The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Purpose To estimate the radiation dose as a result of contrast medium administration in a typical abdominal computed tomographic (CT) examination across a library of contrast material-enhanced computational patient models. Materials and Methods In part II of this study, first, the technique described in part I of this study was applied to enhance the extended cardiac-torso models with patient-specific iodine-time profiles reflecting the administration of contrast material. Second, the patient models were deployed to assess the patient-specific organ dose as a function of time in a typical abdominal CT examination using Monte Carlo simulation. In this hypothesis-generating study, organ dose refers to the total energy deposited in the unit mass of the tissue inclusive of iodine. Third, a study was performed as a strategy to anticipate the biologically relevant dose (absorbed dose to tissue) in highly perfused organs such as the liver and kidney. The time-varying organ-dose increment values relative to those for unenhanced CT examinations were reported. Results The results from the patient models subjected to the injection protocol indicated up to a total 53%, 30%, 35%, 54%, 27%, 18%, 17%, and 24% increase in radiation dose delivered to the heart, spleen, liver, kidneys, stomach, colon, small intestine, and pancreas, respectively. The biologically relevant dose increase with respect to the dose at an unenhanced CT examination was in the range of 0%-18% increase for the liver and 27% for the kidney across 58 patient models. Conclusion The administration of contrast medium increases the total radiation dose. However, radiation dose, while relevant to be included in estimating the risk associated with contrast-enhanced CT, may still not fully characterize the total biologic effects. Therefore, given the fact that many CT diagnostic decisions would be impossible without the use of iodine, this study suggests the need to consider the effect of iodinated contrast material on the organ doses to patients undergoing CT studies when designing CT protocols. RSNA, 2017 Online supplemental material is available for this article.

The purpose of this study was to develop a CT simulation platform that is 1) compatible with voxel-based computational phantoms, 2) capable of modeling the geometry and physics of commercial CT scanners, and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. DukeSim includes detailed models for the detector quantum efficiency, quantum and electronic noise, detector crosstalk, subsampling of the detector and focal spot areas, focal spot wobbling, and the bowtie filter. DukeSim was accelerated using GPU computing. The platform was validated using physical and computational versions of a phantom (Mercury phantom). Clinical and simulated CT scans of the phantom were acquired at multiple dose levels using a commercial CT