Abstract: Methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level, identifying a contrast phase of the image, entering the image as input to a segmentation model trained to output an anatomy mask that identifies each tissue type in the image, generating a guide image from the image and the anatomy mask using a regression model, entering the image and the guide image as input into an energy transformation model trained to output a transformed image at a different, second energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.

Abstract: Various methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level acquired with a single-energy computed tomography (CT) imaging system, identifying a contrast phase of the image, entering the image as input into an energy transformation model trained to output a transformed image at a second energy level, different than the first energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.

Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

Abstract: Systems/techniques that facilitate deep learning robustness against display field of view (DFOV) variations are provided. In various embodiments, a system can access a deep learning neural network and a medical image. In various aspects, a first DFOV, and thus a first spatial resolution, on which the deep learning neural network is trained can fail to match a second DFOV, and thus a second spatial resolution, exhibited by the medical image. In various instances, the system can execute the deep learning neural network on a resampled version of the medical image, where the resampled version of the medical image can exhibit the first DFOV and thus the first spatial resolution. In various cases, the system can generate the resampled version of the medical image by up-sampling or down-sampling the medical image until it exhibits the first DFOV and thus the first spatial resolution.

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

Abstract: The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

Abstract: A system for detecting an array of samples having detectable samples and at least one reference sample is provided. The system comprises an electromagnetic radiation source, a sensing surface comprising a plurality of sample fields, wherein the plurality of sample fields comprise at least one reference field, a phase difference generator configured to introduce differences in pathlengths of one or more samples in the array of samples, and an imaging spectrometer configured to image one or more samples in the array of samples.

Abstract: A system and method for soft-field reconstruction are provided. One method includes establishing an initial estimate of a property distribution of an object, using a first reconstruction process to reconstruct an estimate of the actual property distribution and using a second reconstruction process different than the first reconstruction process to further reconstruct the estimate of the actual property distribution. A solution from the first reconstruction process is used as an initial estimate in the second reconstruction process.

Abstract: An iteration method for computing a distribution of one or more properties within an object comprises defining a first mesh of the object, applying an excitation to the object, computing a response of the object to the applied excitation, obtaining a reference response of the object corresponding to the applied excitation, computing a distribution of one or more properties of the object, and updating at least a subset of the nodes of the first mesh to form an updated mesh of the object. The distribution of one or more properties of the object is computed using the computed response, the reference response, and the first mesh. The first mesh includes a plurality of nodes and elements. A connectivity relationship of the subset of the nodes in the updated mesh remains the same as in the first mesh.

Abstract: A system for detecting an array of samples having detectable samples and at least one reference sample is provided. The system comprises an electromagnetic radiation source, a sensing surface comprising a plurality of sample fields, wherein the plurality of sample fields comprise at least one reference field, a phase difference generator configured to introduce differences in pathlengths of one or more samples in the array of samples, and an imaging spectrometer configured to image one or more samples in the array of samples.

Abstract: A detection system for a two-dimensional (2D) array is provided. The detection system comprises an electromagnetic radiation source, a phase difference generator, a detection surface having a plurality of sample fields that can receive samples, and an imaging spectrometer configured to discriminate between two or more spatially separated points.

Abstract: An iteration method for computing a distribution of one or more properties within an object comprises defining a first mesh of the object, applying an excitation to the object, computing a response of the object to the applied excitation, obtaining a reference response of the object corresponding to the applied excitation, computing a distribution of one or more properties of the object, and updating at least a subset of the nodes of the first mesh to form an updated mesh of the object. The distribution of one or more properties of the object is computed using the computed response, the reference response, and the first mesh. The first mesh includes a plurality of nodes and elements. A connectivity relationship of the subset of the nodes in the updated mesh remains the same as in the first mesh.

Abstract: A system and method for soft-field reconstruction are provided. One method includes establishing an initial estimate of a property distribution of an object, using a first reconstruction process to reconstruct an estimate of the actual property distribution and using a second reconstruction process different than the first reconstruction process to further reconstruct the estimate of the actual property distribution. A solution from the first reconstruction process is used as an initial estimate in the second reconstruction process.

Abstract: A detection system for a two-dimensional (2D) array is provided. The detection system comprises an electromagnetic radiation source, a phase difference generator, a detection surface having a plurality of sample fields that can receive samples, and an imaging spectrometer configured to discriminate between two or more spatially separated points.