Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.

Abstract: The present disclosure relates to a system and method for identifying a tumor or lesion in a probability map. In accordance with certain embodiments, a method includes identifying, with a processor, a first region of interest in a first projection image, generating, with the processor, a first probability map from the first projection image and a second probability map from a second projection image, wherein the first probability map includes a second region of interest that has location that corresponds to a location of the first region of interest, interpolating the first probability map and the second probability map, thereby generating a probability volume, wherein the probability volume includes the second region of interest, and outputting, with the processor, a representation of the probability volume to a display.

Abstract: Systems and methods are provided for projection profile enabled computer aided detection (CAD). Volumetric ultrasound dataset may be generated, based on echo ultrasound signals, and based on the volumetric ultrasound dataset, a three-dimensional (3D) ultrasound volume may generated. Selective structure detection may be applied to the three-dimensional (3D) ultrasound volume.

Abstract: Various methods and systems are provided for computer-aided diagnosis. In one embodiment, a method comprises acquiring, with an imaging system, a medical image of a subject, generating, with a radiologist model associated with a radiologist of an institution, a computer-aided diagnosis for the medical image, the radiologist model comprising a deep neural network trained on a plurality of diagnoses provided by the radiologist, displaying, to the radiologist via a display device, the medical image and the computer-aided diagnosis, and selectively updating, based on the medical image, one or more of the radiologist model, an institution model associated with the institution, and a geographic model associated with a geographic area containing the institution. In this way, a radiologist may be assisted by a deep neural network model configured as a digital twin of the radiologist.

Abstract: A system and method for simulating an ultrasound scanning session is provided. The method includes acquiring an image of at least a portion of a simulated scan surface by a camera. A probe simulator having a visually coded pattern is maneuvered on the simulated scan surface. The method includes analyzing the acquired image to identify the visually coded pattern of the probe simulator maneuvered on the simulated scan surface. The method includes determining a position and orientation of the probe simulator based on the visually coded pattern identified in the acquired image. The method includes estimating a scan plane based at least in part on the determined position and orientation of the probe simulator. The method includes retrieving an ultrasound image from storage. The ultrasound image corresponds with the estimated scan plane. The method includes presenting the retrieved ultrasound image at a display system.

Abstract: A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

Abstract: A system includes a memory unit comprising a classifier network and a detector network. The classifier network is configured to perform a classification of a scan image among maternal images. The detector network is configured to determine a placenta condition in the scan image. The system further includes a data acquisition unit communicatively coupled to an ultrasound scanner and configured to receive maternal images from a maternal scanning procedure. The system also includes an image processing unit communicatively coupled to the memory unit and the data acquisition unit and configured to select a sagittal image from the maternal images using the classifier network. The image processing unit is further configured to determine a placenta condition based on the selected sagittal image using the detector network. The image processing unit is also configured to provide a recommendation to a medical professional based on the placenta condition.

Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: A system (e.g., an ultrasound imaging system) is provided. The system includes an ultrasound probe configured to acquire three-dimensional (3D) ultrasound data of a volumetric region of interest (ROI). The system further includes a display, a memory configured to store programmed instructions, and a controller circuit. The controller circuit includes one or more processors. The controller circuit is configured to execute the programmed instructions stored in the memory. When executing the programmed instructions, the controller circuit performs a plurality of operations. The operations includes collecting the 3D ultrasound data from an ultrasound probe and identifying a select set of the 3D ultrasound data corresponding to an object of interest within the volumetric ROI. The operations further include segmenting the object of interest from the select set of the 3D ultrasound data, generating a visualization plane of the object of interest, and displaying the visualization plane on the display.

Abstract: An imaging system and method are disclosed. An MR image and measured B0 field map of a target volume in a subject are reconstructed, where the MR image includes one or more bright and/or dark regions. One or more distinctive constituent materials corresponding to the bright regions are identified. Each dark region is iteratively labeled as one or more ambiguous constituent materials. Susceptibility values corresponding to each distinctive and iteratively labeled ambiguous constituent material is assigned. A simulated B0 field map is iteratively generated based on the assigned susceptibility values. A similarity metric is determined between the measured and simulated B0 field maps. Constituent materials are identified in the dark regions based on the similarity metric to ascertain corresponding susceptibility values. The MRI data is corrected based on the assigned and ascertained susceptibility values. A diagnostic assessment of the target volume is determined based on the corrected MRI data.

Abstract: A system and method for detecting motion is presented. The system and method includes identifying a region of interest in the plurality of images corresponding to a subject of interest. Furthermore, the system and method includes determining signal characteristics corresponding to the region of interest. In addition, the system and method includes generating a composite signal, where the composite signal comprises an aggregate of the signal characteristics corresponding to the region of interest. The system and method also includes analyzing the composite signal to detect motion in the region of interest.

Abstract: A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.

Abstract: A system and method for simulating an ultrasound scanning session is provided. The method includes acquiring an image of at least a portion of a simulated scan surface by a camera. A probe simulator having a visually coded pattern is maneuvered on the simulated scan surface. The method includes analyzing the acquired image to identify the visually coded pattern of the probe simulator maneuvered on the simulated scan surface. The method includes determining a position and orientation of the probe simulator based on the visually coded pattern identified in the acquired image. The method includes estimating a scan plane based at least in part on the determined position and orientation of the probe simulator. The method includes retrieving an ultrasound image from storage. The ultrasound image corresponds with the estimated scan plane. The method includes presenting the retrieved ultrasound image at a display system.

Abstract: A system (e.g., an ultrasound imaging system) is provided. The system includes an ultrasound probe configured to acquire three dimensional (3D) ultrasound data of a volumetric region of interest (ROI). The system further includes a display, a memory configured to store programmed instructions, and a controller circuit. The controller circuit includes one or more processors. The controller circuit is configured to execute the programmed instructions stored in the memory. When executing the programmed instructions, the controller circuit performs a plurality of operations. The operations includes collecting the 3D ultrasound data from an ultrasound probe and identifying a select set of the 3D ultrasound data corresponding to an object of interest within the volumetric ROI. The operations further include segmenting the object of interest from the select set of the 3D ultrasound data, generating a visualization plane of the object of interest, and displaying the visualization plane on the display.

Abstract: The present disclosure relates to a method for assessing biological features, the method that includes rendering a graphical user interface allowing a user to select patient cohort information defining one or more characteristics of a patient cohort and allowing a user to select an analysis technique from a plurality of analysis techniques, wherein the analysis technique operates on the patient data from both the first data acquisition modality and the second data acquisition modality to generate a derived variable. The method also includes allowing a user to define a threshold for the derived variable to define a first patient group above the threshold and a second patient group below the threshold for each patient of a patient cohort.

Abstract: A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.

Abstract: An imaging system and method are disclosed. An MR image and measured B0 field map of a target volume in a subject are reconstructed, where the MR image includes one or more bright and/or dark regions. One or more distinctive constituent materials corresponding to the bright regions are identified. Each dark region is iteratively labeled as one or more ambiguous constituent materials. Susceptibility values corresponding to each distinctive and iteratively labeled ambiguous constituent material is assigned. A simulated B0 field map is iteratively generated based on the assigned susceptibility values. A similarity metric is determined between the measured and simulated B0 field maps. Constituent materials are identified in the dark regions based on the similarity metric to ascertain corresponding susceptibility values. The MRI data is corrected based on the assigned and ascertained susceptibility values. A diagnostic assessment of the target volume is determined based on the corrected MRI data.

Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.