The present disclosure provides systems and methods for performing an automated scan preparation for a scan of a target subject. The automated scan preparation may include, for example, identifying a target subject to be scanned, generating a target posture model of the target subject, causing a movable component of a medical imaging device to move to its target position, controlling a light field of the medical imaging device, determining a target subject orientation, determining a dose estimation, selecting at least one target ionization chamber, determining whether the posture of the target subject needs to be adjusted, determining one or more scanning parameters (e.g., a size of a light field), performing a preparation check, or the like, or any combination thereof.

Described embodiments include a system that includes a display and a processor. The processor is configured to modify an image that includes a representation of a wall of an anatomical cavity, by overlaying an icon that represents an intrabody tool on a portion of the image that corresponds to a location of the intrabody tool within the anatomical cavity, and overlaying a marker on a portion of the representation of the wall that corresponds to a location at which the intrabody tool would meet the wall, were the intrabody tool to continue moving toward the wall in a direction in which the intrabody tool is pointing. The processor is further configured to display the modified image on the display. Other embodiments are also described.

An X-ray image processing apparatus of an embodiment includes processing circuitry. The processing circuitry acquires fluoroscopy-related information indicating at least one of a fluoroscopic image and a condition for collecting the fluoroscopic image. The processing circuitry evaluates the image quality of the fluoroscopic image based on the fluoroscopy-related information. The processing circuitry outputs identification information identifying whether to save the fluoroscopic image based on the evaluation result.

Systems and methods are provided for a deep learning-based digital breast tomosynthesis (DBT) image reconstruction that mitigates limited angular artifacts and improves in-depth resolution of the resulting images. The systems and methods may reduce the sparse-view artifacts in DBT via deep learning without losing image sharpness and contrast. A deep neural network may be trained in a way to reduce training-time computational cost. An ROI loss method may be used for further improvement on the resolution and contrast of the images.

An X-ray diagnostic apparatus according to an embodiment includes processing circuitry configured to improve quality of fourth data corresponding to a fourth number of views that is smaller than a first number of views by inputting the fourth data to a learned model generated by performing machine learning with second data corresponding to a second number of views as input learning data, and third data corresponding to a third number of views that is larger than the second number of views as output learning data, the second data and the third data being acquired based on first data corresponding to the first number of views. The fourth data is data acquired by tomosynthesis imaging.

A mobile radiography apparatus has radio-opaque markers, each marker coupled to a portion of the mobile radiography apparatus, wherein each of the markers is in a radiation path that extends from an x-ray source or x-ray sources. A detector is mechanically uncoupled from the x-ray source or x-ray sources for positioning behind a patient. Processing logic is configured to calculate a detector position with relation to the x-ray source or x-ray sources according to identified marker positions in acquired projection images, and to reconstruct a volume image according to the acquired projection images.

X-ray backscatter imaging (XBI) methods and systems are provided that enable depth-sensitive information to be obtained from images acquired during a single scan from a single side of an object being imaged. The depth-sensitive information is used in combination with other image information acquired during the scan to produce high-resolution 2-D or 3-D images, where at least one of the dimensions of the 2-D or 3-D image corresponds to depth in the object.

A display control unit displays, on a display unit, at least some of a plurality of images included in each of a plurality of image sets of the same object which have been captured at different imaging dates and times and each of which consists of the plurality of images including at least a plurality of tomographic images acquired by performing tomosynthesis imaging for the object. A setting unit sets at least one past image set, which was acquired at an imaging date and time before the latest imaging date and time and includes images at least some of which have been displayed, among the plurality of image sets as having been displayed.

Methods and systems for identifying a region of interest in breast tissue utilize artificial intelligence to confirm that a target lesion identified during imaging the breast tissue using a first imaging modality (e.g. x-ray imaging) has been identified using a second imaging modality (e.g. ultrasound imaging). A computing system operating a lesion matching engine utilizes a machine learning classifier algorithm trained on cases of x-ray images and corresponding ultrasound images in which lesions were identified for further analysis. The lesion matching engine analyzes a target lesion identified with x-ray imaging and a potential lesion identified with ultrasound imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

A system for multi-mode breast x-ray imaging which comprises a compression arm assembly for compressing and immobilizing a breast for x-ray imaging, an x-ray tube assembly, and an x-ray image receptor is provided. The system is configured for a plurality of imaging protocols and modes.