| Question 1: What is the purpose of tube current modulation? |
| Reference: | • J. R. Haaga, F. Miraldi, W. McIntyre, J. P. LiPuma, P. J. Bryan, and E. Wiesen, “The effect of mAs variation upon computed tomography image quality as evaluated in vivo and in vitro studies”, Radiology 138, 449– 454 (1981).
• M. Gies, W. A. Kalender, H. Wolf, C. Suess, M. T. Madsen, “Dose reduction in CT by ana-tomically adapted tube current modulation. I. Simulation studies”, Medical Physics 26(11), 2235–2247 (1999).
• W. A. Kalender, H. Wolf, C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements”, Medical Physics 26(11), 2248–2253 (1999). |
| Choice A: | To reduce x-ray dose while maintaining image quality |
| Choice B: | To achieve a more uniform image noise distribution |
| Choice C: | Both of the above. |
| Question 2: What does the tube current modulation curve depend on? |
| Reference: | • M. Gies, W. A. Kalender, H. Wolf, C. Suess, M. T. Madsen, “Dose reduction in CT by ana-tomically adapted tube current modulation. I. Simulation studies”, Medical Physics 26(11), 2235–2247 (1999).
• C. H. McCollough, M. R. Bruesewitz, J. M. Kofler, “CT Dose Reduction and Dose Manage-ment Tools: Overview of Available Options”, RadioGraphics, 26(2), 503–512 (2006).
• M. Söderberg, M. Gunnarsson, “Automatic exposure control in computed tomography – an evaluation of systems from different manufacturers”, Acta Radiologica, 51(6), 625–634 (2010 |
| Choice A: | The size of the patient |
| Choice B: | The shape of the patient |
| Choice C: | The tube voltage |
| Choice D: | All of the above |
| Question 3: How does the deep dose estimation speed up patient-specific dose calculations? |
| Reference: | • J. Maier, E. Eulig, S. Dorn, S. Sawall, and M. Kachelrieß, “Real-Time Patient-Specific CT Dose Estimation using a Deep Convolutional Neural Network”, Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (2018). |
| Choice A: | By using a deep neural network that is trained to reproduce Monte Carlo simulations |
| Choice B: | By applying patient-specific conversion factors to the CTDIvol |
| Choice C: | By using look-up tables to phantom dose calculations |
| Question 4: The use of a Sn (Tin) filter in CT systems will result in: |
| Reference: | • Primak AN, Giraldo JC, Eusemann CD, et al. Dual-source dual-energy CT with additional tin filtration: Dose and image quality evaluation in phantoms and in vivo. AJR Am J Roentgenol. 2010;195(5):1164-1174
• Principles and Applications of Multi-energy CT Report of AAPM Task Group 291. Med Phys. 2020 Mar 25. doi: 10.1002/mp.14157. |
| Choice A: | Removal of high energy photons, leading to a decrease in the X-ray beam’s effective en-ergy |
| Choice B: | Removal of low energy photons, leading to an increase in the X-ray beam’s effective en-ergy |
| Choice C: | Removal of photons of all energies, resulting in no change to the beam’s effective ener-gy |
| Question 5: The use of a Sn (Tin) filter in low dose lung cancer screening protocols results in: |
| Reference: | • AAPM Lung Cancer Screening CT Protocols Version 5.1 (13 September 2019), https://www.aapm.org/pubs/CTProtocols/documents/LungCancerScreeningCT.pdf.
• Fujii K, McMillan K, Bostani M, Cagnon C, McNitt-Gray M. Patient Size-Specific Analysis of Dose Indexes From CT Lung Cancer Screening. AJR Am J Roentgenol. 2017 Jan;208(1):144-149. doi: 10.2214/AJR.16.16082 |
| Choice A: | A substantial increase in radiation dose because an increased tube current is used |
| Choice B: | No change in radiation dose because of the change in X-ray spectra |
| Choice C: | A substantial decrease in radiation dose even though an increased tube current setting is used |
| Question 6: Which of the following statement is correct? |
| Reference: | Barrett, Harrison H., and Kyle J. Myers. Foundations of image science. John Wiley & Sons, 2013. |
| Choice A: | The Structure Similarity Index is an objective metric for task-based assessment of image quality |
| Choice B: | The Mean Square Error is an objective metric for task-based assessment of image quality |
| Choice C: | The Contrast-to-Noise ratio is an objective metric for task-based assessment of image quality |
| Choice D: | None of the above |
| Choice E: | All of the above |
| Question 7: What is the motivation for dynamic fluence field modulation? |
| Reference: | • Timothy P. Szczykutowicz Charles A. Mistretta, “Design of a digital beam attenuation sys-tem for computed tomography: Part I. System design and simulation framework”, Medi-cal Physics 40(2), 2013.
• T. Toth, Z. Ge, and M. Daly, “The influence of patient centering on ct dose and image noise,” Med. Phys. 34, 3093–3101 (2007).
• D. Heuscher and F. Noo, “CT dose reduction using dynamic collimation,” in Proceedings of the 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Valencia ( IEEE, New York, New York, 2011), pp. 3470–3473.
• Grace J Gang, Andrew Mao, Wenying Wang, Jeffrey H Siewerdsen, Aswin Mathews, Sato-mi Kawamoto, Reuven Levinson, J Webster Stayman, “Dynamic fluence field modulation in computed tomography using multiple aperture devices”, Physics in Medicine & Biolo-gy, 64(10) |
| Choice A: | Greater dose reduction |
| Choice B: | Can potentially accommodate mis centered patient |
| Choice C: | Can achieve volume of interest imaging |
| Choice D: | All of the above |
| Question 8: What does the ideal fluence modulation profile depend on? |
| Reference: | • Michael D. Harpen, A simple theorem relating noise and patient dose in computed to-mography, Medical Physics, 26(11), 1999.
• S. Bartolac, S. Graham, J. Siewerdsen, and D. Jaffray, “Fluence field optimization for noise and dose objectives in CT,” Med. Phys. 38, S2–S17 (2011).
• Scott S. Hsieh and Norbert J. Pelc, "Control algorithms for dynamic attenuators“, Medi-cal Physics, 41(6), 2014.
• Grace J Gang, Jeffrey H Siewerdsen, J Webster Stayman, "Task-driven optimization of fluence field and regularization for model-based iterative reconstruction in computed tomography”, IEEE transactions on medical imaging, 36(12), 2017. |
| Choice A: | Patient habitus |
| Choice B: | Reconstruction algorithm |
| Choice C: | Image quality objective |
| Choice D: | All of the above |
| Question 9: Which one of the following is NOT a necessary design goal for dynamic beam
filters? |
| Reference: | T. Toth, Z. Ge, and M. Daly, “The influence of patient centering on ct dose and image noise,” Med. Phys. 34, 3093–3101 (2007).
· Andrew Mao, Grace J. Gang, William Shyr, Reuven Levinson, Jeffrey H. Siewerdsen, Satomi Kawamoto, and J. Webster Stayman. "Dynamic fluence field modulation for miscentered patients in computed tomography." Journal of Medical Imaging, 5(4) 2018.
· Shunhavanich, P., Bennett, N. R., Hsieh, S. S., & Pelc, N. J. (June 2019) Implementation of a piecewise-linear dynamic attenuator. Journal of Medical Imaging, 6(2), 023502. |
| Choice A: | Compact. |
| Choice B: | Fast actuation speed. |
| Choice C: | Can only achieve symmetric beam profiles. |
| Choice D: | Can achieve a wide range of beam profiles. |
| Question 10: Which of the following statement is correct? |
| Reference: | Wunderlich, Adam, and Frédéric Noo. "A nonparametric procedure for comparing the areas under correlated LROC curves." IEEE transactions on medical imaging 31.11 (2012): 2050-2061. |
| Choice A: | The area under the ROC curve is the probability of correct decision. |
| Choice B: | The area under the LROC curve is the probability of correct decision. |
| Choice C: | The ROC curve is suboptimal for detection tasks. |
| Choice D: | None of the above. |
| Choice E: | All of the above. |
