TY - GEN
T1 - Anatomy-assisted direct parametric PET imaging for myocardial blood flow abnormality detection
AU - Deng, Wei
AU - Wang, Xinhui
AU - Yang, Bao
AU - Tang, Jing
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/10/3
Y1 - 2016/10/3
N2 - Dynamic myocardial perfusion (MP) PET imaging followed by tracer kinetic modeling provides quantitative measurement of myocardial blood flow (MBF). The purpose of this study is to incorporate anatomical information in the 4D direct parametric image reconstruction and to evaluate the performance in detecting regional MBF abnormality. The one-tissue compartment model was formulated in the maximum likelihood (ML) problem to relate the dynamic projection datasets directly to the kinetic parameters. A maximum a posteriori (MAP) algorithm that incorporates the joint entropy (JE) between the anatomic and parametric images in the reconstruction was developed. The preconditioned steepest ascent (PSA) algorithm was used to solve the ML and the JE-MAP estimation problems. Using the XCAT phantom and the patient-based organ time activity curves, we simulated two sets of dynamic MP Rb-82 PET data, one carrying normal MBF and the other with reduced MBF on a region of interest, each with 20 noise realizations. Corresponding MR images were simulated with the 3D T1-weighted sequence as specified in a clinical PET/MRI protocol. The reconstructed parametric images from the ML and the JE-MAP algorithms were compared using the tradeoff between noise and bias and the signal to noise ratio (SNR), which reflects the separability between the normal and abnormal K1 parameters. The proposed JE-MAP algorithm resulted in improved noise versus bias tradeoff compared to the ML algorithm and also demonstrated better performance in the regional abnormal MBF detection task.
AB - Dynamic myocardial perfusion (MP) PET imaging followed by tracer kinetic modeling provides quantitative measurement of myocardial blood flow (MBF). The purpose of this study is to incorporate anatomical information in the 4D direct parametric image reconstruction and to evaluate the performance in detecting regional MBF abnormality. The one-tissue compartment model was formulated in the maximum likelihood (ML) problem to relate the dynamic projection datasets directly to the kinetic parameters. A maximum a posteriori (MAP) algorithm that incorporates the joint entropy (JE) between the anatomic and parametric images in the reconstruction was developed. The preconditioned steepest ascent (PSA) algorithm was used to solve the ML and the JE-MAP estimation problems. Using the XCAT phantom and the patient-based organ time activity curves, we simulated two sets of dynamic MP Rb-82 PET data, one carrying normal MBF and the other with reduced MBF on a region of interest, each with 20 noise realizations. Corresponding MR images were simulated with the 3D T1-weighted sequence as specified in a clinical PET/MRI protocol. The reconstructed parametric images from the ML and the JE-MAP algorithms were compared using the tradeoff between noise and bias and the signal to noise ratio (SNR), which reflects the separability between the normal and abnormal K1 parameters. The proposed JE-MAP algorithm resulted in improved noise versus bias tradeoff compared to the ML algorithm and also demonstrated better performance in the regional abnormal MBF detection task.
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U2 - 10.1109/NSSMIC.2015.7582181
DO - 10.1109/NSSMIC.2015.7582181
M3 - Conference contribution
AN - SCOPUS:84994311319
T3 - 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
BT - 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
Y2 - 31 October 2015 through 7 November 2015
ER -