An apparatus (1) for providing image quality feedback during a medical imaging examination includes at least one electronic processor (20) programmed to: receive a live video feed (17) of a display (6) of an imaging device controller (4) of an imaging device (2) performing the medical imaging examination; extract a preview image (12) from the live video feed; perform an image analysis (38) on the extracted preview image to determine whether the extracted preview image satisfies an alert criterion; and output an alert (30) when the extracted preview image satisfies the alert criterion as determined by the image analysis.

A system and method for reviewing a tomosynthesis image data set comprising volumetric image data of a breast, the method comprising, in one embodiment, causing an image or a series of images from the data set to be displayed on a display monitor and selecting or indicating through a user interface an object or region of interest in a presently displayed image of the data set, thereby causing an image from the data set having a best focus measure of the user selected or indicated object or region of interest to be automatically displayed on the display monitor.

Methods and systems are provided for cardiac computed tomography imaging. In one embodiment, a method comprises reconstructing an image from projection data acquired during a scan with a reconstruction time determined based on a model relating a timing of an event to be imaged to a heart rate measured during the scan. In this way, the timing of a reconstruction may be consistently applied for a series of reconstructions, thereby inherently registering the reconstructions.

A method includes moving a radiation source and a radiation detector along a scan path substantially transverse to a longitudinal axis of a patient. A beam of radiation is emitted from the radiation source. The beam of radiation is detected at the radiation detector. The detected beam is processed so as to form a first image of a first area of the patient along the scan path.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

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 system for performing a scan of internal structures of an object/patient is provided. The system includes a radiation source operative to emit a radiation beam, a radiation detector operative to receive the radiation beam and generate an output signal based at least in part on the received radiation beam, and a controller in electronic communication with the radiation source and the radiation detector and operative to generate at least one image of the object/patient. The controller is further operative to determine an offset of the at least one image relative to an image reference and to employ the offset to automatically align the at least one image with the image reference without the need for stopping the operation of the radiation source and detector to reposition the object/patient being scanned.

An imaging support device used in a radiography apparatus including a radiation source and a radiation image detector that detects a radiation image of a subject on the basis of radiation emitted from the radiation source and transmitted through the subject, and includes an optical camera that outputs an optical image by optically imaging a region including an irradiation field of the radiation applied to the subject from the radiation source, and at least one processor, in which the processor executes a determination process of determining a possibility of reimaging in a case where radiography is performed on the basis of the optical image acquired by the optical camera before start of the radiography by using a trained model that has learned a relationship between the optical image captured during the radiography and a necessity of recapturing the radiation image that is captured during the radiography.

One or more example embodiments relates to a computer-implemented method for providing key elements of the examination region in an X-ray image.

Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks. The CALs may be further fed to the system for checking misalignment of any of the CALs and correcting the misaligned CALs.