planar and spect monte carlo acceleration using a variance reduction technique in i131 imaging

Planar and SPECT Monte Carlo acceleration using a variance reduction technique in I131 imaging

Background: Various variance reduction techniques such as forced detection (FD) have been implemented in Monte Carlo (MC) simulation of nuclear medicine in an effort to decrease the simulation time while keeping accuracy. However most of these techniques still result in very long MC simulation times for being implemented into routine use. Materials and Methods: Convolution-based force...

Optimization of a SPECT system for imaging of 90Y in liver using Monte Carlo method

Introduction: Acquiring a high quality image has assigned an important concern for obtaining accurate diagnosis in nuclear medicine. Detector and collimator are critical component of Single Photon Emission Computed Tomography (SPECT) imaging system for giving accurate information from exact pattern of radionuclide distribution in the target organ. The images are strongly affect...

Absorbed dose of Fetus in SPECT-CT Imaging of Mother's Abdomen Regions using Monte Carlo method

SPECT-CT is an imaging method for diagnosing diseases and studying physiology of the body. In this type of imaging, due to the use of radiopharmaceutical for imaging (SPECT) and X-rays (CT), a relatively high dose of radiation is introduced into the patient. If the patient is pregnant, considering that the developing fetus is sensitive to any radiation in the uterus throughout the entire period...

Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develo...

Variance Reduction in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) has proven to be a powerful, generic planning technique for decision-making in single-agent and adversarial environments. The stochastic nature of the Monte-Carlo simulations introduces errors in the value estimates, both in terms of bias and variance. Whilst reducing bias (typically through the addition of domain knowledge) has been studied in the MCTS literature...

Monte Carlo Integration and Variance Reduction