Abstract: A system and method for medical imaging protocol name standardization includes generating a synthetic training dataset from medical standards in public documentation utilizing knowledge elicitation, wherein the synthetic training dataset includes, for a given language and a given imaging modality, a plurality of combinations of possible medical imaging protocol names for respective standard protocol codes of a plurality of standard protocol codes for each standard medical imaging protocol. The system and method also includes generating a lightweight text classification model from the synthetic training dataset utilizing machine learning. The system and method further includes utilizing the lightweight text classification model to receive a medical imaging protocol name and to output a list of most probable protocol codes based on the medical imaging protocol name.

Abstract: Methods and systems are provided for automatically evaluating an image quality of medical images, and collecting user feedback with respect to the image quality via a graphical user interface (GUI). The tool provides an assessment of noise, contrast, and other characteristics of slices of an image associated with organs and/or anatomical structures of the image based on various low-level metrics, and calculates an overall image quality score of the image by aggregating the image scores of the organs and/or anatomical structures. The score may be estimated by a machine learning (ML) model based on the low-level metrics. The user can provide feedback with respect to the image quality score, which may be used to increase an accuracy and/or performance of the ML model. In this way, feedback may be efficiently collected and used to refine the ML model, leading to more accurate scan protocols and higher quality CT images.

Abstract: A system and method for determining a data quality of DICOM data are provided. Information related to a data ingestion of the DICOM data from a radiology device to a database may be received. Respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be determined using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database. A user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be generated.

Abstract: Methods and systems are provided for automatically generating an image quality score for a computed tomography (CT) image within an image quality assessment system. In one example, a method for an image quality assessment system comprises receiving a selection of a medical image from a user of the image quality assessment system; generating an image quality score for the selected medical image, the image quality score generated using a trained machine learning (ML) model; displaying the selected medical image and the image quality score in a graphical user interface (GUI) on a display device of the image quality assessment system; receiving an adjusted image quality score of the medical image from the user via the GUI; and using the adjusted image quality score to retrain the ML model.

Abstract: Various methods and systems are provided for automatically classifying a plurality of image slices using body region bounding boxes identified from a localizer image. In one embodiment, a localizer image may be mapped to a plurality of bounding boxes, corresponding to a plurality of body regions, using a trained machine learning model. Coordinates of the plurality of bounding boxes may be used to determine body region boundaries, such that the body regions are non-intersecting and coherent. The body regions identified in the localizer image may then be correlated to image slice ranges, and image slices within each image slice range may be labeled as belonging to the corresponding body region.

Abstract: Various methods and systems are provided for automatically classifying a plurality of image slices using body region bounding boxes identified from a localizer image. In one embodiment, a localizer image may be mapped to a plurality of bounding boxes, corresponding to a plurality of body regions, using a trained machine learning model. Coordinates of the plurality of bounding boxes may be used to determine body region boundaries, such that the body regions are non-intersecting and coherent. The body regions identified in the localizer image may then be correlated to image slice ranges, and image slices within each image slice range may be labeled as belonging to the corresponding body region.

Abstract: System and methods for automatically identifying anatomical regions in medical images are disclosed. A signature is computed from one or more images of a patient. The signature comprises a water equivalent diameter distribution generated from one or more images of the patient. A best matching atlas element is identified from an atlas. The atlas includes a group of atlas elements, each atlas element includes landmarks associated with a set of image data, and a signature associated with the set of image data. The signature of the best matching atlas element matches the signature of the patient the best among the atlas. Landmarks of the best matching atlas element are projected onto an image of the patient. The method can be used on its own for anatomy localization or used in conjunction with another anatomy localization method to correct the result of another method.

Abstract: System and methods for automatically identifying anatomical regions in medical images are disclosed. A signature is computed from one or more images of a patient. The signature comprises a water equivalent diameter distribution generated from one or more images of the patient. A best matching atlas element is identified from an atlas. The atlas includes a group of atlas elements, each atlas element includes landmarks associated with a set of image data, and a signature associated with the set of image data. The signature of the best matching atlas element matches the signature of the patient the best among the atlas. Landmarks of the best matching atlas element are projected onto an image of the patient. The method can be used on its own for anatomy localization or used in conjunction with another anatomy localization method to correct the result of another method.