Abstract: A framework for characterization of a collimator. In accordance with one aspect, first and second sides of the collimator are photographed to generate first and second image data. An optical characterization map (OCM) may be generated based on the first and second image data, wherein the optical characterization map characterizes the individual channels of the collimator. Quality assessment or image reconstruction may then be performed based on the OCM.

Abstract: A system and method includes determination of a region of interest of an imaging subject, generation of a first linear attenuation coefficient map of the imaging subject, the first linear attenuation coefficient map generated to associate voxels of the region of interest of the imaging subject with greater linear attenuation coefficients than voxels of other regions of the imaging subject, attenuation-correction of a plurality of tomographic frames of the imaging subject based on the first linear attenuation coefficient map to generate a second plurality of tomographic frames, and determination of tomographic inconsistency of the second plurality of tomographic frames.

Abstract: A framework for quality-driven image processing. In accordance with one aspect, image data and anatomical data of a region of interest are received. Zonal information is generated based on the anatomical data. Image processing is performed based on the image data to generate an intermediate image. One or more image quality metrics may then be determined for the intermediate image data using the zonal information. A processing action may be performed based on the one or more image quality metrics to generate a final image.

Abstract: A system and method includes determination of a region of interest of an imaging subject, generation of a first linear attenuation coefficient map of the imaging subject, the first linear attenuation coefficient map generated to associate voxels of the region of interest of the imaging subject with greater linear attenuation coefficients than voxels of other regions of the imaging subject, attenuation-correction of a plurality of tomographic frames of the imaging subject based on the first linear attenuation coefficient map to generate a second plurality of tomographic frames, and determination of tomographic inconsistency of the second plurality of tomographic frames.

Abstract: For controlling reconstruction in emission tomography, the quality of data for detected emissions and/or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

Abstract: Systems and methods for imaging. An external device may be used to acquire optical image data of a subject. One or more physical parameters of the subject may be determined based on the optical image data. The one or more physical parameters may be translated to one or more properties of the subject. The one or more properties may then be used to generate medical image data of the subject.

Abstract: For controlling reconstruction in emission tomography, the quality of data for detected emissions and/or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

Abstract: Systems and methods for image reconstruction based on modeling image formation as one or more neural networks. In accordance with one aspect, one or more neural networks are configured based on physics of image formation (202). The one or more neural networks are optimized using acquired test image data (204). An output image may then be reconstructed by applying current image data as input to the one or more optimized neural networks (208).

Abstract: A system and method includes input of a plurality of sets of training data to a neural network to generate a plurality of sets of output data, determination of a first loss based on the plurality of sets of output data and on the plurality of sets of ground truth data, determination if a second loss based on the plurality of sets of output data and one or more physics-based constraints, and modification of the neural network based on the first loss and the second loss.

Abstract: Systems and methods for imaging. An external device may be used to acquire optical image data of a subject. One or more physical parameters of the subject may be determined based on the optical image data. The one or more physical parameters may be translated to one or more properties of the subject. The one or more properties may then be used to generate medical image data of the subject.

Abstract: For controlling reconstruction in emission tomography, the quality of data for detected emissions and/or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

Abstract: For SPECT-based internal dose estimation, a portable detector is used to sample activity. The portable detector may selectively use far-field or near-field imaging for a SPECT scan. A camera and/or gyroscope assist in determining emission location and/or aligning activities from the different times. The time-activity curve or another dose is estimated using activities from different times where the activity for at least one time is from the portable detector, which may allow for more frequent sampling of activity and more accurate dose estimation.

Abstract: A system and method include acquisition of a plurality of sets of images which meet acceptance criteria of an imaging task, each set of images acquired using a respective instance of a type of imaging component, acquisition of a test image using a test instance of the type of imaging component, presentation of a plurality of groups of images, each of the groups of images including the test image and a respective one or more images of the plurality of sets of images, reception, for each group of images, of an indication from an observer of a ranking of the test image of the group with respect to the respective one or more images of the group, and determination of a quality of the test instance of the type of imaging component based on the indications.

Abstract: A system and method include acquisition of a plurality of sets of images which meet acceptance criteria of an imaging task, each set of images acquired using a respective instance of a type of imaging component, acquisition of a test image using a test instance of the type of imaging component, presentation of a plurality of groups of images, each of the groups of images including the test image and a respective one or more images of the plurality of sets of images, reception, for each group of images, of an indication from an observer of a ranking of the test image of the group with respect to the respective one or more images of the group, and determination of a quality of the test instance of the type of imaging component based on the indications.

Abstract: A system and method includes input of a plurality of sets of training data to a neural network to generate a plurality of sets of output data, determination of a first loss based on the plurality of sets of output data and on the plurality of sets of ground truth data, determination if a second loss based on the plurality of sets of output data and one or more physics-based constraints, and modification of the neural network based on the first loss and the second loss.

Abstract: A framework for quality-driven image processing. In accordance with one aspect, image data and anatomical data of a region of interest are received. Zonal information is generated based on the anatomical data. Image processing is performed based on the image data to generate an intermediate image. One or more image quality metrics may then be determined for the intermediate image data using the zonal information. A processing action may be performed based on the one or more image quality metrics to generate a final image.

Abstract: A framework for characterization of a collimator. In accordance with one aspect, first and second sides of the collimator are photographed to generate first and second image data. An optical characterization map (OCM) may be generated based on the first and second image data, wherein the optical characterization map characterizes the individual channels of the collimator. Quality assessment or image reconstruction may then be performed based on the OCM.