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Generating Attenuation Correction Maps for Combined Modality Imaging Studies and Improving Generated Attenuation Correction Maps Using MLAA and DCC Algorithms
Generating Attenuation Correction Maps for Combined Modality Imaging Studies and Improving Generated Attenuation Correction Maps Using MLAA and DCC Algorithms
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机译：生成用于组合模态成像研究的衰减校正图，并使用MLAA和DCC算法改进生成的衰减校正图
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摘要
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.
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