Patient-dependent count-rate adaptive normalization for PET detector efficiency with delayed-window coincidence events

Quantitative PET imaging is widely used in clinical diagnosis in oncology and neuroimaging. Accurate normalization correction for the efficiency of each line-of- response is essential for accurate quantitative PET image reconstruction. In this paper, we propose a normalization calibration method by using the delayed-window coincidence events from the scanning phantom or patient. The proposed method could dramatically reduce the 'ring' artifacts caused by mismatched system count-rates between the calibration and phantom/patient datasets. Moreover, a modified algorithm for mean detector efficiency estimation is proposed, which could generate crystal efficiency maps with more uniform variance. Both phantom and real patient datasets are used for evaluation. The results show that the proposed method could lead to better uniformity in reconstructed images by removing ring artifacts, and more uniform axial variance profiles, especially around the axial edge slices of the scanner. The proposed method also has the potential benefit to simplify the normalization calibration procedure, since the calibration can be performed using the on-the-fly acquired delayed-window dataset.

[1]  Eiji Yoshida,et al.  Monte Carlo simulation of sensitivity and NECR of an entire-body PET scanner , 2014, Radiological Physics and Technology.

[2]  R. Leahy,et al.  Model-based normalization for iterative 3D PET image reconstruction , 2002 .

[3]  R. Huesman,et al.  Effect of errors in the system matrix on iterative image reconstruction , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[4]  Jonathan Ka Poon,et al.  The Performance Limits of Long Axial Field of View PET Scanners , 2013 .

[5]  H. Du,et al.  A new modular and scalable detector for a Time-of-Flight PET scanner , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[6]  Jinyi Qi,et al.  Iterative reconstruction of Fourier-rebinned PET data using sinogram blurring function estimated from point source scans. , 2010, Medical physics.

[7]  J. Ollinger.  Model-based scatter correction for fully 3D PET. , 1996, Physics in medicine and biology.

[8]  M. Defrise,et al.  Fourier rebinning of time-of-flight PET data , 2005, Physics in medicine and biology.

[9]  D. Newport,et al.  Evaluation of simulation-based scatter correction for 3-D PET cardiac imaging , 1995 .

[10]  Roger Fulton,et al.  Joint activity and attenuation reconstruction of listmode TOF-PET data , 2015, 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[11]  Johan Nuyts,et al.  Transmission-less attenuation correction in time-of-flight PET: analysis of a discrete iterative algorithm , 2014, Physics in medicine and biology.

[12]  Hideaki Tashima,et al.  An OpenPET scanner with bridged detectors to compensate for incomplete data. , 2014, Physics in medicine and biology.

[13]  M Conti,et al.  Towards Sub-Minute PET Examination Times , 2011, IEEE Transactions on Nuclear Science.

[14]  Hongwei Ye,et al.  Improved list-mode reconstruction with an Area-Simulating-Volume projector in 3D PET , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[15]  K. Ote,et al.  A high-throughput whole-body PET scanner using flat panel PS-PMTs , 2003, IEEE Transactions on Nuclear Science.

[16]  Charalampos Tsoumpas,et al.  Incorporation of a two metre long PET scanner in STIR , 2015 .

[17]  Thomas K. Lewellen,et al.  The effect of camera geometry on singles flux, scatter fraction and trues and randoms sensitivity for cylindrical 3D PET-a simulation study , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[18]  Magnus Dahlbom,et al.  MAP reconstruction for Fourier rebinned TOF-PET data , 2014, Physics in medicine and biology.

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

[20]  V. Y. Panin.  Iterative algorithms for crystal efficiencies estimations from TOF compressed normalization data , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[21]  J S Karp,et al.  Impact of detector design on imaging performance of a long axial field-of-view, whole-body PET scanner , 2015, Physics in medicine and biology.

[22]  Joel S. Karp,et al.  Comparison of list-mode and DIRECT approaches for time-of-flight PET reconstruction , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[23]  Donald Sashin,et al.  Efficiency normalization techniques for 3D PET data , 1995, 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record.

[24]  P K Marsden,et al.  Algorithms for calculating detector efficiency normalization coefficients for true coincidences in 3D PET. , 1998, Physics in medicine and biology.

[25]  Jeih-San Liow,et al.  Count-Rate Dependent Component-Based Normalization for the HRRT , 2007, IEEE Transactions on Nuclear Science.

[26]  S. Surti,et al.  Efficient 3D TOF PET Reconstruction Using View-Grouped Histo-Images: DIRECT - Direct Image Reconstruction for TOF , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[27]  Wenli Wang,et al.  A new component approach efficiency normalization for 3D PET , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[28]  P. Fonte,et al.  Whole-Body Single-Bed Time-of-Flight RPC-PET: Simulation of Axial and Planar Sensitivities With NEMA and Anthropomorphic Phantoms , 2009, IEEE Transactions on Nuclear Science.

[29]  Jian Zhou,et al.  Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration. , 2011, Physics in medicine and biology.

[30]  Hongdi Li,et al.  The initial design and feasibility study of an affordable high-resolution 100-cm long PET , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[31]  M. Senda,et al.  Japanese guideline for the oncology FDG-PET/CT data acquisition protocol: synopsis of Version 1.0 , 2010, Annals of nuclear medicine.

[32]  H. Malcolm Hudson,et al.  Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.

[33]  Joel S Karp,et al.  Optimization of a fully 3D single scatter simulation algorithm for 3D PET. , 2004, Physics in medicine and biology.

[34]  Daisuke Kokuryo,et al.  Development of a small prototype for a proof-of-concept of OpenPET imaging , 2011, Physics in medicine and biology.

[35]  Martin A Lodge,et al.  Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application , 2013, Physics in medicine and biology.

[36]  J S Karp,et al.  Study of PET scanner designs using clinical metrics to optimize the scanner axial FOV and crystal thickness , 2013, Physics in medicine and biology.

[37]  W. K. Luk,et al.  Component-based normalization for panel detector PET scanners , 2002, IEEE Transactions on Nuclear Science.

[38]  Habib Zaidi,et al.  Four-Dimensional Image Reconstruction Strategies in Cardiac-Gated and Respiratory-Gated PET Imaging. , 2013, PET clinics.

[39]  Jian Zhou,et al.  Efficient fully 3D list-mode TOF PET image reconstruction using a factorized system matrix with an image domain resolution model , 2014, Physics in medicine and biology.

[40]  Hongwei Ye,et al.  A study of count-rate dependent normalization effect in 3D-PET data , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[41]  Maurizio Conti,et al.  An investigation of sensitivity limits in PET scanners , 2007 .

[42]  PET random reduction with a FOV-dependent coincidence window and tangential TOF-mask , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[43]  C. Michel,et al.  Performance of a high sensitivity PET scanner based on LSO panel detectors , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[44]  I. Buvat,et al.  A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology , 2012, Physics in medicine and biology.

[45]  Jong Beom Ra,et al.  Ultra-Fast Hybrid CPU–GPU Multiple Scatter Simulation for 3-D PET , 2014, IEEE Journal of Biomedical and Health Informatics.

[46]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[47]  Jinyi Qi,et al.  Effect of errors in the system matrix on maximum a posteriori image reconstruction , 2005, Physics in medicine and biology.

[48]  Nuno Ferreira,et al.  Sensitivity assessment of wide Axial Field of View PET systems via Monte Carlo simulations of NEMA-like measurements , 2007 .

[49]  R. Siddon.  Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[50]  Eiji Yoshida,et al.  Monte Carlo simulation of efficient data acquisition for an entire-body PET scanner , 2014 .

[51]  A Geissbuhler,et al.  A normalization technique for 3D PET data. , 1991, Physics in medicine and biology.

[52]  Vladimir Y. Panin,et al.  LSO background radiation as a transmission source using time of flight information , 2013 .

[53]  G. Knoll.  Radiation Detection And Measurement, 3rd Ed , 2009 .

[54]  Giovanni Lucignani,et al.  PET systems: the value of added length , 2010, European Journal of Nuclear Medicine and Molecular Imaging.