| Question 1: The remaining fundamental limitation to HRCT resolution is: |
| Reference: | AM Hernandez, P Wu, M Mahesh, JH Siewerdsen, and JM Boone, Location and direction dependence in the 3D MTF for a high-resolution CT system, Medical Physics 48, in press |
| Choice A: | Table vibration |
| Choice B: | Spectral hardening |
| Choice C: | Gantry rotation |
| Choice D: | Laser light positioning |
| Question 2: Clinical applications which benefit from HRCT are most likely to be: |
| Reference: | The Essential Physics of Medical Imaging, JT Bushberg, JA Seibert, EM Leidholdt, JM Boone, Fourth Edition, Wolters Kluwer, Philadelphia (2020) |
| Choice A: | In older patients with weaker bones |
| Choice B: | Where anatomical dimensions are from 50% to 100% of the Nyquist frequency |
| Choice C: | Where the intrinsic subject contrast levels are very low |
| Choice D: | Where contrast injection is not feasible |
| Question 3: The radial component of the MTF exhibits variation across axial CT field-of-view. This variation is especially pronounced in high-resolution imaging modes. Which of the following is true: |
| Reference: | AM Hernandez, P Wu, M Mahesh, JH Siewerdsen, and JM Boone, Location and direction dependence in the 3D MTF for a high-resolution CT system, Medical Physics 48, in press |
| Choice A: | The variations are the results of focal spot elongation for peripheral detector elements |
| Choice B: | At a fixed distance from the isocenter, a focal spot that is longer in the z direction (anode-cathode direction) will result in worse radial MTF |
| Choice C: | For a fixed focal spot size, the radial MTF improves closer to the isocenter |
| Choice D: | All the above |
| Question 4: Which dose metric provides the most information about the radiation exposure to the patient? |
| Reference: | Damilakis J. CT Dosimetry. Investigative Radiology. 2021;56(1):62–68. doi: 10.1097/RLI.0000000000000727. |
| Choice A: | CTDI |
| Choice B: | SSDE |
| Choice C: | Organ Dose |
| Choice D: | Dose Length Product |
| Question 5: Which CT acquisition factor requires more information from manufacturers in order to be accurately modeled in CT dosimetry tools? |
| Reference: | Damilakis J. CT Dosimetry. Investigative Radiology. 2021;56(1):62–68. doi: 10.1097/RLI.0000000000000727. |
| Choice A: | Tube current modulation |
| Choice B: | Gantry rotation time |
| Choice C: | Helical pitch |
| Choice D: | Beam Collimation |
| Question 6: Which of the following statements best describe the primary difference between Linear Boltzmann Transport Equation (LBTE) dose estimation and Monte Carlo dose estimation? |
| Reference: | A. S. Wang, A. Maslowski, T. Wareing, J. Star-Lack, T. G. Schmidt, “A fast, linear Boltzmann transport equation solver for computed tomography dose calculation (Acuros CTD),” Medical Physics, 46 (2), pp. 925–933, 2019. |
| Choice A: | LBTE cannot simulate scatter orders greater than first order |
| Choice B: | LBTE uses a deterministic model, whereas Monte Carlo is stochastic |
| Choice C: | Accelerated GPU implementation is possible for LBTE, but not for Monte Carlo |
| Choice D: | Monte Carlo simulations can account for heel effect, whereas LBTE cannot |
| Question 7: In emphysema susceptible smokers, the following observations suggest a failure to block hypoxic pulmonary hypertension associated with regional smoking induced inflammation. |
| Reference: | Iyer KS, Newell JD Jr, Jin D, Fuld MK, Saha PK, Hansdottir S, Hoffman EA. Quantitative Dual-Energy Computed Tomography Supports a Vascular Etiology of Smoking-induced Inflammatory Lung Disease. Am J Respir Crit Care Med. 2016 Mar 15;193(6):652-61. |
| Choice A: | Increased regional PBV-CV as assessed by DECT (PBV-CV: perfused blood volume-coefficient of variation) |
| Choice B: | Enlarged segmental pulmonary arterial cross sectional areas standardized to the associated segmental airway area. |
| Choice C: | Greater increase in regional PBV-CV in the non-dependent lung compared to the dependent lung. |
| Choice D: | All of the above. |
| Question 8: When assessing regional ventilation by imaging a Xenon Gas mixture via DECT, distribution will most closely represent a distribution of inspired room air if: |
| Reference: | Fuld MK, Halaweish AF, Newell JD Jr, Krauss B, Hoffman EA. Optimization of dual-energy xenon-computed tomography for quantitative assessment of regional pulmonary ventilation. Invest Radiol. 2013 Sep;48(9):629-37. |
| Choice A: | A patient is coached to inspire slowly. |
| Choice B: | A patient is imaged in the prone body posture |
| Choice C: | A xenon-oxygen gas mixture includes helium |
| Choice D: | None of the above |
| Question 9: Which of the following is true for the PWLS-ULTRA method that was proposed for low-dose CT reconstruction? |
| Reference: | X. Zheng, S. Ravishankar, Y. Long and J. A. Fessler, "PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction," in IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1498-1510, June 2018. |
| Choice A: | It uses the wavelet transform to denoise noisy filtered back-projection reconstructions. |
| Choice B: | It performs iterative reconstruction by adaptively clustering image patches into groups depending on which learned filters/transforms best sparsify the patches. |
| Choice C: | It enforces the reconstructed image to be spatially similar to a given reference image. |
| Choice D: | It is not an iterative algorithm. |
| Question 10: Which category of deep learning methods typically incorporates forward models or data consistency terms in the neural network architecture/training and inference? |
| Reference: | S. Ravishankar, J. C. Ye and J. A. Fessler, "Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning," in Proceedings of the IEEE, vol. 108, no. 1, pp. 86-109, Jan. 2020. |
| Choice A: | Image-domain learning |
| Choice B: | AUTOMAP |
| Choice C: | Hybrid-domain learning |
| Choice D: | Sensor-domain learning |
