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    • 1. 发明申请
    • Generating Attenuation Correction Maps for Combined Modality Imaging Studies and Improving Generated Attenuation Correction Maps Using MLAA and DCC Algorithms
    • 使用MLAA和DCC算法生成组合模态成像研究和改进生成的衰减校正图的衰减校正图
    • US20140056500A1
    • 2014-02-27
    • US13970753
    • 2013-08-20
    • Girish BalFrank KehrenVladimir Y. PaninChristian J. MichelJohan Nuyts
    • Girish BalFrank KehrenVladimir Y. PaninChristian J. MichelJohan Nuyts
    • G06T11/00
    • G06T11/005
    • 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.
    • DCC(数据一致条件)算法与MLAA(衰减和活动的最大似然重构)结合使用,以产生用于核医学成像研究的扩展衰减校正图。 MLAA和DCC是可以用于基于PET数据确定mu-map精度的互补算法。 MLAA有助于根据示踪剂的生物分布估计μ值,而DCC检查是否满足给定mu-map的一致性条件。 将这些方法组合起来,以便更好地估计μ值。 在门控MR / PET心脏研究中,PET数据被框架成多个门,并且生成与每个门对应的一系列基于MR的mu图。 来自所有门的PET数据被组合。 一旦生成了扩展的mu-map,就将中心区域替换为与该特定门对应的基于MR的mu-map。 另一方面,在动态PET研究中,仅在跟踪器分布在整个身体之后,患者的手臂中的摄取才达到稳定状态。 因此,对于动态扫描,将所有帧的投影数据求和并用于为所有帧生成基于MLAA的扩展mu-map。
    • 3. 发明授权
    • Generating attenuation correction maps for combined modality imaging studies and improving generated attenuation correction maps using MLAA and DCC algorithms
    • 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
    • US09053569B2
    • 2015-06-09
    • US13970753
    • 2013-08-20
    • Girish BalFrank KehrenVladimir Y. PaninChristian J. MichelJohan Nuyts
    • Girish BalFrank KehrenVladimir Y. PaninChristian J. MichelJohan Nuyts
    • G06K9/00G06T11/00
    • G06T11/005
    • 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.
    • DCC(数据一致条件)算法与MLAA(衰减和活动的最大似然重构)结合使用,以产生用于核医学成像研究的扩展衰减校正图。 MLAA和DCC是可以用于基于PET数据确定mu-map精度的互补算法。 MLAA有助于根据示踪剂的生物分布估计μ值,而DCC检查是否满足给定mu-map的一致性条件。 将这些方法组合起来,以便更好地估计μ值。 在门控MR / PET心脏研究中,PET数据被框架成多个门,并且生成与每个门对应的一系列基于MR的mu图。 来自所有门的PET数据被组合。 一旦生成了扩展的mu-map,就将中心区域替换为与该特定门对应的基于MR的mu-map。 另一方面,在动态PET研究中,仅在跟踪器分布在整个身体之后,患者的手臂中的摄取才达到稳定状态。 因此,对于动态扫描,将所有帧的投影数据求和并用于为所有帧生成基于MLAA的扩展mu-map。