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: A computer-implemented method of latency reduction is disclosed for a digital holography optical imaging system. The method comprises receiving an incoming light field at a focal plane array of the digital holography optical imaging system, applying an interfering light field to the incoming light field at the focal plane array, and generating a holographic image based on the incoming light field and the interfering light field at the focal plane array. The method further comprises generating a sequence of phase errors based on the holographic image, generating at least one training parameter based on the sequence of phase errors, and training a neural network to revise the at least one training parameter using the sequence of phase errors, a time delay, and the at least one training parameter. The method further comprises predicting a future phase error for a future holographic image based on the revised training parameter.

Abstract: A computer-implemented method of latency reduction is disclosed for a digital holography optical imaging system. The method comprises receiving an incoming light field at a focal plane array of the digital holography optical imaging system, applying an interfering light field to the incoming light field at the focal plane array, and generating a holographic image based on the incoming light field and the interfering light field at the focal plane array. The method further comprises generating a sequence of phase errors based on the holographic image, generating at least one training parameter based on the sequence of phase errors, and training a neural network to revise the at least one training parameter using the sequence of phase errors, a time delay, and the at least one training parameter. The method further comprises predicting a future phase error for a future holographic image based on the revised training parameter.

Abstract: A beam-scanning optical design is described for achieving up to kHz frame-rate optical imaging on multiple simultaneous data acquisition channels. In one embodiment, two fast-scan resonant mirrors direct the optical beam on a circuitous trajectory through the field of view, with the trajectory repeat-time given by the least common multiplier of the mirror periods. Dicing the raw time-domain data into sub-trajectories combined with model-based image reconstruction (MBIR) 3D in-painting algorithms allows for effective frame-rates much higher than the repeat time of the Lissajous trajectory. Because sub-trajectory and full-trajectory imaging are different methods of analyzing the same data, both high-frame rate images with relatively low resolution and low frame rate images with high resolution are simultaneously acquired.

Abstract: A beam-scanning optical design is described for achieving up to kHz frame-rate optical imaging on multiple simultaneous data acquisition channels. In one embodiment, two fast-scan resonant mirrors direct the optical beam on a circuitous trajectory through the field of view, with the trajectory repeat-time given by the least common multiplier of the mirror periods. Dicing the raw time-domain data into sub-trajectories combined with model-based image reconstruction (MBIR) 3D in-painting algorithms allows for effective frame-rates much higher than the repeat time of the Lissajous trajectory. Because sub-trajectory and full-trajectory imaging are different methods of analyzing the same data, both high-frame rate images with relatively low resolution and low frame rate images with high resolution are simultaneously acquired.

Abstract: Systems and methods for MBIR reconstruction utilizing a super-voxel approach are provided. A super-voxel algorithm is an optimization algorithm that, as with ICD, produces rapid and geometrically agnostic convergence to the MBIR reconstruction by processing super-voxels which comprise a plurality of voxels whose corresponding memory entries substantially overlap. The voxels in the super-voxel may also be localized or adjacent to one another in the image. In addition, the super-voxel algorithm straightens the memory in the “sinogram” that contains the measured CT data so that both data and intermediate results of the computation can be efficiently accessed from high-speed memory and cache on a computer, GPU, or other high-performance computing hardware. Therefore, each iteration of the super-voxel algorithm runs much faster by more efficiently using the computing hardware.

Abstract: A beam-scanning optical design is described for achieving up to kHz frame-rate optical imaging on multiple simultaneous data acquisition channels. In one embodiment, two fast-scan resonant mirrors direct the optical beam on a circuitous trajectory through the field of view, with the trajectory repeat-time given by the least common multiplier of the mirror periods. Dicing the raw time-domain data into sub-trajectories combined with model-based image reconstruction (MBIR) 3D in-painting algorithms allows for effective frame-rates much higher than the repeat time of the Lissajous trajectory. Because sub-trajectory and full-trajectory imaging are different methods of analyzing the same data, both high-frame rate images with relatively low resolution and low frame rate images with high resolution are simultaneously acquired.

Abstract: A system and method include acquisition of projection data from a scanned object, the set of projection data comprising a plurality of projection measurements. The system and method also include calculation of a set of modified statistical weights from the projection data, wherein a respective modified statistical weight of the set of modified statistical weights comprises a deviation from an inverse variance of a corresponding projection measurement of the projection data. The system and method further include reconstruction of an image of the scanned object using the set of modified statistical weights as coefficients in an iterative reconstruction algorithm.

Abstract: A beam-scanning optical design is described for achieving up to kHz frame-rate optical imaging on multiple simultaneous data acquisition channels. In one embodiment, two fast-scan resonant mirrors direct the optical beam on a circuitous trajectory through the field of view, with the trajectory repeat-time given by the least common multiplier of the mirror periods. Dicing the raw time-domain data into sub-trajectories combined with model-based image reconstruction (MBIR) 3D in-painting algorithms allows for effective frame-rates much higher than the repeat time of the Lissajous trajectory. Because sub-trajectory and full-trajectory imaging are different methods of analyzing the same data, both high-frame rate images with relatively low resolution and low frame rate images with high resolution are simultaneously acquired.

Abstract: A friction stir method comprises causing a rotating probe (1) of a friction stir tool to enter a workpiece or a joint between a pair of workpieces (89), the or each workpiece being a low conductivity, high melting point metal or metal alloy. The probe (1) extends from a shoulder (4), or between shoulders, in contact with the workpiece(s) and rotates relative to the or each shoulder.

Abstract: A system and method include acquisition of projection data from a scanned object, the set of projection data comprising a plurality of projection measurements. The system and method also include calculation of a set of modified statistical weights from the projection data, wherein a respective modified statistical weight of the set of modified statistical weights comprises a deviation from an inverse variance of a corresponding projection measurement of the projection data. The system and method further include reconstruction of an image of the scanned object using the set of modified statistical weights as coefficients in an iterative reconstruction algorithm.

Abstract: A system and method include acquisition of a set of image data corresponding to a time period of data acquisition, the set of image data corresponding to a plurality of voxels, wherein each of the plurality of voxels corresponds to a distinct acquisition time within the time period of data acquisition. The system and method further include the modeling of the plurality of voxels as a function of time based on a plurality of kinetic parameters associated therewith and reconstruction of an image from the set of image data based on the modeled plurality of voxels.

Abstract: An improved iterative reconstruction method to reconstruct a first image includes generating an imaging beam, receiving said imaging beam on a detector array, generating projection data based on said imaging beams received by said detector array, providing said projection data to an image reconstructor, enlarging one of a plurality of voxels and a plurality of detectors of the provided projection data, reconstructing portions of the first image with the plurality of enlarged voxels or detectors, and iteratively reconstructing the portions of the first image to create a reconstructed image.

Abstract: An improved iterative reconstruction method to reconstruct a first image includes generating an imaging beam, receiving said imaging beam on a detector array, generating projection data based on said imaging beams received by said detector array, providing said projection data to an image reconstructor, enlarging one of a plurality of voxels and a plurality of detectors of the provided projection data, reconstructing portions of the first image with the plurality of enlarged voxels or detectors, and iteratively reconstructing the portions of the first image to create a reconstructed image.

Abstract: A non-transitory computer readable storage medium having stored thereon a computer program having instructions, which, when executed by a computer, cause the computer to acquire a set of projection data corresponding to a plurality of image voxels and to calculate coefficients of a regularizing function configured to penalize differences between pairs of the plurality of voxels that are not immediate neighbors. The instructions also cause the computer to iteratively reconstruct an image from the set of projection data based on the regularizing function.

Abstract: A non-transitory computer readable storage medium having stored thereon a computer program having instructions, which, when executed by a computer, cause the computer to acquire a set of projection data corresponding to a plurality of image voxels and to calculate coefficients of a regularizing function configured to penalize differences between pairs of the plurality of voxels that are not immediate neighbors. The instructions also cause the computer to iteratively reconstruct an image from the set of projection data based on the regularizing function.

Abstract: A method of improving a resolution of an image using image reconstruction is provided. The method includes acquiring scan data of an object and forward projecting a current image estimate of the scan data to generate calculated projection data. The method also includes applying a data-fit term and a regularization term to the scan data and the calculated projection data and modifying at least one of the data fit term and the regularization term to accommodate spatio-temporal information to form a reconstructed image from the scan data and the calculated projection data.

Abstract: Methods and systems for reconstructing an image are provided. The method includes performing a tomographic image reconstruction using a joint estimation of at least one of a gain parameter and an offset parameter, and an estimation of the reconstructed image.

Abstract: A method for reconstructing an image of an object, the image comprising a plurality of image elements, is disclosed. The method includes accessing image data associated with the plurality of image elements, applying a first algorithm to the plurality of image elements, selecting a spatially non-homogenous set of the plurality of image elements, and applying an iterative algorithm to the set of image elements to reduce an amount of time necessary for reconstructing the image, or to improve an image quality at a fixed computation time, or both.