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 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 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: A method for iterative reconstruction of tomographic images of an object of interest includes obtaining data corresponding to a field of view of a detector, processing the data to prepare a reconstruction support region corresponding to a region within the field of view encompassing the object, and reconstructing a tomographic image using the reconstruction support region. Preferably, the data corresponding to the field of view includes at least one of a projection image of the object, an orbit location of a detector relative to a center of rotation of the detector, or an attenuation coefficient map of the object, which data can be used to describe a reconstruction support region corresponding to the object.

Abstract: Implementations of the present technology include error mitigation processes that determine gantry angle-dependent measures of detector deflections for a given class of systems using a first method, then determine gantry angle-independent deviations from the class measures using a second method on a specific system; then apply, to the specific system of the second method, the gantry angle-dependent class deflection results of the first method modified by the system-specific gantry angle independent deflections of the second method; and further include a system calibrated by such combinations of processes and computer program products for performing at least portions of the combination of processes.

Abstract: A method and system for compensating truncated transmission projection data used for attenuation correction in a SPECT apparatus. The method and system include generating an initial estimate of a transmission reconstruction without modeling source geometry or detector collimator response and refining the transmission reconstruction by using the initial estimate with a modified model of the source geometry and the collimation geometry.

Abstract: A method for iterative reconstruction of tomographic images of an object of interest includes obtaining data corresponding to a field of view of a detector, processing the data to prepare a reconstruction support region corresponding to a region within the field of view encompassing the object, and reconstructing a tomographic image using the reconstruction support region. Preferably, the data corresponding to the field of view includes at least one of a projection image of the object, an orbit location of a detector relative to a center of rotation of the detector, or an attenuation coefficient map of the object, which data can be used to describe a reconstruction support region corresponding to the object.

Abstract: A method and system for compensating truncated transmission projection data used for attenuation correction in a SPECT apparatus. The method and system include generating an initial estimate of a transmission reconstruction without modeling source geometry or detector collimator response and refining the transmission reconstruction by using the initial estimate with a modified model of the source geometry and the collimation geometry.