Abstract: A computer-implemented method for generating a three-dimensional (3D) composite model of a region of a subject, the method comprising: generating a 3D clinical model of the region of the subject based on one or more images of the region of the subject; obtaining a 3D generic model of the region of a generic subject; combining the 3D clinical model and the 3D generic model using an affine transformation, a bending transformation, and a squeezing transformation of the 3D generic model to obtain the 3D composite model of the subject; and displaying the composite 3D model on a display.

Abstract: To plan tumor treating fields (TTFields) therapy, a model of a patient's head is often used to determine where to position the transducer arrays during treatment, and the accuracy of this model depends in large part on an accurate segmentation of MRI images. The quality of a segmentation can be improved by presenting the segmentation to a previously-trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality scores are used to determine which segmentation provides better results, optionally by running simulations for models that correspond to each segmentation for a plurality of different transducer array layouts.

Abstract: Systems and methods are provided for ultrasound imaging with real-time feedback for cardiopulmonary resuscitation (CPR) compressions. Ultrasound images generated based on received echo ultrasound signals during cardiopulmonary resuscitation (CPR) of a patient may be processed, and based on the processing of the ultrasound images, real-time information relating to the cardiopulmonary resuscitation (CPR) may be determined. Feedback for assisting in conducting the cardiopulmonary resuscitation (CPR) may be generated based on the information. The feedback may include information and/or indications relating to compressions applied during the cardiopulmonary resuscitation (CPR). The feedback may be configured for outputting during displaying of the generated ultrasound images.

Abstract: A method and ultrasound imaging system for performing an ultrasound examination. The method and system includes entering a workflow and displaying a plurality of graphical icons positioned on a graphical model. The method and system includes selecting a first anatomical zone, acquiring a first image, and saving and associating the first image with the first anatomical zone. The method and system includes saving and associating a first clinical finding with the first anatomical zone. The method and system includes selecting a second anatomical zone, acquiring a second image, and saving and associating the second image with the second anatomical zone. The method and system includes saving and associating a second clinical finding with the second anatomical zone. The method and system include displaying an examination overview including the first image, the first clinical finding, the second image, and the second clinical finding.

Abstract: Various methods and systems are provided for graphical grading of medical findings during a diagnostic imaging exam. In one example, a method includes displaying an acquired medical image on a display area of a display device, displaying a virtual anatomical diagram on the display area adjacent to the ultrasound medical image, responsive to selection of an anatomical region from the anatomical diagram, displaying a plurality of icons representing graded diagnostic findings associated with the anatomical region, and responsive to selection of an icon of the plurality of icons, storing the ultrasound medical image in permanent memory.

Abstract: Various methods and systems for displaying cine loops during a periodic imaging session are disclosed. In one example, a method includes acquiring, with an ultrasound probe, a first set of images of an imaging subject during a first imaging period, displaying the first set of images as a first cine loop at a first display area of a display, acquiring, with the ultrasound probe, a second set of images of the imaging subject during a second imaging period different than the first imaging period, and displaying the second set of images as a second cine loop at a second display area of the display, different than the first area, while maintaining display of the first cine loop at the first display area.

Abstract: To plan tumor treating fields (TTFields) therapy, a model of a patient's head is often used to determine where to position the transducer arrays during treatment, and the accuracy of this model depends in large part on an accurate segmentation of MRI images. The quality of a segmentation can be improved by presenting the segmentation to a previously-trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality scores are used to determine which segmentation provides better results, optionally by running simulations for models that correspond to each segmentation for a plurality of different transducer array layouts.

Abstract: To plan tumor treating fields (TTFields) therapy, a model of a patient's head is often used to determine where to position the transducer arrays during treatment, and the accuracy of this model depends in large part on an accurate segmentation of MRI images. The quality of a segmentation can be improved by presenting the segmentation to a previously-trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality scores are used to determine which segmentation provides better results, optionally by running simulations for models that correspond to each segmentation for a plurality of different transducer array layouts.

Abstract: Systems and methods are provided for ultrasound imaging with real-time feedback for cardiopulmonary resuscitation (CPR) compressions. Ultrasound images generated based on received echo ultrasound signals during cardiopulmonary resuscitation (CPR) of a patient may be processed, and based on the processing of the ultrasound images, real-time information relating to the cardiopulmonary resuscitation (CPR) may be determined. Feedback for assisting in conducting the cardiopulmonary resuscitation (CPR) may be generated based on the information. The feedback may include information and/or indications relating to compressions applied during the cardiopulmonary resuscitation (CPR).

Abstract: Various methods and systems for displaying cine loops during a periodic imaging session are disclosed. In one example, a method includes acquiring, with an ultrasound probe, a first set of images of an imaging subject during a first imaging period, displaying the first set of images as a first cine loop at a first display area of a display, acquiring, with the ultrasound probe, a second set of images of the imaging subject during a second imaging period different than the first imaging period, and displaying the second set of images as a second cine loop at a second display area of the display, different than the first area, while maintaining display of the first cine loop at the first display area.

Abstract: A method and ultrasound imaging system for performing an ultrasound examination. The method and system includes entering a workflow and displaying a plurality of graphical icons positioned on a graphical model. The method and system includes selecting a first anatomical zone, acquiring a first image, and saving and associating the first image with the first anatomical zone. The method and system includes saving and associating a first clinical finding with the first anatomical zone. The method and system includes selecting a second anatomical zone, acquiring a second image, and saving and associating the second image with the second anatomical zone. The method and system includes saving and associating a second clinical finding with the second anatomical zone. The method and system include displaying an examination overview including the first image, the first clinical finding, the second image, and the second clinical finding.

Abstract: To plan tumor treating fields (TTFields) therapy, a model of a patient's head is often used to determine where to position the transducer arrays during treatment, and the accuracy of this model depends in large part on an accurate segmentation of MRI images. The quality of a segmentation can be improved by presenting the segmentation to a previously-trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality scores are used to determine which segmentation provides better results, optionally by running simulations for models that correspond to each segmentation for a plurality of different transducer array layouts.

Abstract: Various methods and systems are provided for graphical grading of medical findings during a diagnostic imaging exam. In one example, a method includes displaying an acquired medical image on a display area of a display device, displaying a virtual anatomical diagram on the display area adjacent to the ultrasound medical image, responsive to selection of an anatomical region from the anatomical diagram, displaying a plurality of icons representing graded diagnostic findings associated with the anatomical region, and responsive to selection of an icon of the plurality of icons, storing the ultrasound medical image in permanent memory.

Abstract: An ultrasound imaging system and method acquires ultrasound image data from moving an ultrasound probe over a body of a person, automatically divides the ultrasound image data into segments of interest based on where the ultrasound image data was acquired, and displays a panoramic view of the ultrasound image data that includes two or more of the segments of interest with at least one of the segments of interest displayed as a video.