A mammography apparatus includes a diagnostic image acquisition unit that acquires a diagnostic image in which a calcification as a biopsy target is marked; a scout image acquisition unit that acquires a scout image obtained by imaging a mamma undergoing the biopsy from a specific direction; and a display unit that highlights a calcification (candidate for biological tissue examination) in the scout image which matches at least the marked calcification in the diagnostic image.

Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.

With a diagnostic imaging support apparatus, a diagnostic imaging support method, and a diagnostic imaging support program, an optimum image for extracting a contour can be automatically selected from a superimposed image obtained by superimposing a plurality of images of different types. A diagnostic imaging support apparatus 1 includes: an accepting unit 22 that accepts a specification of the position of a predetermined defined region R on a superimposed image G obtained by superimposing a plurality of images of different types including a target image G0 that is a target on which a contour is created; a selection unit 23 that selects an image for extracting a contour on the basis of image information about regions R0, R1, and R2, in the plurality of images of different types, each corresponding to the accepted defined region R; and a contour extraction unit 24 that extracts the contour from the selected image.

The present disclosure provides a computer-implemented method, a device, and a computer program product for radiographic bone mineral density (BMD) estimation. The method includes receiving a plain radiograph, detecting landmarks for a bone structure included in the plain radiograph, extracting an ROI from the plain radiograph based on the detected landmarks, estimating the BMD for the ROI extracted from the plain radiograph by using a deep neural network.

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

This invention relates to an image processing device (1) comprising an input (2) for receiving image data representative of a region of interest in the body of a patient from a medical X-ray imaging apparatus (100). The image data comprises a first dark-field image obtained for a first X-ray spectrum and a second dark-field image obtained for a second, different, X-ray spectrum. A combination unit (3) provides a combination image that is representative of a medical condition map, e.g. a lung condition map, by combining the first dark-field image and the second dark-field image.

A processor functions as a trained neural network that derives an estimation result relating to a three-dimensional bone density of a bone part from a simple radiation image acquired by simply imaging a subject including the bone part, or a DXA scanning image acquired by imaging the subject by a DXA method. The trained neural network learns using, as teacher data, (i) two radiation images or the like acquired by imaging the subject including the bone part with radiation having different energy distributions, and a two-dimensional bone density of the bone part included in the two radiation images or the like, or (ii) the radiation image or the like of the subject or a bone part image representing the bone part of the subject, the two-dimensional bone density of the bone part included in the radiation image or the like, or the bone part image, and the three-dimensional bone density of the bone part of the subject.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.