A method of calculating a system matrix for time-of-flight (TOF) list-mode reconstruction of positron-emission tomography (PET) images, the method including determining a TOF geometric projection matrix G including effects of object attenuation; estimating an image-blurring matrix R in image space; obtaining a diagonal matrix D that includes TOF-based normalization factor; and calculating the system matrix H as H=DGR.

The present disclosure relates to the field of a cabinet x-ray incorporating an x-ray tube, an x-ray detector, and an optical camera for the production of organic and non-organic images. The computing device receives data including video data from an optical camera, a laser detector, an infrared detector, an ultrasonic detector, or a weight scale or pressure sensor, and determines automatically, based on the resultant data, if a sample/specimen has been left in the sample chamber. In particular, the disclosure relates to a system and method with corresponding apparatus for automatic detection if a sample/specimen has been left in the sample chamber without having to open the chamber door.

An imaging method may include obtaining imaging data associated with a region of interest (ROI) of an object. The imaging data may correspond to a plurality of time-series images of the ROI. The imaging method may also include determining, based on the imaging data, a data set including a spatial basis and one or more temporal bases. The spatial basis may include spatial information of the imaging data. The one or more temporal bases may include temporal information of the imaging data. The imaging method may also include storing, in a storage medium, the spatial basis and the one or more temporal bases.

In a method and an evaluation device for the evaluation of projection data of an object being examined, which are determined along a trajectory in a multiplicity of projection positions relative to a co-ordinate origin, a particular trajectory function is determined for the projection positions, for each of a multiplicity of positions from a reconstruction region of dimension n by establishing an offset (d) and a direction vector at the co-ordinate origin, establishing a hyperplane of dimension n?1 which runs perpendicular to the direction vector and has an offset to the co-ordinate origin, establishing a number of intersection points where the hyperplane intersects the trajectory, establishing a derivative vector of the trajectory according to its trajectory path and calculating the derivative vector in the projection position, and establishing an absolute value of a scalar product between the derivative vector and the position and dividing the absolute value by the number. The determined trajectory functions are transformed to a frequency domain of dimension n and the projection data are evaluated by means of the transformed trajectory functions.

A method for the reconstruction of a test part in an X-ray CT method in an X-ray CT system, which has an X-ray with a focus, an X-ray detector, and a manipulator which moves the test part within the X-ray CT system. To generate recordings of the test part in various positions, the manipulator travels a predefinable parameterizable path-curve and makes recordings at triggered positions. For each recording, the position of the manipulator is determined and the respective associated projective geometry is calculated. Thereafter, a further path curve is followed having different parameters from the preceding path curve. The path curve is determined iteratively by means of an optimization algorithm, at the value of which the quality function is minimal. For each test part, a CT reconstruction is carried out by means of a suitable algorithm with reference to the allocation of the individual recordings to the respective projective geometry.

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.

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 present invention is directed to a novel tomographic reconstruction framework that explicitly models the covariance of the measurements in the forward model using a mean measurement model and a noise model. This more accurate model can result in improved image quality, increased spatial resolution, and enhanced detectabilityin particular, for imaging scenarios where there are features on the order of the correlation length in the projection data. Applications where these methods might have particular benefit include high resolution CBCT applications as in CBCT mammography (where very fine calcifications are difficult to resolve due to detector blur and correlation), musculoskeletal imaging (where fine bone details are important to the imaging task), or in temporal bone imaging where the fine detail structures of the inner ear are also difficult to resolve with standard imaging techniques.

The present invention provides an apparatus and a method for enhancing spatial resolution of a CT image and a CT imaging system, the method comprising: acquiring an original CT projection curve; performing deconvolution for projection data on the original CT projection curve in a tube sampling direction or a texture direction of the original CT projection curve; and reconstructing an image according to the projection data after deconvolution.