Medical ultrasound imaging optimization using a machine-learned network
Machine learning network trained to tune settings and optimize images. In accordance with one aspect, a method is provided for image optimization with a medical ultrasound scanner. A medical ultrasound scanner images a patient using first settings. A first image from the imaging using the first settings and patient information for the patient are input to a machine-learned network. The machine-learned network outputs second settings in response to the inputting of the first image and the patient information. The medical ultrasound scanner re-images the patient using the second settings. A second image from the re-imaging is displayed.
1 . A method for image optimization with a medical ultrasound scanner, the method comprising: imaging, by a medical ultrasound scanner, a patient using first settings; inputting a first image from the imaging using the first settings and patient information for the patient to a machine-learned network, wherein the machine learned network is trained based on training image data having ground truth labels inferred by a computer based at least in part on contextual data produced from user workflow in patient examination, wherein the contextual data is indicative of acceptance or rejection of the training image data for diagnostic use during the user workflow;
wherein the computer infers a positive ground label in response to the contextual data being indicative of acceptance of the training image data for diagnostic use when the contextual data indicates storage or capture of the training image data in a patient medical record, and a negative ground truth label in response to the contextual data being indicative of rejection of the training image data for diagnostic use when the contextual data indicates failure to save the training image data in a patient medical record or overwriting of the training image data;
outputting, by the machine-learned network, second settings in response to the inputting of the first image and the patient information for facilitating efficient patient-specific ultrasound image optimization by automatic tuning of imaging parameters specific to patient's situation;
re-imaging, by the medical ultrasound scanner, the patient using the second settings; and displaying a second image from the re-imaging for providing improved ultrasound image suited for diagnosis.
2 . The method of claim 1 wherein imaging comprises imaging by the medical ultrasound scanner operated by a user, wherein inputting comprises inputting user information for the user, and wherein outputting comprises outputting in response to the inputting of the user information.
3 . The method of claim 1 wherein inputting comprises inputting the first image, the patient information and a location of the medical ultrasound scanner, and wherein outputting comprises outputting in response to the inputting of the location.
4 . The method of claim 1 further comprising notifying a user of the medical ultrasound scanner of the second settings, wherein the user triggers the re-imaging using the second settings by a response to the notification.
5 . The method of claim 1 wherein the contextual data comprises scanner log data.
6 . The method of claim 1 wherein inputting comprises inputting to the machine-learned network, the machine-learned network having been trained based on images for other patients and corresponding settings labelled as negative examples when not stored for the other patients and images for the other patients and corresponding settings labelled as positive examples when stored for the other patients.
7 . The method of claim 1 wherein the second settings comprise transmit frequency, receive frequency, scan line format, scan line density, pulse repetition frequency, overall gain, depth gain, dynamic range, focal depth, scan depth, focal position, filter kernel, spatial filter parameters, temporal filter parameters, noise thresholds, motion thresholds, color mapping, three-dimensional rendering parameters, or a combination thereof.
8 . An imaging system comprising: an ultrasound scanner configurable based on first and second values of imaging parameters; a processor configured to determine the second values of the imaging parameters with a machine-learned network in response to input of a first image of a patient, the first values of the imaging parameters used for the first image and patient information for the patient for facilitating efficient patient-specific ultrasound image optimization by automatic tuning of imaging parameters specific to patient's situation, wherein the machine-learned network is trained based on training image data having ground truth labels-inferred by the processor based at least in part on contextual data produced from user workflow in patient examination, wherein the contextual data is indicative of acceptance or rejection of the training image data for diagnostic use during the user workflow;
wherein the processor infers a positive ground label in response to the contextual data being indicative of acceptance of the training image data for diagnostic use when the contextual data indicates storage or capture of the training image data in a patient medical record, and a negative ground truth label in response to the contextual data being indicative of rejection of the training image data for diagnostic use when the contextual data indicates failure to save the training image data in a patient medical record or overwriting of the training image data;
and a display configured to display a second image of the patient generated by the ultrasound scanner configured by the second values for providing improved ultrasound image suited for diagnosis.
9 . The imaging system of claim 8 wherein the processor infers the ground truth labels based on scanner log data.
10 . The imaging system of claim 8 wherein the contextual data is indicative of rejection of the training image data for diagnostic use when the contextual data indicates a repeated imaging.
11 . The imaging system of claim 8 wherein the contextual data is indicative of acceptance of the training image data for diagnostic use when the contextual data indicates storage of the training image data in a patient medical record.
12 . The imaging system of claim 8 wherein the processor is configured to determine the second values in response to the input of the first image, the first values, the patient information and a location of the ultrasound scanner.
13 . The imaging system of claim 12 wherein the patient information comprises age, gender, role, or a combination thereof.
14 . The imaging system of claim 8 wherein the processor is configured to determine the second values in response to the input of the first image, the first values, the patient information and user information for a user of the ultrasound scanner.
15 . The imaging system of claim 8 wherein the machine learned network comprises an artificial neural network.
16 . One or more non-transitory computer-readable media embodying instructions executable by machine to perform operations for image optimization, the operations comprising: receiving, from a medical ultrasound scanner, a first image of a patient imaged using first settings; inputting the first image and patient information for the patient to a machine-learned network, wherein the machine-learned network is trained based on training image data having ground truth labels inferred by a computer based at least in part on contextual data produced from user workflow in patient examination, wherein the contextual data is indicative of acceptance or rejection of the training image data for diagnostic use during the user workflow;
wherein the computer infers a positive ground label in response to the contextual data being indicative of acceptance of the training image data for diagnostic use when the contextual data indicates storage or capture of the training image data in a patient medical record, and a negative ground truth label in response to the contextual data being indicative of rejection of the training image data for diagnostic use when the contextual data indicates failure to save the training image data in a patient medical record or overwriting of the training image data;
generating, by the machine-learned network, second settings in response to the inputting of the first image and the patient information for facilitating efficient patient-specific ultrasound image optimization by automatic tuning of imaging parameters specific to patient's situation; triggering re-imaging, by the medical ultrasound scanner, of the patient using the second settings; and displaying a second image from the re-imaging for providing improved ultrasound image suited for diagnosis.