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Several component collections are available in many domains: biology, chemistry, material sciences, modelling and simulation, imaging, and so forth.
Respiratory motion can be managed in one of the following two ways: motion suppression for static simulation imaging and treatment delivery, or motion compensation for dynamic simulation imaging and treatment delivery.
Respiratory motion suppression during treatment simulation imaging and treatment delivery is achieved either through breath hold or forced shallow breathing.
Tumor and nodal motion is accounted for in radiotherapy planning and delivery with uniform spatial margins and daily image guidance, but in the future could be suppressed or compensated for throughout treatment simulation imaging, planning, and delivery.
The primary methods of motion compensation during treatment simulation imaging and treatment delivery exist: gating during a defined window of the respiratory cycle or tracking motion through correlation to the entire respiratory cycle.
It is important to quantify residual errors that ensue from each motion management strategy, which would then be propagated through the simulation imaging, treatment planning, and treatment delivery processes.
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This paper reviews current progress and challenges in the simulation of imaging systems with a focus on Monte Carlo approaches to X-ray detector modeling, acceleration approaches, and validation strategies.
Theoretical simulations of imaging probe localization were carried out for three agents with specificity for cancer cells, stromal host response, or vascular perfusion.
We next examined the practical implications of our simulations by imaging red cells at a variety of set points.
An approach to overcome this is to iteratively compare experimental images with simulations [5, 6]; imaging parameters and model structure of the sample are refined until the simulated and experimental image match.
Fig. 4 Simulation results of imaging algorithm.
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