A computer-implemented method includes, in an embodiment, receiving first X-ray projections of an examination volume in respect of a first X-ray energy and second X-ray projections in respect of a second X-ray energy, the first and second X-ray energies differing. The method further includes determination of a multienergetic real image data record of the examination volume based upon the first and second X-ray projections; selection of first voxels of the multienergetic real image data record based upon the multienergetic real image data record; selection of second voxels of the multienergetic real image data record based upon the first X-ray projections and the second X-ray projections, the first voxels including the second voxels and the second voxels mapping contrast medium in the examination volume. The method further includes provision of a constraint image data record and/or a difference image data record based upon the second voxels.

A method for generating a personalized scaffold for an individual includes acquiring images of an anatomy of interest corresponding to an intended scaffold location and acquiring test results related to the anatomy of interest. One or more functional specifications are generated based on the images and test results and one or more scaffold parameters are selected based on the functional specifications. A final scaffold may then be generated using the one or more scaffold parameters.

A method is disclosed for determining result data based upon medical measurement data of an examination object. Within the method, a high-dimensional first parameter space is formed, in which measurement values of the various measurements are represented with the aid of value tuples. The measurement values of the various measurements are assigned to a value tuple based on their spatial arrangement in the examination object and/or on their temporal arrangement relative to one another. In the first parameter space, the value tuples are analyzed, using at least one mapping function to at least one further parameter space including a lower dimension than the first parameter space, in order to obtain result data. Furthermore, the result data is output, preferably visualized. In addition, a corresponding device for determining result data is described.

The present disclosure is related to methods and systems for image reconstruction including accelerated forward transformation with an Artificial Neural Network (ANN).

The disclosure relates to a system and method for determining and pre-fetching projection data in image reconstruction. The method may include: determining a sequence of a plurality of pixels including a first pixel and a second pixel relating to the first pixel; determining a first geometry calculation used for at least one processor to access a first set of projection data relating to the first pixel from a first storage; determining a second geometry calculation based on the first geometry calculation; determining a first data template relating to the first pixel and a second data template relating to the second pixel based on the second geometry calculation; and pre-fetching a second set of projection data based on the first data template and the second data template, from a storage.

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.

An image generation method of an interior CT includes the following steps: a step for obtaining interior CT projection data by measuring only all of quantum beams passing through a region of interest (ROI) in an inside of photographing object in a geometrical system for CT measurement; a step for obtaining partial entire projection data by measuring quantum beams passing through an entire of said photographing object from a segment in an outside of said photographing object in said geometrical system for CT measurement; and a processing step for exactly reconstructing said ROI upon basis of said interior CT projection data obtained and said partial entire projection data.

The present disclosure relates to a system, method and storage medium for generating an image. At least one processor, when executing instructions, may perform one or more of the following operations. When raw data is received, a plurality of iterations may be implemented. During each iteration, a first voxel value relating to a first voxel in an image is calculated; at least a portion of a second voxel may be continuously changed with respect to at least a portion of the first voxel value; the image may be transformed to a projection domain to generate an estimated projection based on the first voxel value and the second voxel value; a projection error may be obtained based on the estimated projection and the raw data; and the image may be corrected or updated based on the projection error.

The disclosure relates to a system and method for determining and pre-fetching projection data in image reconstruction. The method may include: determining a sequence of a plurality of pixels including a first pixel and a second pixel relating to the first pixel; determining a first geometry calculation used for at least one processor to access a first set of projection data relating to the first pixel from a first storage; determining a second geometry calculation based on the first geometry calculation; determining a first data template relating to the first pixel and a second data template relating to the second pixel based on the second geometry calculation; and pre-fetching a second set of projection data based on the first data template and the second data template, from a storage.

The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.