A radiation imaging control apparatus is provided that includes a camera imaging control unit configured to control a camera apparatus to image an implementation state of a radiation imaging examination, a subject body shape recognition unit configured to recognize a body shape in an imaging part of a subject by using a camera image imaged by the camera apparatus under a control of the camera imaging control unit, a specifying unit configured to specify a radiation imaging setting related to the radiation imaging examination by using the body shape in the imaging part of the subject recognized by the subject body shape recognition unit, and a selecting unit configured to select the radiation imaging setting specified by the specifying unit as setup information of the radiation imaging examination.

An imaging system, comprising a shooting operation interface that operates to form an image of a subject, and a processor that has a bio-information acquisition section and a stress determination section, wherein the bio-information acquisition section acquires bio-information of an operator when, during shooting awaiting action where an instant for acquiring still images is awaited, the shooting operation interface is operated, and the stress determination section determines stress conditions that shooting actions place on the operator based on the bio-information that has been acquired using the bio-information acquisition section.

A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Systems and methods for improving soft tissue contrast, characterizing tissue, classifying phenotype, stratifying risk, and performing multi-scale modeling aided by multiple energy or contrast excitation and evaluation are provided. The systems and methods can include single and multi-phase acquisitions and broad and local spectrum imaging to assess atherosclerotic plaque tissues in the vessel wall and perivascular space.

A system for dental X-ray imaging includes an intra oral imaging plate. A tag is attached directly to the intra oral imaging plate. The tag attached directly to the intra oral imaging plate is configured to have tag information remotely read from the tag or the tag is configured to have tag information written to the tag. The tag information is configured to control a portion of the system adapted to perform at least one operation controlled by the tag information.

Described herein is an implantable device having a sensor configured to detect an amount of an analyte, a pH, a temperature, strain, or a pressure; and an ultrasonic transducer with a length of about 5 mm or less in the longest dimension, configured to receive current modulated based on the analyte amount, the pH, the temperature, or the pressure detected by the sensor, and emit an ultrasonic backscatter based on the received current. The implantable device can be implanted in a subject, such as an animal or a plant. Also described herein are systems including one or more implantable devices and an interrogator comprising one or more ultrasonic transducers configured to transmit ultrasonic waves to the one or more implantable devices or receive ultrasonic backscatter from the one or more implantable devices. Also described are methods of detecting an amount of an analyte, a pH, a temperature, a strain, or a pressure.

Disclosed herein is an X-ray apparatus for acquiring a medical image, and a method of using said X-ray apparatus, said method comprising the steps of: acquiring an original radiation image of a target object and capturing condition information of the object; acquiring a scatter radiation image related to the original radiation image by inputting the original radiation image and the capturing condition information to a learning network model configured to estimate scatter radiation; and acquiring a scatter radiation-processed medical image from the original radiation image on the basis of the original radiation image and the scatter radiation image, wherein the learning network model configured to estimate scatter radiation is a learning network model taught using a plurality of scatter radiation images and a plurality of pieces of capturing condition information related to each of the plurality of scatter radiation images.

A method and system is disclosed for acquiring image data of a subject. The image data can be collected with an imaging system with at least two different power characteristics. The image data can be reconstructed using dynamic or enhanced reconstruction techniques.

A method for customizing an interactive control boundary includes positioning a virtual implant model relative to a virtual bone model based on a user input, and extracting reference feature information associated with the virtual implant model, wherein the reference feature information describes one of a point, a line, a plane, and a surface associated with the virtual implant model. The method further includes mapping the extracted reference feature information to the virtual model of the bone, and receiving information indicative of a positional landmark associated with the bone, then estimating an intersection between the positional landmark and the mapped reference feature and generating a virtual boundary based, at least in part, on the estimated intersection between the positional landmark and the mapped reference feature.