A Novel Method to Improve Image Quality for 2-D Small Animal PET Reconstruction by Correcting a Monte Carlo-Simulated System Matrix Using an Artificial Neural Network

The aim of this study was to improve image quality of statistical reconstruction by using the 2-D system matrix (SM) trained with an artificial neural network (ANN). For training the ANN SM (SMANN), the inputs of ANN, the digital images, were generated by scanning the mini-deluxe cold spot phantom at ten different orientations using an optical scanner (resolution: 0.01 mm/pixel). The desired outputs were generated by acquiring the projection data with the corresponding angles using the micro positron emission tomography (microPET) R4. In the ANN method, the ADALINE network with a bias vector and a momentum term were used. Moreover, a multiline-source phantom and a four-segment phantom were scanned to obtain the spatial resolutions and the quantitative accuracy for comparison, respectively. A rat FDG microPET image was acquired to compare the difference between the results reconstructed by the microPET's built-in 2-D-ordered subsets expectation maximization algorithm (OSEM), OSEMmicroPET, OSEM by Monte Carlo simulated SM, OSEMSMd, and OSEM by the SMANN, OSEMANN. In the multiline-source experiment, the resolutions of OSEMmicroPET measured at center, 10, and 20 mm from the center were 1.61, 1.78, and 2.30 mm, respectively. The resolutions of OSEMSMd and OSEMANN were 1.24, 1.68, and 1.87, and 1.28, 1.62, and 1.72 mm, respectively. In the results of the four-segment phantom, the sum of absolute error of the truth versus the values reconstructed by OSEMmicroPET, OSEMSMd, and OSEMANN were 1085.53, 913.48, and 435.02, respectively. By interpreting the results of the evaluation, the image quality reconstructed by the SMANN is better than that reconstructed by the original SM. The results indicated that SM can be updated toward ideal SM using ANN for statistical reconstruction.

[1]  M. Rafecas,et al.  Use of a Monte Carlo-based probability matrix for 3-D iterative reconstruction of MADPET-II data , 2004, IEEE Transactions on Nuclear Science.

[2]  R. Lecomte,et al.  Detector response models for statistical iterative image reconstruction in high resolution PET , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[3]  N. Giokaris,et al.  Tomographic image reconstruction using Artificial Neural Networks , 2004 .

[4]  T. J. Hebert,et al.  Pre-reconstruction Restoration Of SPECT Projection Images With A Neural Network , 1993 .

[5]  G. Muehllehner,et al.  Positron emission tomography , 2006, Physics in medicine and biology.

[6]  Sze Fong Yau,et al.  Training of artificial neural network for tomographic reconstruction of time varying object , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[7]  Albert Macovski,et al.  A Maximum Likelihood Approach to Emission Image Reconstruction from Projections , 1976, IEEE Transactions on Nuclear Science.

[8]  M E Phelps,et al.  Positron emission tomography. , 1979, Medical instrumentation.

[9]  Vladimir Y. Panin,et al.  Fully 3-D PET reconstruction with system matrix derived from point source measurements , 2006, IEEE Transactions on Medical Imaging.

[10]  R. N. Goble,et al.  Performance evaluation of the microPET R4 PET scanner for rodents , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

[11]  J. Bowsher,et al.  Artificial neural networks for SPECT image reconstruction with optimized weighted backprojection , 1991, Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference.

[12]  Roberto de la Prieta.  An Accurate and Parallelizable Geometric Projector/Backprojector for 3D PET Image Reconstruction , 2004, ISBMDA.

[13]  Martin T. Hagan,et al.  Neural network design , 1995 .

[14]  P Knoll,et al.  An artificial neural net and error backpropagation to reconstruct single photon emission computerized tomography data. , 1999, Medical physics.

[15]  James E. Bowsher,et al.  A spatially-variant SPECT reconstruction scheme using artificial neural networks , 1992, IEEE Conference on Nuclear Science Symposium and Medical Imaging.

[16]  Sze Fong Yau,et al.  An artificial neural network for tomographic reconstruction of time varying object , 1994, Proceedings of ICSIPNN '94. International Conference on Speech, Image Processing and Neural Networks.

[17]  C. C. Watson,et al.  New, faster, image-based scatter correction for 3D PET , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

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

[19]  C. Byrne,et al.  Reconstruction of 2D PET data with Monte Carlo generated system matrix for generalized natural pixels , 2006, Physics in Medicine and Biology.

[20]  Uday B. Desai,et al.  Image restoration using a multilayer perceptron with a multilevel sigmoidal function , 1993, IEEE Trans. Signal Process..

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

[22]  D. McElroy,et al.  Use of a Monte-Carlo based probability matrix for 3D iterative reconstruction of MADPET-II data , 2003, 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).

[23]  Markus Alber,et al.  A parallelizable compression scheme for Monte Carlo scatter system matrices in PET image reconstruction , 2007, Physics in medicine and biology.

[24]  M. Rafecas,et al.  Effect of noise in the probability matrix used for statistical reconstruction of PET data , 2002, 2002 IEEE Nuclear Science Symposium Conference Record.

[25]  K L Leenders,et al.  Artificial neural network Radon inversion for image reconstruction. , 2001, Medical physics.

[26]  Habib Zaidi,et al.  Scatter modelling and compensation in emission tomography , 2004, European Journal of Nuclear Medicine and Molecular Imaging.

[27]  J J Vaquero,et al.  FIRST: Fast Iterative Reconstruction Software for (PET) tomography. , 2006, Physics in medicine and biology.

[28]  R. Leahy,et al.  High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner. , 1998, Physics in medicine and biology.

[29]  C R Floyd,et al.  An artificial neural network for SPECT image reconstruction. , 1991, IEEE transactions on medical imaging.

[30]  I Buvat,et al.  Fully 3D Monte Carlo reconstruction in SPECT: a feasibility study , 2005, Physics in medicine and biology.