Abstract: Various systems and methods are provided for processing computed tomography (CT) data in a CT imaging system. The CT imaging system comprising an X-ray source configured to emit X-rays, an X-ray detector configured to generate sampled digital detector data and a digital processor configured to process the sampled digital detector data. The method comprises filtering, in the digital processor, the sampled digital detector data to generate filtered detector data and resampling, in the digital processor, the filtered detector data to generate resampled detector data. The resampling comprises a reduction in data size of the filtered detector data. The filtering is performed on at least part of the sampled digital detector data according to a filtering setting and the resampling is performed on at least part of the filtered digital detector data according to a resampling setting, wherein the filtering setting and resampling setting are decoupled.

Abstract: Systems are provided for a computed tomography system, comprising a gantry configured to rotate around an axis of rotation and a detector array comprised of a plurality of photon-counting computed tomography (PCCT) detector units configured to be rotated around the axis of rotation by the gantry, wherein a stacking axis of least one of the plurality of PCCT detector arrays is positioned at an angle with respect to the axis of rotation.

Abstract: A system for generating target images, comprising a processor configured to train a latent space encoder to generate latent space information by encoding the multi-spectral images to generate anatomy latent space information and contrast latent space information, the anatomy latent space information representing compressed anatomy information in the multi-spectral images, the contrast latent space information representing compressed contrast information in the multi-spectral images, combining selected features of the anatomy latent space information and the contrast latent space information to reproduce the multi-spectral images, comparing the reproduced multi-spectral images to the multi-spectral images, and adjusting the encoding of the multi-spectral images and repeating the training until.

Abstract: Various systems and methods are provided for processing computed tomography (CT) data in a CT imaging system. The CT imaging system comprising an X-ray source configured to emit X-rays, an X-ray detector configured to generate sampled digital detector data and a digital processor configured to process the sampled digital detector data. The method comprises filtering, in the digital processor, the sampled digital detector data to generate filtered detector data and resampling, in the digital processor, the filtered detector data to generate resampled detector data. The resampling comprises a reduction in data size of the filtered detector data. The filtering is performed on at least part of the sampled digital detector data according to a filtering setting and the resampling is performed on at least part of the filtered digital detector data according to a resampling setting, wherein the filtering setting and resampling setting are decoupled.

Abstract: Various systems and methods are provided for MAR in CT images. A corrupted CT image, of a region of interest (ROI) of a subject, including artifacts caused by a metal object in the subject may be acquired. A corrupted sinogram including a corrupted region of corrupted data caused by the metal object and an uncorrupted region of uncorrupted data may be generated. A mask sinogram that delineates the corrupted region of the corrupted data may be generated. A corrected sinogram including the uncorrupted region of the uncorrupted data and an inpainted region of inpainted data corresponding to the corrupted region may be generated using a denoising diffusion probabilistic model, the corrupted sinogram, and the mask sinogram. A corrected CT image, of the ROI of the subject, that includes reduced artifacts relative to the artifacts in the corrupted CT image may be generated based on the corrected sinogram.

Abstract: Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.

Abstract: Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.

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 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 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 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 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 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 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: A method for reconstructing an image of an object includes acquiring a set of measured projection data, reconstructing the measured projection data using a first algorithm to generate a first reconstructed image dataset, reconstructing the measured projection data using a second algorithm to generate a second reconstructed image dataset, the second algorithm being utilized to improve the temporal resolution of the second reconstructed image dataset, and generating a final image dataset using both the first and second image datasets.

Abstract: Exemplary embodiments are directed to a system and method for estimating and compensating for anode target filtration in an X-ray tube. Certain embodiments record changes in the photon flux over the life of the tube. By comparing the flux values, filtration resulting from a roughened target surface and target deposition on the tube window may be inferred. A plurality of systems and methods for comparing flux values are provided and the relative merits and complementary effects of each discussed.

Abstract: A method for reconstructing an image of an object includes acquiring a set of measured projection data, reconstructing the measured projection data using a first algorithm to generate a first reconstructed image dataset, reconstructing the measured projection data using a second algorithm to generate a second reconstructed image dataset, the second algorithm being utilized to improve the temporal resolution of the second reconstructed image dataset, and generating a final image dataset using both the first and second image datasets.

Abstract: Systems and methods are provided for acquiring and reconstructing projection data that is mathematically complete or sufficient using a computed tomography (CT) system having stationary distributed X-ray sources and detector arrays. In one embodiment, a distributed source is provided as arcuate segments offset in the X-Y plane and along the Z-axis.

Abstract: A technique is provided for the temporal interpolation of a projection data set acquired of a dynamic object, such as a heart. The projection data set is acquired using a slowly rotating gantry and a distributed X-ray source. The projection data may be interpolated at each view position to a selected instant of time, such as relative to a cardiac phase. The resulting interpolated projection data characterize the projection data at each view location at any instant in time. The set of interpolated projection data may then be reconstructed to generate images and/or volume with improved temporal resolution.

Abstract: A technique is provided for improving z-axis coverage and/or reducing cone beam artifacts during CT imaging. Multiple X-ray emission points are provided along the z-axis. Some or all of the emission points may be concurrently active. X-rays from concurrently active emission points are collimated so that X-rays from two or more emission points do not overlap on the detector. In addition, different groups of concurrently activated emission points may be sequentially or alternately activated, in conjunction with collimation, to prevent the overlap of X-rays from different emission points on the detector. In this manner, The X-rays may be timed and collimated such that the respective streams of radiation become adjacent at different locations, such as at the detector, the isocenter, or edge of the field of view.