| Question 1: For the Deep Scatter Estimation (DSE), which Statement(s) are True? |
| Reference: | J. Maier, E. Eulig, T. Vöth, M. Knaup, J. Kuntz, S. Sawall, and M. Kachelrieß. Real‐time scatter estimation for medical CT using the deep scatter estimation (DSE): Method and robustness analysis with respect to different anatomies, dose levels, tube voltages and data truncation. Med. Phys. 46(1):238-249, 2019.
J. Maier, S. Sawall, M. Knaup, and M. Kachelrieß. Deep scatter estimation (DSE): Accurate real-time scatter estimation for X-ray CT using a deep convolutional neural network. Journal of Nondestructive Evaluation 37:57, 2018. |
| Choice A: | DSE needs to see projections from many directions in its input. |
| Choice B: | DSE fails on truncated projections because those are not sufficient to reconstruct the whole patient. |
| Choice C: | DSE can estimate scatter from a single x-ray image. |
| Choice D: | DSE only works if being trained with Monte-Carlo scatter estimates. Other methods to estimate scatter, such as measurement-based ones, for example, cannot be used as a training label |
| Question 2: The following are differences between Demons and VoxelMorph. Select the false answer. |
| Reference: | Thirion, “Image matching as a diffusion process: An analogy with Maxwell’s demons,” Medical Image Analysis 2(3), 243–260, 1998
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. and Dalca, A. V., “VoxelMorph: A Learning Framework for Deformable Medical Image Registration,” IEEE Trans. Med. Imaging 38(8), 1788–1800, 2019 |
| Choice A: | Demons is an iterative registration algorithm performing individually on each patient, while VoxelMorph is a multiparametric function providing deformation vector fields whose parameters were fit to provide the corect deformation vectors for a set of training data samples. |
| Choice B: | Demons is performing an affine registration while VoxelMorph can also handle deformable registration tasks. |
| Choice C: | Demons is 20 years older than VoxelMorph. |
| Choice D: | Due to its non-iterative nature VoxelMorph is potentially performing faster than Demons. |
| Question 3: Approaches for implementing dual energy CBCT include: |
| Reference: | L. Shi et al., “Characterization and potential applications of a dual‐layer flat‐panel detector,” Med. Phys., vol. 47, no. 8, pp. 3332–3343, 2020. |
| Choice A: | kV switching |
| Choice B: | Dual layer detector |
| Choice C: | Photon counting detector |
| Choice D: | Spatial-spectral filter |
| Choice E: | All of the above |
| Question 4: Material decomposition enables material-specific quantification but generally increases image noise: |
| Reference: | C. H. McCollough, S. Leng, L. Yu, and J. G. Fletcher, “Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications,” Radiology, vol. 276, no. 3, pp. 637–653, 2015, doi: 10.1148/radiol.2015142631 |
| Choice A: | True |
| Choice B: | False |
| Question 5: Which of the following will decrease dual energy performance |
| Reference: | A. S. Wang, “Single-shot quantitative x-ray imaging from simultaneous scatter and dual energy measurements: a simulation study,” in SPIE Medical Imaging 2021: Physics of Medical Imaging, Feb. 2021, p. 77, doi: 10.1117/12.2580728 |
| Choice A: | Optimal spectral separation |
| Choice B: | Uncorrected patient scatter |
| Choice C: | Higher detector efficiency |
| Choice D: | Lower electronic noise |
| Question 6: Regarding motion in cone-beam CT of the liver, which statement is false? |
| Reference: | Becker et al. “Evaluation of a Motion Correction Algorithm for C-Arm Computed Tomography Acquired During Transarterial Chemoembolization,” CardioVascular and Interventional Radiology, volume 44, (2021), pp: 610–618 |
| Choice A: | Is primarily controlled with patient immobilization and performing the acquisition under breath-hold conditions. |
| Choice B: | Respiratory (breathing) motion is the only source of motion in abdominal cone-beam CT. |
| Choice C: | Results in image blurring, shape distortion, and streaks artifacts. |
| Question 7: Autofocus approaches for motion compensation... |
| Reference: | Capostagno et al. “Deformable motion compensation for interventional cone-beam CT,” Physics in Medicine and Biology, 66, 055010, (2021)
Sisniega et al. “Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion,” Physics in Medicine and Biology, 62, 3712, (2017) |
| Choice A: | Estimate motion by maximizing some metric of sharpness and/or image quality in the reconstructed image |
| Choice B: | Bin the acquired projections in different motion phases according to a surrogate signal obtained with an external sensor (e.g., ECG) |
| Choice C: | Estimate periodic motion by registering 2D projection data to a previously acquired, motion-free 3D image |
| Question 8: The concept of Adaptive Radiotherapy includes which of the following: |
| Reference: | Glide-Hurst et al. Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State-of-the-ART Review From NRG Oncology, International Journal of Radiation Oncology*Biology*Physics, Volume 109, Issue 4, 2021, Pages 1054-1075 |
| Choice A: | Offline modification of treatment beams between treatment fractions. |
| Choice B: | Online modification of treatment beams immediately before a treatment fraction. |
| Choice C: | Real-time modification of treatment beams during a treatment fraction. |
| Choice D: | All of the above |
| Question 9: All of the following regarding the iterative CBCT reconstruction algorithm, compared to conventional FDK reconstruction, are correct EXCEPT: |
| Reference: | Gardner SJ, et al. Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation. Adv Radiat Oncol. 2019 Jan 10;4(2):390-400 |
| Choice A: | Improve image uniformity |
| Choice B: | Improve HU value accuracy |
| Choice C: | Reduce streak artifacts |
| Choice D: | Reconstruct with limited number of projections |
| Choice E: | Increase imaging dose to patients |
| Question 10: All of the following regarding the synthetic CT generated from a supervised trained convolutional neural network, compared to the original low-dose fast scan CBCT in the head and neck region are correct, EXCEPT: |
| Reference: | Yuan N, et al. Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy. Phys Med Biol. 2020 Jan 27; 65 (3):035003 |
| Choice A: | The synthetic CT shows improved signal-to-noise ratio (SNR) and structural similarity (SSIM) |
| Choice B: | The synthetic CT shows enhanced visualization for small but critical structures, i.e. optical nerves |
| Choice C: | The synthetic CT shows reduced image noise and high Z streaky artifacts, i.e. in the dental region |
| Choice D: | The synthetic CT shows equivalent HU accuracy when compared to the planning CT |
