Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.

Abstract: A system and method include training of an artificial neural network to generate an output three-dimensional image volume based on input two-dimensional projection images, the training based on a plurality of subsets of two-dimensional projection images of each of a plurality of sets of two-dimensional projection images and associated ones of three-dimensional image volumes reconstructed from each of the plurality of sets of two-dimensional projection images.

Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.

Abstract: For calibration of internal dose in nuclear imaging, the dose model used for estimating internal dose in a patient is calibrated. One or more values of the dose model (e.g., a physics simulation, dose kernels, or a transport model) are set based on measured dose. The dose may be measured relative to specific tissues and/or isotopes, providing for tracer and tissue specific calibration. For example, dose from the tracer to be injected into the patient is estimated from emissions as well as measured by a dosimeter in a tissue mimicking tissue mimicking object. These doses are used to calibrate the dose model, which calibrated dose model is then used to determine internal dose for the patient.

Abstract: A system and method include training of an artificial neural network to generate an output data set, the training based on the plurality of sets of emission data acquired using a first imaging modality and respective ones of data sets acquired using a second imaging modality.

Abstract: Parameterized model reconstruction is used for internal dose tomography. The parameterized model, solved for within the reconstruction, models the dose level and may account for diffusion, isotope half-life, and/or biological half-life. Using the detected emissions from different scans (e.g., from different scan sessions in a given cycle) as input for the one reconstruction, the parameterized model reconstruction determines the biodistribution of dose at any time.

Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.

Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.

Abstract: For denoising in SPECT, such as qSPECT, machine learning is used to relate settings to noise structure. Given the SPECT imaging arrangement for a patient, the machine-learned model estimates the structure of the noise. This noise structure may be used to denoise the reconstructed representation.

Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.

Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.

Abstract: An emission image is generated from poor quality emission data. A machine-learned model may be used to recover information. Emission imaging may be provided due to the recovery in a way that at least some diagnostically useful information is made available despite corruption that would otherwise result in less diagnostically useful information or no image at all.

Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.

Abstract: A system and method include acquisition of a set of tomographic images of a patient volume associated with each of a plurality of timepoints of a first radiopharmaceutical therapy cycle, determination, for each of the plurality of timepoints, of a systematic uncertainty for each of a plurality of regions within the patient volume based on the set of tomographic images associated with the timepoint, determination, for each of the plurality of timepoints, of a quantitative statistical uncertainty based on the set of tomographic images associated with the timepoint, determination of a dose and a dose uncertainty for each of the plurality of regions based on the set of tomographic images, the systematic uncertainty and the quantitative statistical uncertainty for each of the plurality of timepoints, and display of a cumulative dose and cumulative dose uncertainty for each of the plurality of regions based on the dose and the dose uncertainty determined for each of the plurality of regions.

Abstract: For calibration of internal dose in nuclear imaging, the dose model used for estimating internal dose in a patient is calibrated. One or more values of the dose model (e.g., a physics simulation, dose kernels, or a transport model) are set based on measured dose. The dose may be measured relative to specific tissues and/or isotopes, providing for tracer and tissue specific calibration. For example, dose from the tracer to be injected into the patient is estimated from emissions as well as measured by a dosimeter in a tissue mimicking tissue mimicking object. These doses are used to calibrate the dose model, which calibrated dose model is then used to determine internal dose for the patient.

Abstract: Parameterized model reconstruction is used for internal dose tomography. The parameterized model, solved for within the reconstruction, models the dose level and may account for diffusion, isotope half-life, and/or biological half-life. Using the detected emissions from different scans (e.g., from different scan sessions in a given cycle) as input for the one reconstruction, the parameterized model reconstruction determines the biodistribution of dose at any time.

Abstract: A system and method include training of an artificial neural network to generate an output data set, the training based on the plurality of sets of emission data acquired using a first imaging modality and respective ones of data sets acquired using a second imaging modality.

Abstract: For denoising in SPECT, such as qSPECT, machine learning is used to relate settings to noise structure. Given the SPECT imaging arrangement for a patient, the machine-learned model estimates the structure of the noise. This noise structure may be used to denoise the reconstructed representation.

Abstract: An emission image is generated from poor quality emission data. A machine-learned model may be used to recover information. Emission imaging may be provided due to the recovery in a way that at least some diagnostically useful information is made available despite corruption that would otherwise result in less diagnostically useful information or no image at all.

Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.