Monte Carlo Modeling in Nuclear Medicine Imaging

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

“…Unsupervised classification algorithms can be applied to the cases when there are few available segmentation samples or when the interested structures have large shape variations and have shown promise for medical image segmentation in the fields like the tumor detection in positron emission tomography (PET) imaging (Zaidi, 2005). However, pattern recognition models are also sensitive to noise.…”

“…Information used for medical image processing comes not only from image appearances but also from imaging devices and doctors' professional knowledge. A prior knowledge such as the imaging environment or structures' biomechanical behavior can be crucial information for designing an effective algorithm, especially when the images are influenced by noises or partial volume effects (Zaidi, 2005). Also, the appearances of the same organs or structures may vary in different slices and imaging modalities and therefore, may need distinctive segmentation algorithms.…”

Design and assessment of a novel SPECT system for desktop open-gantry imaging of small animals: A simulation study

“…Furthermore, nonrestrained/nonanesthetized animal imaging is expected to be more feasible, given better ability to track motion and its various degrees of freedom. [27][28][29] Monte Carlo (MC) simulation in the field of medical imaging and nuclear medicine is an important tool 30 in design and development, [31][32][33] performance evaluation, 34,35 and correction and optimization 24,[36][37][38][39] of systems. Despite the accuracy and reliability of MC modeling, it is a time-consuming approach in simulating the process of data acquisition in a medical imaging system, e.g., SPECT or PET.…”

Evaluation of the Respiratory Motion Effect in Small Animal PET Images with GATE Monte Carlo Simulations

“…Virtually, any complex system can in principle be modeled: if the distribution of events that occur in a system is know from experience, a PDF can be generated and sampled randomly to simulate the real system. A detailed description of the general principles and applications of the Monte Carlo method can be found elsewhere: (Andreo, 1991;Zaidi, 1999;Ljungberg, 1998Ljungberg, , 2004Zaidi & Sgouros, 2002;Zaidi, 2006). The simulation of PET imaging using Monte Carlo allows the optimization of system design for new scanners, the study of factors affecting image quality, the validation of correction methodologies for effects such as scatter, attenuation and partial volume, for improved image quantification, as well as the development and testing of new image reconstruction algorithms.…”

“…An important aspect of simulation is the possibility of having a realistic model (phantom) of the subject's anatomy and physiological functions from which imaging data can be generated using accurate models of the imaging process (Ljungberg, 2004;Zaidi, 2006;Zubal, 1998;Poston et al, 2002;Peter et al, 2000). Conceptually, the purpose of a physical or computerized phantom is to represent an organ or body region of interest, to allow modeling the biodistribution of a particular radiotracer and the chemical composition of the scattering medium, which absorbs and scatters the emitted radiation in a manner similar to biological tissues.…”