Abstract: A computer system is configured to receiving a data set from a data provider and automatically save the data set in a quarantine database where copying, moving, and sharing of the data set are restricted until the data set is released by a data provider. The data set is parsed to find and mark portions with potentially sensitive information. At least those parts are reviewed by a data governor, who can confirm, add, edit, or remove markers. Those parts can be visually indicated to the data governor, along with a preview of, metadata about, and analysis of the data set. After reviewing at least the automatically marked portions, the data governor can release the data set to a non-quarantine database where another user can use the data set. The user is restricted from accessing the quarantine database.

Abstract: A spectral computed tomography imaging system (102) includes a radiation source (112) configured to emit x-ray radiation and a detector array (114) configured to detect x-ray radiation and generate spectral data. The spectral imaging system further includes a memory (134) configured to store a virtual non-contrast image enhancing module (136) that includes computer executable instructions including a neural network trained to produce image quality enhanced virtual non-contrast images. The neural network is trained with training spectral data and training non-contrast-enhanced images generated from a non-contrast-enhanced scan. The spectral imaging system further includes a processor (132) configured to process the spectral data with the trained neural network to produce the image quality enhanced virtual non-contrast images.

Abstract: A non-spectral computed tomography scanner includes a radiation source configured to emit x-ray radiation, a detector array configured to detect x-ray radiation and generate non-spectral data, and a memory configured to store a spectral image module that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: Methods for treating phosphogypsum-containing water are disclosed. The water may be treated so as to promote precipitation of one or more target constituents and to facilitate downstream membrane treatment. A coagulant may be added to promote phosphate recovery. Ammonia may optionally be removed. Related systems are also disclosed.

Abstract: A water treatment system comprises a source of water including one or more contaminants, an electrocoagulation cell including a housing defining a fluid flow conduit, an anode disposed within the fluid flow conduit, and a cathode disposed within the fluid flow conduit, the housing including an inlet fluidly connectable to the source of water and an outlet, a solids/liquid separation system having an inlet fluidly connectable to the outlet of the housing of the electrocoagulation cell, a solids-rich outlet, and a solids-lean outlet, and a ballast feed system configured to deliver a ballast to the solids/liquid separation system.

Abstract: Systems and methods are provided for processing an input dataset or running an inference. The systems and methods may be configured to accept an input dataset, access one or more predefined logic plugins for processing the input dataset, process the input dataset based at least in part on a first predefined logic plugin, and generate the one or more outputs based at least in part of the processing of the input dataset. The one or more outputs may have a different format than a format of the input dataset.

Abstract: A non-spectral computed tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (114) configured to detect x-ray radiation and generate non-spectral data, and a memory (134) configured to store a spectral image module (130) that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor (126) configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: Methods for treating phosphogypsum-containing water are disclosed. The water may be treated so as to promote precipitation of one or more target constituents and to facilitate downstream membrane treatment. A coagulant may be added to promote phosphate recovery. Related systems are also disclosed.

Abstract: Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein.

Abstract: Methods for processing pretreated phosphogypsum wastewater are disclosed. The pretreated wastewater may be subjected to electrodialysis involving at least one monovalent cation selective membrane. Further downstream membrane treatment may be applied. Upstream precipitation and air-stripping techniques may optionally also be employed. Related systems are also disclosed.

Abstract: A computer system is configured to receiving a data set from a data provider and automatically save the data set in a quarantine database where copying, moving, and sharing of the data set are restricted until the data set is released by a data provider. The data set is parsed to find and mark portions with potentially sensitive information. At least those parts are reviewed by a data governor, who can confirm, add, edit, or remove markers. Those parts can be visually indicated to the data governor, along with a preview of, metadata about, and analysis of the data set. After reviewing at least the automatically marked portions, the data governor can release the data set to a non-quarantine database where another user can use the data set. The user is restricted from accessing the quarantine database.

Abstract: Methods for processing pretreated phosphogypsum wastewater are disclosed. Precipitation of select constituents may be promoted to control a hardness level of the pretreated wastewater. Ammonia may then be removed from the process stream via reverse osmosis. A membrane contactor and/or polishing unit(s) may optionally be used. Related systems are also disclosed.

Abstract: A computer system is configured to receiving a data set from a data provider and automatically save the data set in a quarantine database where copying, moving, and sharing of the data set are restricted until the data set is released by a data provider. The data set is parsed to find and mark portions with potentially sensitive information. At least those parts are reviewed by a data governor, who can confirm, add, edit, or remove markers. Those parts can be visually indicated to the data governor, along with a preview of, metadata about, and analysis of the data set. After reviewing at least the automatically marked portions, the data governor can release the data set to a non-quarantine database where another user can use the data set. The user is restricted from accessing the quarantine database.

Abstract: A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

Abstract: A non-spectral computed tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (114) configured to detect x-ray radiation and generate non-spectral data, and a memory (134) configured to store a spectral image module (130) that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor (126) configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: An X-ray imaging device (10, 100) is configured to acquire an uncorrected X-ray image (30). An image reconstruction device comprises an electronic processor (22) and a non-transitory storage medium (24) storing instructions readable and executable by the electronic processor to perform an image correction method (26) including: applying a neural network (32) to the uncorrected X-ray image to generate a metal artifact image (34) wherein the neural network is trained to extract residual image content comprising a metal artifact; and generating a corrected X-ray image (40) by subtracting the metal artifact image from the uncorrected X-ray image.

Abstract: A spectral computed tomography imaging system (102) includes a radiation source (112) configured to emit x-ray radiation and a detector array (114) configured to detect x-ray radiation and generate spectral data. The spectral imaging system further includes a memory (134) configured to store a virtual non-contrast image enhancing module (136) that includes computer executable instructions including a neural network trained to produce image quality enhanced virtual non-contrast images. The neural network is trained with training spectral data and training non-contrast-enhanced images generated from a non-contrast-enhanced scan. The spectral imaging system further includes a processor (132) configured to process the spectral data with the trained neural network to produce the image quality enhanced virtual non-contrast images.

Abstract: An imaging system includes an X-ray tube (202) having a focal spot (204) and a port window (206), and a filter (208) having at least a first region (310) with a first material having first X-ray attenuation characteristics for a redetermined X-ray photon energy range of interest and a second region (312) with a different X-ray attenuation characteristic. The filter is disposed between the port window and an examination region (108) and is configured to rotate such that the at least the first and the second regions sweep through and filter X-ray radiation emitted from the focal spot. The system further includes an X-ray radiation flux detector (2802, 2902) configured to detect an X-ray radiation flux of the filtered X-ray radiation, a detector array (112) configured to detect the filtered X-ray radiation traversing the examination region and produce a signal indicative thereof, and a reconstructor (114) configured to process the signal based on the detected flux to reconstruct volumetric image data.

Abstract: Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein.

Abstract: Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein.