Maximum likelihood activity and attenuation estimation using both emission and transmission data with application to utilization of Lu-176 background radiation in TOF PET.

PURPOSE We present a new method for joint reconstruction of activity and attenuation images using both emission and transmission data and demonstrate its advantage over the standard maximum likelihood activity and attenuation (MLAA) reconstruction using emission data alone. METHODS We define a joint likelihood function including both TOF emission data and transmission data. The latter can be obtained from an external source or from Lu-176 background radiation. Activity and attenuation images are estimated jointly by maximizing the likelihood function. The proposed method solves the undermined scale problem in the conventional MLAA. A monotonically convergent algorithm was derived to optimize the objective function. Furthermore, we present a theoretical analysis of the noise propagation in the joint reconstruction. Simulations and experiment were conducted to validate the feasibility of the proposed method. RESULTS Quantitatively correct and less noisy images were reconstructed with the proposed method. Artifacts in the attenuation map reconstructed from the standard MLAA were removed by incorporating transmission data. Noise analysis was validated with different transmission sources and transmission count levels. The theoretical prediction indicated that noise of activity map would not change in a large range of transmission count level and a very low transmission count level could result in good estimation. CONCLUSIONS The results demonstrate the feasibility of obtaining quantitatively correct images in time of flight PET by using both emission and (weak) transmission data. The noise analysis also provides guidance for choosing a proper transmission source configuration to reduce noise propagation.

[1]  Jeffrey A. Fessler,et al.  Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs , 1996, IEEE Trans. Image Process..

[2]  Hakan Erdogan,et al.  Ordered subsets algorithms for transmission tomography. , 1999, Physics in medicine and biology.

[3]  C Lartizien,et al.  GATE: a simulation toolkit for PET and SPECT. , 2004, Physics in medicine and biology.

[4]  M. Defrise,et al.  Time-of-flight PET data determine the attenuation sinogram up to a constant , 2012, Physics in medicine and biology.

[5]  Suleman Surti,et al.  Benefit of Time-of-Flight in PET: Experimental and Clinical Results , 2008, Journal of Nuclear Medicine.

[6]  Jeffrey A. Fessler,et al.  Accelerating Ordered Subsets Image Reconstruction for X-ray CT Using Spatially Nonuniform Optimization Transfer , 2013, IEEE Transactions on Medical Imaging.

[7]  Richard M. Leahy,et al.  A theoretical study of the contrast recovery and variance of MAP reconstructions from PET data , 1999, IEEE Transactions on Medical Imaging.

[8]  Vladimir Panin,et al.  LSO background radiation as a transmission source using time of flight , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).

[9]  J. S. Karp,et al.  Recent developments in time-of-flight PET , 2016, EJNMMI Physics.

[10]  Michael V. Green,et al.  Characteristics of a pair of small field-of-view LSO scintillation cameras , 1996 .

[11]  D. Townsend,et al.  Physical and clinical performance of the mCT time-of-flight PET/CT scanner , 2011, Physics in medicine and biology.

[12]  C. Watson Extension of Single Scatter Simulation to Scatter Correction of Time-of-Flight PET , 2007, IEEE Transactions on Nuclear Science.

[13]  M. S. Basunia,et al.  Nuclear Data Sheets for A=176 , 2006 .

[14]  Jian Zhou,et al.  Performance of the Tachyon Time-of-Flight PET Camera , 2015, IEEE Transactions on Nuclear Science.

[15]  Jeffrey A. Fessler Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography , 1996, IEEE Trans. Image Process..

[16]  W. Moses,et al.  Total-Body PET: Maximizing Sensitivity to Create New Opportunities for Clinical Research and Patient Care , 2018, The Journal of Nuclear Medicine.

[17]  M. Defrise,et al.  Iterative reconstruction for helical CT: a simulation study. , 1998, Physics in medicine and biology.

[18]  Stefaan Vandenberghe,et al.  Comparison of transmission- and emission-based attenuation correction for TOF-PET/MRI , 2014, 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[19]  K. Lange,et al.  EM reconstruction algorithms for emission and transmission tomography. , 1984, Journal of computer assisted tomography.

[20]  Paul Kinahan,et al.  Attenuation correction for a combined 3D PET/CT scanner. , 1998, Medical physics.

[21]  Hui Yang,et al.  Performance Evaluation of a High-resolution TOF Clinical PET/CT , 2016 .

[22]  M E Casey,et al.  Simultaneous reconstruction of emission activity and attenuation coefficient distribution from TOF data, acquired with external transmission source , 2013, Physics in medicine and biology.

[23]  Richard M. Leahy,et al.  Resolution and noise properties of MAP reconstruction for fully 3-D PET , 2000, IEEE Transactions on Medical Imaging.

[24]  Johan Nuyts,et al.  ML-reconstruction for TOF-PET with simultaneous estimation of the attenuation factors , 2014, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[25]  Habib Zaidi,et al.  Joint Estimation of Activity and Attenuation in Whole-Body TOF PET/MRI Using Constrained Gaussian Mixture Models , 2015, IEEE Transactions on Medical Imaging.

[26]  A. Buck,et al.  PET attenuation coefficients from CT images: experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients , 2002, European Journal of Nuclear Medicine and Molecular Imaging.

[27]  M. Conti Why is TOF PET reconstruction a more robust method in the presence of inconsistent data? , 2011, Physics in medicine and biology.

[28]  L. Shao,et al.  A generalized model for the conversion from CT numbers to linear attenuation coefficients , 2002 .

[29]  Yun Dong,et al.  A High-Resolution Time-of-Flight Clinical PET Detection System Using a Gapless PMT-Quadrant-Sharing Method , 2015, IEEE Transactions on Nuclear Science.