Abstract: A method of minimizing a patient's exposure to CT scan radiation during the mu-map generation process in a long axial field of view (FOV) PET scan includes performing a long axial FOV PET scan on a patient; performing one or multiple truncated FOV CT scan of a region in the patient's body in which the organs of interest lies; generating a truncated mu-map covering the truncated CT FOV; and generating a mu-map for the whole long axial FOV of the PET scan by extending the truncated mu-map generated from the truncated FOV CT scan by estimating the missing mu-map data using the PET data.

Abstract: A method for performing a multi-bed scan includes receiving scanner-specific information including scanner sensitivity and receiving patient-specific information including attenuation. An attenuation-weighted sensitivity profile is calculated based on the scanner sensitivity and the attenuation. Individual bed scan times for each bed in a multi-bed study is calculated based on the attenuation-weighted sensitivity profile and the multi-bed scan is performed using the calculated individual bed scan times.

Abstract: A set of first modality data (e.g., MR or CT) is provided. The set of first modality data comprises a plurality of mu-maps, a plurality of motion vectors and a plurality of gated data. Each of the mu-maps corresponds to one of the beds. A set of second modality data (e.g., PET/SPECT) is provided. The set of second modality data comprises a plurality of frames for each of the beds. Each of the plurality of frames is warped by one or more motion vectors of the plurality of motion vectors. A single-bed image is generated for each bed by summing the frames corresponding to the bed. A whole body image is generated by summing the single-bed images for each of the beds.

Abstract: A set of first modality data (e.g., MR or CT) is provided. The set of first modality data comprises a plurality of mu-maps, a plurality of motion vectors and a plurality of gated data. Each of the mu-maps corresponds to one of the beds. A set of second modality data (e.g., PET/SPECT) is provided. The set of second modality data comprises a plurality of frames for each of the beds. Each of the plurality of frames is warped by one or more motion vectors of the plurality of motion vectors. A single-bed image is generated for each bed by summing the frames corresponding to the bed. A whole body image is generated by summing the single-bed images for each of the beds.

Abstract: The DCC (Data Consistency Condition) algorithm is used in combination with MLAA (Maximum Likelihood reconstruction of Attenuation and Activity) to generate extended attenuation correction maps for nuclear medicine imaging studies. MLAA and DCC are complementary algorithms that can be used to determine the accuracy of the mu-map based on PET data. MLAA helps to estimate the mu-values based on the biodistribution of the tracer while DCC checks if the consistency conditions are met for a given mu-map. These methods are combined to get a better estimation of the mu-values. In gated MR/PET cardiac studies, the PET data is framed into multiple gates and a series of MR based mu-maps corresponding to each gate is generated. The PET data from all gates is combined. Once the extended mu-map is generated the central region is replaced with the MR based mu-map corresponding to that particular gate.

Abstract: The DCC (Data Consistency Condition) algorithm is used in combination with MLAA (Maximum Likelihood reconstruction of Attenuation and Activity) to generate extended attenuation correction maps for nuclear medicine imaging studies. MLAA and DCC are complementary algorithms that can be used to determine the accuracy of the mu-map based on PET data. MLAA helps to estimate the mu-values based on the biodistribution of the tracer while DCC checks if the consistency conditions are met for a given mu-map. These methods are combined to get a better estimation of the mu-values. In gated MR/PET cardiac studies, the PET data is framed into multiple gates and a series of MR based mu-maps corresponding to each gate is generated. The PET data from all gates is combined. Once the extended mu-map is generated the central region is replaced with the MR based mu-map corresponding to that particular gate.

Abstract: An apparatus and methods for evaluating the operation of pixelated detectors are provided. The method includes obtaining data values for each of a plurality of pixels of a pixelated detector and determining a data consistency metric for each of the plurality of detector pixels. The method further includes identifying, using the determined data consistency metric, any detector pixels that exceed an acceptance criterion as noisy pixels.

Abstract: Apparatus and methods for determining a system matrix for pinhole collimator imaging systems are provided. One method includes using a closed form expression to determine a penetration term for a collimator of the medical imaging system and determining a point spread function of the collimator based on the penetration term. The method further includes calculating the system matrix for the medical imaging system based on the determined point spread function.

Abstract: Apparatus and methods for determining a system matrix for pinhole collimator imaging systems are provided. One method includes using a closed form expression to determine a penetration term for a collimator of the medical imaging system and determining a point spread function of the collimator based on the penetration term. The method further includes calculating the system matrix for the medical imaging system based on the determined point spread function.

Abstract: An apparatus and methods for evaluating the operation of pixelated detectors are provided. The method includes obtaining data values for each of a plurality of pixels of a pixelated detector and determining a data consistency metric for each of the plurality of detector pixels. The method further includes identifying, using the determined data consistency metric, any detector pixels that exceed an acceptance criterion as noisy pixels.

Abstract: An imaging system for acquiring multi-dimensional tomographic image data of an object, the imaging system having (1) a plurality of detectors to acquire image data, the detectors coupled to a supporting structure, wherein at least one of the detectors is adapted to move relative to the object during image data acquisition and wherein the detectors are adapted to rotate independently of each other, provided that image data from the detectors are collected concomitantly during a study time and (2) a data analyzer adapted to acquire and/or reconstruct the image data.

Abstract: An imaging system for acquiring multi-dimensional tomographic image data of an object, the imaging system having (1) a plurality of detectors to acquire image data, the detectors coupled to a supporting structure, wherein at least one of the detectors is adapted to move relative to the object during image data acquisition and wherein the detectors are adapted to rotate independently of each other, provided that image data from the detectors are collected concomitantly during a study time and (2) a data analyzer adapted to acquire and/or reconstruct the image data.