As new imaging techniques and diagnostic methods emerge in response to disease, a major challenge is how to evaluate which technique is best in terms of patient diagnosis and treatment. As imaging technologies become progressively more advanced, they also become exceedingly complex and any optimization becomes a daunting challenge. Essentially there is too much heterogeneity in patient size and pathology and too many technical variables in modern imaging systems to optimize them to the clinical task. It is frequently difficult both ethically and practically to test every combination of parameters on patients under clinical conditions. This is especially true for current CT systems given the relatively high radiation dose. The use of physical phantoms for optimization tasks is also limited in that it would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to optimization is through realistic computer simulation. As a result, computer simulation methods are finding an increasingly important role in medical imaging research for characterizing, evaluating, and optimizing medical imaging systems. They have become an important and indispensable complement to theoretical derivations, experimental methods, and clinical studies in medical imaging research and development.

Computer simulation involves computer generated phantoms, models of the imaging process, and fast computational methods, Fig. 1. Computer phantoms provide a model of the subject’s anatomy and physiology. Given a model of the physics of the imaging process, acquired data of a computer phantom can be generated using the computational methods. A major advantage to using computer-generated phantoms in simulation studies is that the exact anatomy and physiological functions of the phantom are known, thus providing a gold standard from which to evaluate and improve medical imaging devices and image processing and reconstruction techniques. Other advantages are that computer phantoms are always willing participants and can be altered easily to model different anatomies and medical situations providing a large population of subjects from which to perform research.

A vital aspect of simulation is to have a realistic phantom or model of the subject's anatomy. Without this, the results of the simulation may not be indicative of what would occur in actual patients or animal subjects and would, therefore, have limited practical value. Dr. Paul Segars has been leading the development of two realistic digital phantoms for use in medical imaging research, the 4D NCAT and 4D MOBY phantoms. The 4D NCAT phantom was developed to model the human anatomy and physiology for imaging studies, Fig. 2. The 4D MOBY phantom was developed to model the mouse anatomy and physiology for small animal studies, Fig. 4. Both phantoms are based on state-of-the-art computer graphics techniques and when combined with accurate models for the imaging process are capable of providing a wealth of realistic imaging data from subjects of various anatomies, Figs. 3 and 5. With this ability, the phantoms have enormous potential to study the effects of anatomical, physiological, physical, and instrumentational factors on medical and small animal imaging and to research new instrumentation, image acquisition strategies, image processing and reconstruction methods and image visualization and interpretation techniques.