images of the patient, The imaging system according to any one of claims 1 to 4, further comprising a mobile annotation device having an output channel for providing to at least one training database (i) collected patient images and received user annotations, and/or (ii) a set of pre-image settings and received user annotations.
  6. The aforementioned mobile annotation device is a handheld device that includes one or more of the following: a mobile phone, a laptop computing device, and a tablet computer. The imaging system according to claim 5 .
  7. The training module repeatedly trains the at least one data-driven model. The imaging system according to any one of claims 1 to 6 .
  8. The training module randomly generates the user annotations in order to begin training the at least one data-driven model. The imaging system according to any one of claims 1 to 7 .
  9. The at least one data-driven model provides suggestions based on the calculated medical decision support information, enabling the user to actively accept or reject the suggestions given. The imaging system according to claim 3 .
  10. The aforementioned user annotation, Instructions for image quality, Clinical findings and, Includes one or more instructions for a set of desired pre-image settings, The imaging system according to any one of claims 1 to 9.
  11. The aforementioned medical decision-making support information, The recommended workflow for the aforementioned patient, Display of the image quality of the collected images, Instructions regarding medical findings, Includes one or more of the following priority information that indicates the urgency of the medical findings: The imaging system according to claim 3 .
  12. The medical imaging device uses a set of pre-image settings to collect images of the patient during the imaging session. The steps include: receiving user annotations via a user interface regarding (i) images of the patient collected and/or (ii) a set of pre-image settings used by the medical imaging device for collecting images of the patient, and creating annotated training data; The steps include storing in at least one training database the created annotated training data, which includes (i) the collected patient images and the received user annotations, and/or (ii) the set of pre-image settings and the received user annotations; The training module includes the step of training at least one data-driven model using training data obtained from the at least one training database , Via the aforementioned user interface, (i) images of a patient collected by a medical imaging device in a first group of medical imaging devices, and/or (ii) a first user annotation relating to a set of pre-image settings used by the medical imaging devices in the first group, (i) images of a patient collected by a medical imaging device in a second group of medical imaging devices, which is different from the first group of medical imaging devices, and/or (ii) a second user annotation relating to a set of pre-image settings used by the medical imaging devices in the second group. The step of receiving, The aforementioned at least one training database, (i) images of the patient collected by the medical imaging devices in the first group and the user annotations received, and/or (ii) a set of pre-image settings and the user annotations used by the medical imaging devices in the first group; (i) images of the patient collected by the medical imaging devices in the second group and the user annotations received, and/or (ii) a set of pre-image settings used by the medical imaging devices in the second group and the user annotations received, in a second training database for storing these: Includes, An image processing method comprising the steps of the training module training a first data-driven model using training data obtained from the first training database, and training a second data-driven model using training data obtained from the second training database .
  13. A computer program, when executed by at least one processing unit, that causes the imaging system according to any one of claims 1 to 11 to perform the method according to claim 12.
  14. A computer-readable medium comprising the computer program described in claim 13.

Description

This invention generally relates to image processing, and more particularly to an imaging system, an image processing method, a computer program element, and a computer-readable medium. Previously, medical imaging equipment was mostly operated by specialized operators such as X-ray technicians (X-ray, CT, or MRI), sonographers (ultrasound), or nuclear medicine technicians (NM imaging). However, a new trend is emerging where less qualified staff are entrusted with performing these examinations. Without proper safety measures, this practice can lead to a decline in clinical quality. The operator (referred to as "user" in this specification) may, for example, depending on the modality and equipment specifications, (i) Patient placement, (ii) Adapt the imaging scan parameters. (iii) Perform the collection itself (iv) At the imaging equipment console, you will be responsible for performing a set of work steps throughout the examination, including reviewing and post-processing the acquired images. This figure shows a schematic block diagram of an exemplary medical imaging system.This figure shows a schematic block diagram of an exemplary imaging processing device.This figure shows a schematic block diagram of an exemplary mobile annotation device.This figure shows an example of implementing a mobile annotation device for radiologic technologists.This figure shows a schematic block diagram of a further exemplary medical imaging system.This figure shows a schematic block diagram of a further exemplary imaging processing device.This diagram shows a flowchart illustrating an example of an image processing method.This figure shows a schematic diagram of an exemplary data-driven model. The following describes in detail a method relating to a data-driven model for analyzing patient images collected to compute medical decision support information. While the following detailed description is illustrative and applies to a specific data-driven model, those skilled in the art should understand that the methods and imaging systems described above and below may be applicable to any other data-driven model, such as a data-driven model for analyzing patient camera images to compute a set of pre-image settings. Therefore, the examples described below are presented without any loss of generality of the claimed invention and without imposing any limitations on the claimed invention. Figure 1 shows a schematic block diagram of an exemplary medical imaging system 100. The medical imaging system 100 comprises a medical imaging device 10, an image processing device 12, a mobile annotation device 14, a training database 16, image databases 18a and 18b, and a display configuration 20. The medical imaging device 10 is configured to collect images 22 of the patient during an imaging session. The medical imaging device 10 may be of any modality, such as transmission imaging or radiography. Transmission imaging includes, for example, X-ray-based imaging performed using a CT scanner. Magnetic resonance imaging (MRI) and ultrasound imaging are also conceivable. Radiography includes PET/SPECT and other nuclear medicine modalities. During the imaging session, images 22 of the patient are collected. The images 20 are preferably in digital format to assist the physician in diagnosis. The image processing device 12 includes an image analyzer 26 configured to apply a data-driven model to analyze patient images collected for calculating medical decision support information. In one example, the image processing device 14 is a mobile device, such as a mobile phone, laptop computer, or tablet computer, but is not limited to these. In another example, the image processing device 12 is a server providing computing services. In yet another example, the imaging processing device 12 is a workstation with a display configuration 18. Figure 2 shows a schematic block diagram of an exemplary image processing device 12. In this example, the image processing device 12 comprises an input channel 24, an image analyzer 26, and an output channel 28. Input channel 24 is configured to receive patient images 22 collected during an imaging session. Input channel 12 is implemented, in one example, as an Ethernet interface, a USB® interface, a wireless interface such as Wi-Fi® or Bluetooth®, or any equivalent data transfer interface enabling data transfer between input peripherals and the image analyzer 26. Furthermore, the input channel accesses data via a network, such as the Internet, or any combination of wired networks, wireless networks, wide area networks, or local area networks. The image analyzer 26 of the image processing device 12 may be driven by artificial intelligence. In particular, the image analyzer 26 may be included as a pre-trained data-driven model. The image analyzer runs on the processing unit of the image processing device 12. The processing unit may include general-purpose circuits and/or dedicated computing circuits such as a GPU, or it may be a de