Abstract: Methods and systems are provided for reconstructing and automatically segmenting an image. In one embodiment, a method comprises acquiring projection data, the projection data comprising higher energy projection data and lower energy projection data, generating a first image from the projection data, generating a second image from the projection data, segmenting the second image to generate segments, and segmenting the first image based on the segments of the second image. In this way, an image which may otherwise prove challenging for an automatic segmentation process may be accurately segmented without sacrificing textural details of the image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

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: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.

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.

Abstract: The present approach provides a non-invasive methodology for estimation of coronary flow and/or fractional flow reserve. In certain implementations, various approaches for personalizing blood flow models of the coronary vasculature are described. The described personalization approaches involve patient-specific measurements and do not assume or rely on the resting coronary flow being proportional to myocardial mass. Consequently, there are fewer limitations in using these approaches to obtain coronary flow and/or fractional flow reserve estimates non-invasively.

Abstract: A collimator for use in a CT system made of an X-ray absorbing material, the collimator comprises an imaging aperture having a first width for passage of a first X-ray beam, the first X-ray beam being used for X-ray imaging, and a tracking aperture having a second width for passage of a second X-ray beam, the second X-ray beam being used for X-ray beam tracking.

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: 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: 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: Various methods and systems are provided for estimating and compensating for table deflection in reconstructed images. In one embodiment, a method for computed tomography (CT) imaging comprises reconstructing images from data acquired during a helical CT scan where table deflection parameters are estimated and the reconstruction is adjusted based on the table deflection parameters. In this way, images may be reconstructed without artifacts caused by table deflection.

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.

Abstract: An imaging system includes a computed tomography (CT) acquisition unit and a processing unit. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The processing unit includes at least one processor operably coupled to the CT acquisition unit. The processing unit is configured to control the CT acquisition unit to collect at least one sample projection during rotation of the CT acquisition unit about the object to be imaged, compare an intensity of the at least one sample projection to an intensity of a reference projection, select a time to perform an imaging scan based on the comparison of the intensity of the at least one sample projection to the intensity of the reference projection, and control the CT acquisition unit to perform the imaging scan.

Abstract: An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.

Abstract: Methods and systems are provided for reconstructing and automatically segmenting an image. In one embodiment, a method comprises acquiring projection data, the projection data comprising higher energy projection data and lower energy projection data, generating a first image from the projection data, generating a second image from the projection data, segmenting the second image to generate segments, and segmenting the first image based on the segments of the second image. In this way, an image which may otherwise prove challenging for an automatic segmentation process may be accurately segmented without sacrificing textural details of the image.

Abstract: Methods and systems are provided for reconstructing and automatically segmenting an image. In one embodiment, a method comprises generating a first image from acquired projection data based on an iterative reconstruction algorithm, generating a second image from the acquired projection data based on a modified iterative reconstruction algorithm, segmenting the second image to obtain segments, segmenting the first image based on the segments of the second image, and outputting the segmented first image to a display. In this way, an image which may otherwise prove challenging for an automatic segmentation process may be accurately segmented without sacrificing textural details of the image.

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 to be imaged. The at least one processor is operably coupled to the CT acquisition unit, and is configured to reconstruct an image using the CT imaging information; extract spatial frequency information from at least a portion of the image, wherein the spatial frequency is defined along a longitudinal direction; and remove a periodically recurring artifact from the at least a portion of the image based on a spatial frequency corresponding to a longitudinal collection periodicity to provide a corrected image.

Abstract: A method and apparatus for metal artifact elimination in a medical image. The method includes: obtaining a medical image to be processed; determining whether or not a metal region is contained in the medial image; and performing artifact elimination processing to the medical image when metal regions are contained in the medical image and a metal density value of one of the metal regions is greater than or equal to a preset density value.

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 to be imaged. The at least one processor is operably coupled to the CT acquisition unit, and is configured to reconstruct an image using the CT imaging information; extract spatial frequency information from at least a portion of the image, wherein the spatial frequency is defined along a longitudinal direction; and remove a periodically recurring artifact from the at least a portion of the image based on a spatial frequency corresponding to a longitudinal collection periodicity to provide a corrected image.