Siemens Medical Solutions Usa, Inc.

Other

Girish Bal

Frank Kehren

Vladimir Y Panin

Matthias Fenchel

Christian J Michel

Johan Nuyts

William Curtis Howe

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.

Pet imaging using multiple organ specific short CT scans

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.

Multi-bed elastic motion correction

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. On the other hand, in dynamic PET studies the uptake in the patient's arms reaches a steady state only after the tracer distributes throughout the body. Hence, for dynamic scans, the projection data of all frames is summed and used to generate the MLAA based extended mu-map for all frames.

Generating attenuation correction maps for combined modality imaging studies and improving generated attenuation correction maps using MLAA and DCC algorithms

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Knoxville, TN, United States of America

Girish Bal