A shape data generation method includes: generating a target shape of transformation from plural tomographic images of an object; specifying, from among plural vertices of a first shape that is a reference shape of the object, plural first vertices, each first vertex of which satisfies a condition that a normal line of the first vertex passes through a point that is located on the target shape and is located on a boundary of the object in any one of the plural tomographic images; identifying, for each of the plural first vertices, a second vertex that internally divides a segment between the first vertex and the point; transforming the first shape so as to put each of the plural first vertices on a corresponding second vertex; setting a shape after the transforming to the first shape; and executing the first specifying and the subsequent processings a predetermined number of times.

A method for generating a combined projection image from a medical inspection object, includes steps of capturing a set of initial projection images; reconstructing a first and a second three-dimensional volume from the set of initial projection images; generating a first re-projection image from the first three-dimensional volume and a second re-projection image from the second three-dimensional volume; weighting the first re-projection image and the second re-projection image; and combining the weighted first re-projection image and second re-projection image for generating the combined projection image.

A system and method for classifying compartments at a security checkpoint includes classifying a compartment into a first category or a second category using a first stage neural network that analyzes a three-dimensional representation of the compartment extracted from an imaging device coupled to the computing system, and in response to classifying the compartment into the second category, screening the compartment using a second stage neural network that performs image segmentation to isolate a hazardous object present in the compartment.

A novel proton imaging system incorporates positron emission detections to enhance proton therapy treatment preparation and procedural efficiencies while reducing operational costs associated with proton therapy. In one case, the novel proton imaging system incorporating a positron emission tomography (PET) module enables rapid on-the-fly in vivo range verification for proton therapy using position information from short-lived positron emitters produced during treatment. This unique in vivo range verification method produces more streamlined, accurate, and cost-effective results relative to conventional proton imaging systems. In another case, the novel proton imaging system incorporating the PET module provides a unique combinatory PET/pCT (proton computer tomography) scanning that creates more accurate maps for proton therapy planning for metabolically-active tumors. This proton imaging system also utilizes a novel concept of “virtual protons” originating from in vivo range verification measurements that mimic proton particle's characteristics for more accurate proton computer tomography (pCT) or computer tomography (CT).

Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

A computed tomography (CT) image display system (10) includes a mapping unit (32), which receives reconstructed volumetric image data (28) of a subject with values in Hounsfield Units (HU), and a set of reference settings (34), adjusts the set of reference settings (34) to an adjusted set of reference settings (35) according to a pixel-value distribution analysis of the HU values selected according to the reference settings (34), and maps the values in HU to gray scale values according to the adjusted reference settings (35).

Embodiments provide the capability to determine a digital 3D model of a patient's teeth obtains projection images acquired by scanning a negative impression of the patient's teeth using a computed tomographic imaging apparatus, where the impression includes a radio-opaque transfer element for a dental implant or crown post. One exemplary method reconstructs, from the projection images, an air volume model within the reconstructed volume and bounded by a transition surface defined according to the negative impression. A transfer element volume model of the transfer element is defined, segmented from the air volume model and from the transition surface. A combined digital 3D model of the patient's teeth is formed according to the air volume model and transfer element volume model. At least a portion of the combined digital 3D model of the teeth is displayed.

Provided is a preview image generating section configured to generate preview image during radiography for the purpose of providing a radiation tomography apparatus that allows suppression of unnecessary imaging time by displaying a condition of an image during the radiography in the process of diagnosis. An operator can recognize from the preview image how a subject appears in the image in a radiation tomography apparatus also during the radiography. This allows stopping the radiography before a diagnostic image having a suitable level of clearness for diagnosis is generated. As a result, a shorter imaging time is achieved, and burden to the subject can be suppressed.

In a method and medical imaging apparatus for depiction of medical imaging data, medical imaging data of the examination object are acquired over a period of time, and an artifact parameter is established, which characterizes artifacts that occur as a result of breathing of the examination object during the period of time. The medical imaging data are displayed on a display screen together with a depiction of the artifact parameter.

Systems and methods are shown for developing physics-based high resolution biomechanical head and neck deformable models for generating ground-truth deformations that can be used for validating both image registration and adaptive RT frameworks.