Abstract: Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.

Abstract: Techniques are described that facilitate generating neural network (NNs) tailored to optimize specific properties of medical images using novel loss functions. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a training component that trains a NN to generate a modified version of computed tomography (CT) data comprising one or more optimized properties relative to the CT data using a loss function tailored to control learning adaptation of the NN based on error attributed to one or more defined components associated with the CT data, resulting in a trained NN, wherein the one or more defined components comprise at least one of a frequency component or a spatial feature component.

Abstract: Systems and methods for computed tomography imaging are provided. In one embodiment, a method includes acquiring an image, inputting the image to a machine learning model to generate a denoised image, the machine learning model trained with a loss function that weights variance differently from bias, and outputting the denoised image. In this way, structural details in denoised CT images may be improved while maintaining textural information in the denoised images.

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.

Abstract: An iterative reconstruction approach is provided that allows the use of differing weights in pixels or larger sub-regions in the reconstructed image. By way of example, the relative significance of each projection measurement may be determined based on both the measurement position and the location of the reconstructed pixel. Computationally, the significance of each projection based on these two factors is represented by a weight factor employed in the algorithmic computation.

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.

Abstract: Methods and systems for model-based image processing are provided. One method includes selecting at least one reference image from a plurality of reference images, partitioning the at least one reference image into a plurality of patches, generating a probability distribution for each of the patches, and generating a model of a probability distribution for the at least one reference image using the probability distributions for each of the patches.

Abstract: Methods and systems for model-based image processing are provided. One method includes selecting at least one reference image from a plurality of reference images, partitioning the at least one reference image into a plurality of patches, generating a probability distribution for each of the patches, and generating a model of a probability distribution for the at least one reference image using the probability distributions for each of the patches.

Abstract: A method is provided for reconstructing an image of an object that includes image elements. The method includes accessing measurement data associated with the image elements, introducing an auxiliary variable to transform an original problem of reconstructing the image to a constrained optimization problem, and solving the constrained optimization problem using a method of multipliers to create a sequence of sub-problems and solve the sequence of sub-problems. Solving the sequence of sub-problems includes reconstructing the image by optimizing a first objective function. The first objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. An inner loop iteratively optimizes a second objective function approximating the first objective function. An outer loop utilizes the solution of the second objective function to optimize the first objective function.

Abstract: A method is provided for reconstructing an image of an object that includes image elements. The method includes accessing measurement data associated with the image elements, introducing an auxiliary variable to transform an original problem of reconstructing the image to a constrained optimization problem, and solving the constrained optimization problem using a method of multipliers to create a sequence of sub-problems and solve the sequence of sub-problems. Solving the sequence of sub-problems includes reconstructing the image by optimizing a first objective function. The first objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. An inner loop iteratively optimizes a second objective function approximating the first objective function. An outer loop utilizes the solution of the second objective function to optimize the first objective function.

Abstract: A method for improving quality of an image is described. The method includes reconstructing a first image of a sample volume, segmenting the first image to generate a first region and a second region, reconstructing a second image of the sample volume, and generating a final image from a combination of the segmentation, the first image, and the second image.

Abstract: A method for filtering a measurement of density of an object is described. The method includes filtering the measurement of the density and preserving a local average of the measurement and additional measurements when performing the filtering.

Abstract: A method of reconstructing an image includes combining a two-dimensional forward projection function and a three-dimensional stabilizing function to generate an iterative reconstruction algorithm, and using the obtained iterative reconstruction algorithm to perform a multislice Computed Tomography (CT) reconstruction to generate an image.

Abstract: A method of reconstructing an image includes performing an iterative CT X-ray image reconstruction using a model including a point spread function (PSF) to reconstruct an image.

Abstract: A method for calculating an element of a projection includes calculating an element Ai,j of a projection matrix using at least two different rays through a voxel.

Abstract: A method for improving images of an imaging system including a plurality of detector cells includes assigning a quality factor for each detector cell.

Abstract: A method of reconstructing an image includes substantially minimizing a cost function of the form x ^ = arg ? ? ? min x ? { ? m = 0 M ? ? ? D m ? ( y m , F m ? ( x ) ) + S ? ( x ) } , where {circumflex over (x)} is the value of x which achieves the minimum summation, ym is an integral projection, Fm(x) is an forward projection function, and S(x) is a stabilizing function, to obtain {circumflex over (x)}, and using the obtained {circumflex over (x)} to perform a multislice Computed Tomography (CT) reconstruction to generate an image.

Abstract: A multi-slice computed tomography imaging system is provided including a source that generates an x-ray beam and a detector array that receives the x-ray beam and generates projection data. A translatable table has an object thereon and is operable to translate in relation to the source and the detector array. The source and the detector array rotate about the translatable table to helically scan the object. An image reconstructor is electrically coupled to the detector array and reconstructs an image in response to the projection data using a computed tomography modeled iterative reconstruction technique. The iterative reconstruction technique includes determining a cross-section reconstruction vector, which approximately matches the projection data via a computed tomography model. Methods for performing the same are also provided including accounting for extended boundary regions.

Abstract: An imaging system is provided including a source generating an x-ray beam and a detector array receiving the x-ray beam and generating projection data corresponding to at least a scanned portion of an object. An image reconstructor is electrically coupled to said detector array and reconstructs an image of the scanned portion of the object for a full field-of-view in response to said projection data using a dual iterative reconstruction technique. The dual iterative reconstruction technique includes reconstructing the full field-of-view using a first resolution and reconstructing a region-of-interest within the full FOV using a second resolution. A method is also provided for reconstructing an image using projection data from such an imaging system.