Abstract:
An imaging system includes a computed tomography (CT) acquisition unit and at least one processor. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object. The at least one processor is operably coupled to the CT acquisition unit, and configured to reconstruct an initial image using the CT imaging information, the initial image including at least one object representation portion and at least one artifact portion; identify at least one region of the initial image containing at least one artifact and isolate the at least one artifact by analyzing the initial image using an artifact dictionary and a non-artifact dictionary, the artifact dictionary including entries describing corresponding artifact image portions, the non-artifact dictionary including entries defining corresponding non-artifact image portions; and remove the at least one artifact from the initial image to provide a corrected image.
Abstract:
Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.
Abstract:
The present invention provides a system and method for generating a CT slice image. The system comprises an MIP image generation module, a region of interest determination module, an angle setting module, a curve determination module, a match module and a slice generation module. The MIP image generation module generates MIP images of a reconstructed image; the region of interest determination module determines an image range in an original slice, and determines the parts of the MIP images within the image range as regions of interest; the angle setting module rotates the regions of interest to a plurality of specific angles for a plurality of times; the curve determination module generates a plurality of two-dimensional projected curves of the regions of interest for the plurality of specific angles; the match module selects a two-dimensional projected curve matching with a part to be diagnosed based on features of the plurality of two-dimensional projected curves; the slice generation module determines a slice position range and a slice angle based on the features of the matched curve and the corresponding specific angle.
Abstract:
An imaging system is provided that includes a gantry having a bore extending therethrough; a plurality of image detectors attached to the gantry and radially spaced around a circumference of the bore such that gaps exist between image detectors along the circumference of the bore; an x-ray source attached to the gantry, wherein the x-ray source transmits x-rays across the bore towards at least two of the image detectors; wherein at least two image detectors detect both emission radiation and x-ray radiation.
Abstract:
The present invention provides an apparatus and method for beam hardening artifact correction of CT image, comprising a bone tissue image obtain module, a first correction module, an orthographic projection module, and a correction image obtaining module. The bone tissue image obtain module is used to extract a bone tissue image from a reconstructed original image; the first correction module is used to increase a current CT value of the bone tissue image; the orthographic projection module is used to perform an orthographic projection on the bone tissue image with the CT value being increased to obtain an orthographic projection data of the bone tissue image; the correction image obtaining module is used to perform image reconstruction according to the orthographic projection data of the bone tissue image described above and obtain a correction image.
Abstract:
A method is provided. The method includes acquiring a first dataset at a first energy spectrum and a second dataset at a second energy spectrum. The method also includes extracting a metal artifact correction signal using the first dataset and the second dataset or using a first reconstructed image and a second reconstructed image generated respectively from the first and the second datasets. The method further includes performing metal artifact correction on the first reconstructed image using the metal artifact correction signal to generate a first corrected image.
Abstract:
A collimator for an imaging system includes a first region comprising a first one-dimensional array of apertures along a channel direction, and a second region comprising a second one-dimensional array of apertures along the channel direction, wherein an aspect ratio of the apertures of the first region is greater than an aspect ratio of the second region.
Abstract:
An imaging system includes a rotatable gantry for receiving an object to be scanned, a generator configured to energize an x-ray source to generate x-rays, a detector positioned to receive the x-rays that pass through the object, and a computer. The computer is programmed to obtain knowledge of a metal within the object, scan the object using system scanning parameters, reconstruct an image of the object using a reconstruction algorithm, and automatically select at least one of the system scanning parameters and the reconstruction algorithm based on the obtained knowledge.
Abstract:
A method is provided. The method includes acquiring projection data of an object from a plurality of pixels, reconstructing the acquired projection data from the plurality of pixels into a reconstructed image, performing material characterization and decomposition of an image volume of the reconstructed image to reduce a number of materials analyzed in the image volume to two basis materials. The method also includes generating a re-mapped image volume for at least one basis material of the two basis materials, and performing forward projection on at least the re-mapped image volume for the at least one basis material to produce a material-based projection. The method further includes generating multi-material corrected projections based on the material-based projection and a total projection attenuated by the object, which represents both of the two basis materials, wherein the multi-material corrected projections include linearized projections.
Abstract:
The present approach relates to the training of a machine learning algorithm for image generation and use of such a trained algorithm for image generation. Training the machine learning algorithm may involve using multiple images produced from a single set of tomographic projection or image data (such as a simple reconstruction and a computationally intensive reconstruction), where one image is the target image that exhibits the desired characteristics for the final result. The trained machine learning algorithm may be used to generate a final image corresponding to a computationally intensive algorithm from an input image generated using a less computationally intensive algorithm.