TY - GEN
T1 - Direct 4d reconstruction of parametric images incorporating anato-functional joint entropy
AU - Tang, Jing
AU - Kuwabara, Hiroto
AU - Wong, Dean F.
AU - Rahmim, Arman
PY - 2008
Y1 - 2008
N2 - We developed a closed-form 40 algorithm to directly reconstruct parametric images as obtained using the Patlak graphical method for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 20/30 PET data, followed by graphical analysis on the sequence of reconstructed images. The proposed approach maintains the simplicity and accuracy of the EM algorithm by extending the system matrix to include the relation between the parametric images and the measured data. The proposed technique achieves a closed-form solution by utilizing a different hidden complete-data formulation within the EM framework. Additionally, the method is extended to maximum a posterior (MAP) reconstruction via incorporating MR image information, with the joint entropy between the MR and parametric PET features. AParzen window method was used to estimate the joint probability density of the MR and parametric PET images. Using realistic simulated [llC]-Naitrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise vs. bias performance have been achieved, when performing direct parametric reconstruction, and additionally when extending the algorithm to its Bayesian counter-part using MR-PET join entropy.
AB - We developed a closed-form 40 algorithm to directly reconstruct parametric images as obtained using the Patlak graphical method for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 20/30 PET data, followed by graphical analysis on the sequence of reconstructed images. The proposed approach maintains the simplicity and accuracy of the EM algorithm by extending the system matrix to include the relation between the parametric images and the measured data. The proposed technique achieves a closed-form solution by utilizing a different hidden complete-data formulation within the EM framework. Additionally, the method is extended to maximum a posterior (MAP) reconstruction via incorporating MR image information, with the joint entropy between the MR and parametric PET features. AParzen window method was used to estimate the joint probability density of the MR and parametric PET images. Using realistic simulated [llC]-Naitrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise vs. bias performance have been achieved, when performing direct parametric reconstruction, and additionally when extending the algorithm to its Bayesian counter-part using MR-PET join entropy.
KW - 40 PET reconstruction
KW - Anato-functional joint entropy
KW - Parametric image estimation
UR - http://www.scopus.com/inward/record.url?scp=67649212370&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2008.4774491
DO - 10.1109/NSSMIC.2008.4774491
M3 - Conference contribution
AN - SCOPUS:67649212370
SN - 9781424427154
T3 - IEEE Nuclear Science Symposium Conference Record
SP - 5471
EP - 5474
BT - 2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008
T2 - 2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008
Y2 - 19 October 2008 through 25 October 2008
ER -