The present invention relates to spectral correction. A spectral correction apparatus is described that is configured to identify a voltage fluctuation in the X-ray tube and to parameterize the high voltage fluctuation to correct the effective X-ray spectrum per individual frame.

A method for automatically reconstituting the architecture, along a reinforcing axis, of the reinforcement of a composite material, includes acquiring images of the reinforcement of the composite material, each image being acquired along a section plane perpendicular to the reinforcing axis; for each image acquired, detecting, using a neural network, barycentre and/or the circumference of each section of the reinforcing thread; for at least one acquired reference image, assigning a tag corresponding to a reinforcing thread, to each detected barycentre or circumference; for each other acquired image, assigning, to each detected barycentre and/or each detected circumference, the tag of the corresponding barycentre in the acquired reference image; reconstituting the architecture of each reinforcing thread from each detected barycentre and/or circumference having the tag of the reinforcing thread and the position on the reinforcing axis associated with the acquired image on which the barycentre and/or the circumference has been detected.

In some embodiments, the present specification describes methods for displaying a three-dimensional image of an isolated threat object or region of interest with a single touch or click and providing spatial and contextual information relative to the object, while also executing a view dependent virtual cut-away or rendering occluding portions of the reconstructed image data as transparent. In some embodiments, the method includes allowing operators to associate audio comments with a scan image of an object. In some embodiments, the method also includes highlighting a plurality of voxels, which are indicative of at least one potential threat item, in a mask having a plurality of variable color intensities, where the intensities may be varied based on the potential threat items.

The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

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.

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.

In a radiation therapy system, treatment X-rays are delivered to a target volume at the same time that imaging X-rays are also delivered to the target volume for generating image data of the target volume. That is, during an imaging interval in which imaging X-rays are delivered to the target volume, one or more pulses of treatment X-rays are also delivered to the target volume. In each pixel of an X-ray imaging device of the radiation therapy system, image signal is accumulated during portions of the imaging interval in which only imaging X-rays are delivered to the target volume and is prevented from accumulating in each pixel during the pulses of treatment X-rays.

In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

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.

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can “cut” the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information.