Provided is an image processing device including a hardware processor. The hardware processor: obtains a static image and a dynamic image of a same subject by radiographic imaging; detects, on the static image, a first analysis target area; detects, on the dynamic image, a second analysis target area corresponding to the first analysis target area; analyzes the second analysis target area of the dynamic image to generate a functional information representative from change caused by biological motion; deforms and positions the second analysis target area so that the second analysis target area corresponds to the first analysis target area; overlays the functional information representative of the deformed and positioned second analysis target area on the static image.

A radiographic image analysis apparatus includes a hardware processor that calculates an area of a lung field from a chest image obtained by radiation imaging of a chest in one direction, and estimates a residual volume, a functional residual capacity, a total lung capacity or a residual volume ratio of the lung field, based on the calculated lung field area.

Methods include processing image data through a plurality of network stages of a progressively holistically nested convolutional neural network, wherein the processing the image data includes producing a side output from a network stage m, of the network stages, where m>1, based on a progressive combination of an activation output from the network stage m and an activation output from a preceding stage m−1. Image segmentations are produced. Systems include a 3D imaging system operable to obtain 3D imaging data for a patient including a target anatomical body, and a computing system comprising a processor, memory, and software, the computing system operable to process the 3D imaging data through a plurality of progressively holistically nested convolutional neural network stages of a convolutional neural network.

According to one embodiment, an X-ray computed tomography apparatus includes processing circuitry. The processing circuitry generates first tasks and second tasks, for each of a plurality of reconstruction requests for image reconstruction. The processing circuitry manages an order of execution of the first tasks and the second tasks such that the second tasks are executed after the first tasks. The processing circuitry executes the first tasks and the second tasks in the managed order of execution, based on the projection data set.

An imaging apparatus and associated methods are provided to efficiently estimate scatter during multi-fraction treatments for improved quality and workflow. Estimated scatter from one fraction during a treatment course can be utilized during subsequent fractions, allowing for measurements with higher scatter-to-primary ratios. The quality of scatter estimates can be maintained, while workflow improves and dosage decreases. Scan configuration limits can be utilized to maintain a minimum level of scatter measurement quality. Patient information can be monitored to ensure that prior fraction scatter estimates are still applicable to current patient status.

In one embodiment, a medical image processing apparatus includes a memory storing a predetermined program and processing circuitry. The processing circuitry is configured, by executing the predetermined program, to acquire a plurality of images that are obtained by imaging a same object and are different in imaging time, classify tissue property of the object into a plurality of tissue-property classes by analyzing the tissue property of the object based on pixel values of respective regions of the plurality of images, assign the classified tissue-property classes to the respective regions of the plurality of images, and estimate change in disease state of the object from change in the classified tissue-property classes in respectively corresponding regions of the plurality of images.

A portable barium test apparatus is used to perform Modified Barium Swallow Studies (MBSS) on patients in different locations, such as the home of the patient or a care facility. The apparatus includes a hand truck, a U-shaped support, a height-adjusting track, a support carriage, an X-ray generator, and an image-capturing device. The hand truck allows the medical staff to transport the apparatus to the desired location to perform the procedure. The U-shaped support maintains the X-ray generator and the image-capturing device at the desired arrangement to perform the procedure. The height-adjusting track enables the repositioning of the U-shaped support at the desired height to accommodate the patient. The support carriage connects the U-shaped support to the height-adjusting track and maintains the arrangement of the U-shaped support during the procedure. The X-ray generator and the image-capturing device enable the medical staff to perform the MBSS procedure on the patient.

A photonic integrated circuit, PIC, comprising a plurality of semiconductor layers on a substrate, the plurality of semiconductor layers forming a PIN or PN doping structure, the PIC comprising a waveguide arranged for conducting light waves; an optical element connected to the waveguide, wherein the optical element, in operation, is in reverse-bias mode, and wherein the optical element comprises a contact layer arranged for connecting to a voltage source; wherein the waveguide comprises conducting contacts proximal to the optical element, and wherein the PIC further comprises at least one isolation section arranged in between the optical element and the conducting contacts. Corresponding methods of operation of such a PIC are also presented herein.

Described herein is a medical linear accelerator including an accelerator target structure constructed of a material having a thickness of less than 0.2 radiation lengths, and an accelerator structure to receive an electromagnetic wave and generate an output therapy dose rate of electrons having a beam energy between 4-25 mega-electronvolts (MeV).

The present disclosure generally relates to a system and method for determining medical maladies in radiological imaging. In exemplary embodiments, neural network models may be established to read radiological images for characteristics associated with for example, pulmonary diseases. Embodiments may build predictions from an ensemble of model outputs that predict various pulmonary characteristics from features identified in the images. The system may be used to process a selected patient's X-ray lung image to make predictions of whether pulmonary disease is present in the image.