Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes receiving a first CT image data; receiving a second CT image data; and generating, by an artificial neural network (ANN), CT output image data configured to include fewer artifacts compared to the first and second CT image data. The ANN includes at least two parallel CT image data streams and a CT output image data stream. A first of the at least two parallel CT image data streams is based, at least in part, on the first CT image data, a second of the at least two parallel CT image data stream is based, at least in part, on the second CT image data. The CT output image data stream is based, at least in part, on respective outputs of the at least two parallel CT image data streams.

Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes receiving a first CT image data; receiving a second CT image data; and generating, by an artificial neural network (ANN), CT output image data configured to include fewer artifacts compared to the first and second CT image data. The ANN includes at least two parallel CT image data streams and a CT output image data stream. A first of the at least two parallel CT image data streams is based, at least in part, on the first CT image data, a second of the at least two parallel CT image data stream is based, at least in part, on the second CT image data. The CT output image data stream is based, at least in part, on respective outputs of the at least two parallel CT image data streams.

Abstract: Systems and methods for obtaining simultaneous X-ray—magnetic resonance imaging (MRI) images are provided. A magnetic resonance X-ray CT (MRX) system can combine X-ray imaging and MRI in a cost-effective and relatively simple solution for improved imaging. During imaging of a subject, the X-ray source and X-ray detector can be simultaneously rotated around the subject, and the means for generating a magnetic field can also be rotated around the subject. The means for generating a magnetic field can be a plurality of permanent magnets.

Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

Abstract: Systems and methods for obtaining simultaneous X-ray—magnetic resonance imaging (MRI) images are provided. A magnetic resonance X-ray CT (MRX) system can combine X-ray imaging and MRI in a cost-effective and relatively simple solution for improved imaging. During imaging of a subject, the X-ray source and X-ray detector can be simultaneously rotated around the subject, and the means for generating a magnetic field can also be rotated around the subject. The means for generating a magnetic field can be a plurality of permanent magnets.

Abstract: Systems and methods for obtaining simultaneous X-ray-magnetic resonance imaging (MRI) images are provided. A magnetic resonance X-ray CT (MRX) system can combine X-ray imaging and MRI in a cost-effective and relatively simple solution for improved imaging. During imaging of a subject, the X-ray source and X-ray detector can be simultaneously rotated around the subject, and the means for generating a magnetic field can also be rotated around the subject. The means for generating a magnetic field can be a plurality of permanent magnets.

Abstract: A simultaneous emission-transmission tomography in an MRI hardware framework is described. A method of multimodality imaging includes reconstructing, by a simultaneous emission transmission (SET) circuitry, a concentration image based, at least in part, on a plurality of selected ?-rays; and reconstructing, by the SET circuitry, an attenuation image based, at least in part, on the plurality of selected ?-rays. The plurality of selected ?-rays is emitted by a polarized radio tracer included in a test object. The selected ?-rays are selected based, at least in part, on a radio frequency (RF) pulse and based, at least in part, on a gradient magnetic field.

Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

Abstract: Systems and methods for obtaining simultaneous X-ray-magnetic resonance imaging (MRI) images are provided. A magnetic resonance X-ray CT (MRX) system can combine X-ray imaging and MRI in a cost-effective and relatively simple solution for improved imaging. During imaging of a subject, the X-ray source and X-ray detector can be simultaneously rotated around the subject, and the means for generating a magnetic field can also be rotated around the subject. The means for generating a magnetic field can be a plurality of permanent magnets.

Abstract: Imaging systems and methods are provided. Systems and methods of the subject invention can include the use of nanoparticles (for example, nanophosphors) within a sample to be imaged. Excitation with radiation, such X-ray radiation, can be performed on the nanoparticles to give rise to a change in one or more resonance parameters of the nanoparticles, and this change can be measured using magnetic resonance imaging to provide localization information.