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Purpose: Monte Carlo simulation is a frequently used technique for assessing patient dose in CT. The accuracy of a Monte Carlo program is often validated using the standard CT dose index (CTDI) phantoms by comparing simulated and measured CTDI100. To achieve good agreement, many input parameters in the simulation (e.g., energy spectrum and effective beam width) need to be determined. However, not all the parameters have equal importance. Our aim was to assess the relative importance of the various factors that influence the accuracy of simulated CTDI100. Methods: A Monte Carlo program previously validated for a clinical CT system was used to simulate CTDI100. For the standard CTDI phantoms (32 and 16 cm in diameter), CTDI100 values from central and four peripheral locations at 70 and 120 kVp were first simulated using a set of reference input parameter values (treated as the truth). To emulate the situation in which the input parameter values used by the researcher may deviate from the truth, additional simulations were performed in which intentional errors were introduced into the input parameters, the effects of which on simulated CTDI100 were analyzed. Results: At 38.4‐mm collimation, errors in effective beam width up to 5.0 mm showed negligible effects on simulated CTDI100 (<1.0%). Likewise, errors in acrylic density of up to 0.01 g/cm3 resulted in small CTDI100 errors (<2.5%). In contrast, errors in spectral HVL produced more significant effects: slight deviations (±0.2 mm Al) produced errors up to 4.4%, whereas more extreme deviations (±1.4 mm Al) produced errors as high as 25.9%. Lastly, ignoring the CT table introduced errors up to 13.9%. Conclusion: Monte Carlo simulated CTDI100 is insensitive to errors in effective beam width and acrylic density. However, they are sensitive to errors in spectral HVL. To obtain accurate results, the CT table should not be ignored. This work was supported by a Faculty Research and Development Award from Cleveland State University.

For practicing medical physicists, CT image quality assessment is, in general, limited to the ones required by ACR and based on the axial images, such as CT number uniformity, image noise, contrast‐to‐noise ratio (CNR), and high contrast resolution. However, with more advanced technologies introduced by CT vendors, just assessing these basic image quality metrics may not be enough. Noise power spectrum (NPS), characterizing the signal noise component at each spatial frequency, is a powerful tool to predict the CT performance related to lesion detectability and noise texture. The quality of coronal and sagittal reformatted images is, in a large part, determined by the slice profile. How to accurately assess the slice profile is important for clinical medical physicists but not well recognized. Automatic tube current modulation (ATCM) has been adopted by major CT manufacturers, to maintain image quality across the scan range. The way how ATMC works varies significantly among vendors. A “standard” method is needed to evaluate the performance of this critical dose‐saving and image quality improvement technique. As dual energy CT becomes more readily available for routine clinical use, the need of image quality assessment metrics unique to the dual energy CT mode is becoming urgent. The session will facilitate the discussion of NPS and its application in the evaluation of FBP and iterative reconstruction. This session will also present the methods to measure the slice profile and characterize the performance of ATCM. The last part of this session will focus on the physics of CT dual energy mode, as well as unique image quality metrics to evaluate the performance of the dual energy mode. Learning Objectives: Be familiar with procedures on how to correctly compute CT noise power spectrum Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction Understand CT slice profile and how to accurately measure it Be familiar with ATCM from major CT vendors and how to evaluate its performance Understand different hardware approaches for dual energy CT Be familiar with image quality metrics relevant to dual energy mode Schmidt: Employee of Siemens Healthcare Fan: Employee of GE Healthcare

Purpose: The purpose of this investigation was to quantify percent depth dose (PDD) curves for fluoroscopic x‐ray beam qualities incorporating added copper filtration. Methods: A PTW (Freiburg, Germany) MP3 water tank was used with a Standard Imaging (Middleton, WI) Exradin Model 11 Spokas Chamber to measure PDD curves for 60, 80, 100 and 120 kVp x‐ray beams with copper filtration ranging from 0.0–0.9 mm at 22cm and 42cm fields of view from 0 to 150 mm of water. A free‐in‐air monitor chamber was used to normalize the water tank data to fluctuations in output from the fluoroscope. The measurements were acquired on a Siemens (Erlangen, Germany) Artis ZeeGo fluoroscope. The fluoroscope was inverted from the typical orientation providing an x‐ray beam originating from above the water tank. The water tank was positioned so that the water level was located at 60cm from the focal spot; which also represents the focal spot to interventional reference plane distance for that fluoroscope. Results: PDDs for 60, 80, 100, and 120 kVp with 0 mm of copper filtration compared well to previously published data by Fetterly et al. [Med Phys, 28, 205 (2001)] for those beam qualities given differences in fluoroscopes, geometric orientation, type of ionization chamber, and the water tank used for data collection. PDDs for 60, 80, 100, and 120 kVp with copper filtration were obtained and are presented, which have not been previously investigated and published. Conclusion: The equipment and processes used to acquire the reported data were sound and compared well with previously published data for PDDs without copper filtration. PDD data for the fluoroscopic x‐ray beams incorporating copper filtration can be used as reference data for estimating organ or soft tissue dose at depth involving similar beam qualities or for comparison with mathematical models.