Accelerated simulation of cone beam X-ray scatter projections

Monte Carlo (MC) methods can accurately simulate scatter in X-ray imaging. However, when low noise scatter projections have to be simulated these MC simulations tend to be very time consuming. Rapid computation of scatter estimates is essential for several applications. The aim of the work presented in this paper is to speed up the estimation of noise-free scatter projections while maintaining their accuracy. Since X-ray scatter projections are often rather smooth, an approach is chosen whereby a short MC simulation is combined with a data fitting program that is robust to projection truncation and noise. This method allows us to estimate the smooth scatter projection rapidly. The speed-up and accuracy achieved by using the fitting algorithm were validated for the projection simulation of a small animal X-ray CT system. The acceleration that can be obtained over standard MC simulations is typically two orders of magnitude, depending on the accuracy required. The proposed approach may be useful for rapid simulation of patient and animal studies and for correction of the image-degrading effects of scatter in tomography.

[1]  G Panayiotakis,et al.  Monte Carlo generated mammograms: development and validation. , 1998, Physics in medicine and biology.

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

[3]  M Honda,et al.  Method for estimating the intensity of scattered radiation using a scatter generation model. , 1991, Medical physics.

[4]  P M Joseph,et al.  The effects of scatter in x-ray computed tomography. , 1982, Medical physics.

[5]  W Kalender,et al.  Monte Carlo calculations of x-ray scatter data for diagnostic radiology. , 1981, Physics in medicine and biology.

[6]  H Kanamori,et al.  Effects of scattered X-rays on CT images. , 1985, Physics in medicine and biology.

[7]  M. H. Kalos,et al.  On the Estimation of Flux at a Point by Monte Carlo , 1963 .

[8]  C E Floyd,et al.  Inverse Monte Carlo as a unified reconstruction algorithm for ECT. , 1986, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[9]  U Neitzel,et al.  Coherent scatter in radiographic imaging: a Monte Carlo simulation study. , 1985, Physics in medicine and biology.

[10]  L. Lucy An iterative technique for the rectification of observed distributions , 1974 .

[11]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[12]  P. C. Johns,et al.  Scattered radiation in fan beam imaging systems. , 1982, Medical physics.

[13]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[14]  G H Glover,et al.  Compton scatter effects in CT reconstructions. , 1983, Medical physics.

[15]  E. Hoffman,et al.  Investigation of accelerated Monte Carlo techniques for PET simulation and 3-D PET scatter correction , 1999 .

[16]  J F Williamson,et al.  Monte Carlo evaluation of kerma at a point for photon transport problems. , 1987, Medical physics.

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

[18]  K Doi,et al.  The validity of Monte Carlo simulation in studies of scattered radiation in diagnostic radiology. , 1983, Physics in medicine and biology.

[19]  C. J. Leliveld A fast Monte Carlo simulator for scattering in X-ray computerized tomography , 1996 .

[20]  J. Boone,et al.  An analytical model of the scattered radiation distribution in diagnostic radiology. , 1988, Medical physics.

[21]  R. Jaszczak,et al.  Inverse Monte Carlo: A Unified Reconstruction Algorithm for SPECT , 1985, IEEE Transactions on Nuclear Science.

[22]  D. Jaffray,et al.  Optimization of x-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography). , 2000, Medical physics.

[23]  E. Veklerov,et al.  MLE reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule , 1988 .

[24]  E. Hoffman,et al.  A Monte Carlo correction for the effect of Compton scattering in 3-D PET brain imaging , 1995 .

[25]  Freek J. Beekman,et al.  Efficient fully 3-D iterative SPECT reconstruction with Monte Carlo-based scatter compensation , 2002, IEEE Transactions on Medical Imaging.

[26]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[27]  D. Jaffray,et al.  Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. , 2001, Medical physics.

[28]  J A Seibert,et al.  Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology. , 1988, Medical physics.

[29]  T Bortfeld,et al.  Correction of scatter in megavoltage cone-beam CT , 2001, Physics in medicine and biology.

[30]  M. Endo,et al.  Effect of scattered radiation on image noise in cone beam CT. , 2001, Medical physics.