When acquiring detailed utilization information from imaging equipment in a cross-vendor approach, one or more sensors (16, 18, 22, 24) are positioned within a data security zone (14) in which an imaging procedure is performed. Sensor data is pre-processed on an isolated processing unit (20) to remove any sensitive information and keep a selection of features only. The resultant feature pattern is transmitted outside of the data security zone to a processing unit (28) where pattern recognition is performed on feature pattern to identify the type of imaging modality, scan, etc. being performed as well as to determine whether the scan is being performed according to schedule.

When acquiring detailed utilization information from imaging equipment in a cross-vendor approach, one or more sensors (16, 18, 22, 24) are positioned within a data security zone (14) in which an imaging procedure is performed. Sensor data is pre-processed on an isolated processing unit (20) to remove any sensitive information and keep a selection of features only. The resultant feature pattern is transmitted outside of the data security zone to a processing unit (28) where pattern recognition is performed on feature pattern to identify the type of imaging modality, scan, etc. being performed as well as to determine whether the scan is being performed according to schedule.

Provided are a linear structure extraction device, a method, a program, and a learned model which can detect a linear structure in an image. A linear structure extraction device according to an embodiment of the present disclosure includes a learning model that is learned to receive an input of the image and output, as a prediction result, one or more element points which constitute the linear structure from the image, in which the learning model includes a first processing module that receives the image and generates a feature map representing a feature amount of the image by convolution processing, and a second processing module that calculates a shift amount from a unit center point to the element point of the linear structure closest to the unit center point, for each unit obtained by dividing the feature map into a plurality of the units including regions having a predetermined size in a grid pattern.

A division unit 22 divides a brain included in a first brain image into a plurality of regions by performing registration between the first brain image including a brain of a subject and a standard brain image divided into a plurality of regions. A registration unit 23 performs registration between the first brain image and a second brain image that includes the brain of the subject and has a different imaging date and time from the first brain image. A change amount acquisition unit 24 acquires the amount of change from a corresponding region in the brain included in the first brain image, for at least one region of the plurality of regions in the brain included in the second brain image, based on the registration result.

A radiation detector including: a substrate formed with plural pixels that accumulate electrical charges generated in response to light converted from radiation in a pixel region at an opposite-side surface of a base member to a surface including a fine particle layer; the base member being flexible and is made of resin and that includes a fine particle layer containing inorganic fine particles having a mean particle size of from 0.05 μm to 2.5 μm, a conversion layer provided at the surface of the base member provided with the pixel region and configured to convert the radiation into light; and a reinforcement substrate provided to at least one out of a surface on the substrate side of a stacked body configured by stacking the substrate and the conversion layer, or a surface on the conversion layer side of the stacked body.

A radiation detector including: a substrate formed with plural pixels that accumulate electrical charges generated in response to light converted from radiation in a pixel region at an opposite-side surface of a base member to a surface including a fine particle layer; the base member being flexible and is made of resin and that includes a fine particle layer containing inorganic fine particles having a mean particle size of from 0.05 μm to 2.5 μm, a conversion layer provided at the surface of the base member provided with the pixel region and configured to convert the radiation into light; and a reinforcement substrate provided to at least one out of a surface on the substrate side of a stacked body configured by stacking the substrate and the conversion layer, or a surface on the conversion layer side of the stacked body.

A robotic surgical system comprising at least two robotic arms having co-ordinate systems known relative to each other, one of the arms carrying an X-ray source, and the other an imaging detector plate. The arms are disposed to enable an image to be generated on the region of interest of a subject. One of the arms can additionally or alternatively carry a surgical tool or tool holder, such that the pose of the tool is known in the same co-ordinate system as that of an image generated by the X-ray source and detector. Consequently, any surgical procedure planned on such an X-ray image can be executed by the tool with high accuracy, since the tool position is known in the image frame of reference. This enables the surgeon to accurately position his tool in a real-time image without the need for an external registration procedure.

A robotic surgical system comprising at least two robotic arms having co-ordinate systems known relative to each other, one of the arms carrying an X-ray source, and the other an imaging detector plate. The arms are disposed to enable an image to be generated on the region of interest of a subject. One of the arms can additionally or alternatively carry a surgical tool or tool holder, such that the pose of the tool is known in the same co-ordinate system as that of an image generated by the X-ray source and detector. Consequently, any surgical procedure planned on such an X-ray image can be executed by the tool with high accuracy, since the tool position is known in the image frame of reference. This enables the surgeon to accurately position his tool in a real-time image without the need for an external registration procedure.

A PET detecting module may include a scintillator array configured to receive a radiation ray and generate optical signals in response to the received radiation ray. The scintillator array may have a plurality of rows of scintillators arranged in a first direction and a plurality of columns of scintillators arranged in a second direction. A first group of light guides may be arranged on a top surface of the scintillator array along the first direction. The light guide count of the first group of light guides may be less than the row count of the plurality of rows of scintillators. A second group of light guides may be arranged on a bottom surface of the scintillator array. The light guide count of the second group of light guides may be less than the column count of the plurality of columns of scintillators.

An information processing apparatus includes a soft part image generation unit that generates a soft part image representing a soft part region with a soft tissue of a subject from a first radiographic image and a second radiographic image acquired by radiation having different energy distributions transmitted through the subject, and a muscle mass derivation unit that derives a muscle mass based on a pixel value for each pixel of the soft part region of the soft part image.