Abstract: Systems and methods that normalize medical imaging data between different species and breeds of animals in order to allow for cross-species and crossbreed usage of machine trained models. Image data of a non-human subject is acquired, registered using a standardized model, and segmented using a machine trained model.

Abstract: Techniques for determining at least one characteristic of adipose tissue included in an anatomical structure are provided. The at least one characteristic of the adipose tissue is determined based on one or more segmented CT images using a trained neural network. For example, the at least one characteristic of the adipose tissue may be determined by inputting the one or more segmented CT images into the trained neural network. The one or more segmented CT images are obtained by segmenting each one of one or more CT images depicting the anatomical structure including the adipose tissue to determine a contour of the adipose tissue.

Abstract: A computer-implemented method is provided for classifying a malignancy risk of a kidney, in particular a human kidney. Imaging data of an anatomy of a subject patient at least partially includes a representation of a kidney of the subject patient. A first neural network segments at least one region of the kidney representation based on the imaging data. A second neural network detects one or more suspected lesions of the segmented kidney representation. A third neural network classifies the detected suspected lesion with a malignancy risk. The third neural network is a deep profiler.

Abstract: Techniques of determining a quantification of at least one characteristic of a muscle structure comprising at least one muscle and at least one tendon are disclosed. The quantification of the at least one characteristic of the rotator cuff may be determined by using at least one artificial neural network and based on one or more medical images depicting the muscle structure of a patient.

Abstract: Systems and methods for an intuitive display of one or more anatomical objects are provided. One or more 3D medical images of one or more anatomical objects of a patient are received. Correspondences between the one or more 3D medical images and points on a 2D map representing the one or more anatomical objects are determined. The 2D map is updated with patient information extracted from the one or more 3D medical images. The updated 2D map with the determined correspondences is output.

Abstract: A method for controlling an X-ray projection imaging device includes applying preferences for a serial radiography image acquisition to the X-ray projection imaging device. The method further includes executing the serial radiography image acquisition with the X-ray projection imaging device; recording data frames at different times during the serial radiography image acquisition; applying a trigger signal to the X-ray projection imaging device during the serial radiography image acquisition; and generating a snapshot image from a subset of the data frames, wherein the subset is chosen from the data frames recorded based on the trigger signal. A related system, a related control unit and a related X-ray projection imaging system are also disclosed.

Abstract: System and methods are provided for localizing a target object in a medical image. The medical image is discretized into a plurality of images having different resolutions. For each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, a sequence of actions is performed for modifying parameters of a target object in the respective image. The parameters of the target object comprise nonlinear parameters of the target object. The sequence of actions is determined by an artificial intelligence agent trained for a resolution of the respective image to optimize a reward function. The target object is localized in the medical image based on the modified parameters of the target object in the last image.

Abstract: Systems and methods are provided for generating a visualization of a lung fissure. Medical imaging data is processed to identify a lung mesh and fissures data. The lung mesh is augmented with the fissures data and projected onto a straight plane for rendering into a concise two-dimensional image in which completeness of the lung fissure may be detected.

Abstract: For patient positioning for scanning, a current pose of a patient is compared to a desired pose. The desired pose may be based on a protocol or a pose of the same patient in a previous examination. Any differences in pose, such as arm position, leg position, head orientation, and/or torso orientation (e.g., laying on side, back, or stomach), are communicated. By changing the current pose of the patient to be more similar to the desired pose, a more consistent and/or registerable dataset may be acquired by scanning the patient.

Abstract: A method for controlling an X-ray projection imaging device includes applying preferences for a serial radiography image acquisition to the X-ray projection imaging device. The method further includes executing the serial radiography image acquisition with the X-ray projection imaging device; recording data frames at different times during the serial radiography image acquisition; applying a trigger signal to the X-ray projection imaging device during the serial radiography image acquisition; and generating a snapshot image from a subset of the data frames, wherein the subset is chosen from the data frames recorded based on the trigger signal. A related system, a related control unit and a related X-ray projection imaging system are also disclosed.

Abstract: System and methods are provided for localizing a target object in a medical image. The medical image is discretized into a plurality of images having different resolutions. For each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, a sequence of actions is performed for modifying parameters of a target object in the respective image. The parameters of the target object comprise nonlinear parameters of the target object. The sequence of actions is determined by an artificial intelligence agent trained for a resolution of the respective image to optimize a reward function. The target object is localized in the medical image based on the modified parameters of the target object in the last image.

Abstract: A medical imaging phantom (18) is three-dimensionally printed (36). In one specific approach, three-dimensional printing (36) allows for any number of variations in phantoms (18). A library of different phantoms (18), different inserts, different textures, different densities, different organs, different pathologies, different sizes, different shapes, and/or other differences allows for defining a specific phantom (18) as needed. The defined phantom (18) is then printed (36) for calibration or other use in medical imaging.

Abstract: Systems and methods are provided for generating a visualization of a lung fissure. Medical imaging data is processed to identify a lung mesh and fissures data. The lung mesh is augmented with the fissures data and projected onto a straight plane for rendering into a concise two-dimensional image in which completeness of the lung fissure may be detected.

Abstract: The process of creating a 3D printer ready model of patient specific anatomy is automated. Instead of manual manipulation of the 3D mesh from imaging to create the 3D printer ready model, an automated manipulation is provided. The segmentation may be automated as well. In one approach, a transform between a predetermined mesh of anatomy and patient specific 3D mesh is calculated. The predetermined mesh has a corresponding 3D printer ready model. By applying the transform to the 3D printer ready model, the 3D printer ready model is altered to become specific to the patient. In addition, target manipulation that alters semantic parts of the anatomical structure may be included in 3D printing.

Abstract: A method operates an x-ray system for examining an object. In order to optimize the operation of an x-ray system, in particular the dose regulation and/or the image processing, the following method steps are performed. Radiation is passed through the object to be examined and a number of fluoroscopic images of the object are generated. A relevant image region of a fluoroscopic image is selected by a user of the x-ray system. An image-based dose regulation of the radiation dose used for passing radiation through the object is carried out using the selected image region and/or image processing is carried out using the selected image region.

Abstract: A computer-implemented method for providing a multi-modality visualization of a patient includes receiving one or more image datasets. Each image dataset corresponds to a distinct image modality. The image datasets are segmented into a plurality of anatomical objects. A list of clinical tasks associated with displaying the one or more image datasets are received. A machine learning model is used to determine visualization parameters for each anatomical object based on the list of clinical tasks. Then, a synthetic display of the image datasets is created by presenting each anatomical object according to its corresponding visualization parameters.

Abstract: A window regulator for raising and lowering a windowpane in a motor vehicle, and a vehicle door having at least one window regulator.

Abstract: A computer-implemented method for providing a multi-modality visualization of a patient includes receiving one or more image datasets. Each image dataset corresponds to a distinct image modality. The image datasets are segmented into a plurality of anatomical objects. A list of clinical tasks associated with displaying the one or more image datasets are received. A machine learning model is used to determine visualization parameters for each anatomical object based on the list of clinical tasks. Then, a synthetic display of the image datasets is created by presenting each anatomical object according to its corresponding visualization parameters.

Abstract: A medical imaging phantom (18) is three-dimensionally printed (36). In one specific approach, three-dimensional printing (36) allows for any number of variations in phantoms (18). A library of different phantoms (18), different inserts, different textures, different densities, different organs, different pathologies, different sizes, different shapes, and/or other differences allows for defining a specific phantom (18) as needed. The defined phantom (18) is then printed (36) for calibration or other use in medical imaging.

Abstract: A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.