| Question 1: FISTA minimizes objective functions of the form f(x) + g(x), where f and g are convex and additionally _____________.
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| Reference: | “Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems,” A. Beck and M. Teboulle, IEEE Transactions on Image Processing, 2009 |
| Choice A: | f and g are both differentiable. |
| Choice B: | f has an easy proximal operator and g is unrestricted. |
| Choice C: | f and g both have easy proximal operators. |
| Choice D: | f is differentiable and g has an easy proximal operator. |
| Question 2: The group sparsity BOO formulation contains dose fidelity terms, a group sparsity term that encourages most candidate beams to be inactive, and an indicator function that enforces nonnegativity constraints. Which of the following are correct: |
| Reference: | “4pi non-coplanar IMRT beam angle selection by convex optimization with group sparsity penalty,” D. O’Connor et al, AAPM 2016
“Beam orientation optimization for intensity modulated radiation therapy using adaptive L2,1-minimization,” X. Jia et al, Physics in Medicine and Biology, 2011
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| Choice A: | The group sparsity term is included in f. |
| Choice B: | The group sparsity term is included in g. |
| Choice C: | The indicator function is included in f. |
| Choice D: | The indicator function is included in g. |
| Choice E: | Both b and d. |
| Question 3: Volumetric Modulated Arc Therapy (VMAT) creates a single arc by optimizing many closely angularly-spaced beams, and using a version of simulated annealing DAO that:
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| Reference: | Otto, Karl. "Volumetric modulated arc therapy: IMRT in a single gantry arc." Medical physics 35.1 (2008): 310-317. |
| Choice A: | Deterministically solves for the deliverable apertures. |
| Choice B: | Creates multiple deliverable apertures for each beam. |
| Choice C: | Produces fluence maps that then need to be MLC segmented. |
| Choice D: | Solves for exactly one deliverable aperture for each beam. |
| Choice E: | None of the above. |
| Question 4: The multiphase piecewise-constant Mumford-Shah function:
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| Reference: | Chan, Tony F., Selim Esedoglu, and Mila Nikolova. "Algorithms for finding global minimizers of image segmentation and denoising models." SIAM journal on applied mathematics 66.5 (2006): 1632-1648.
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| Choice A: | Can use a labeling array/function to segment the fluence map. |
| Choice B: | Solves for many overlapping segments. |
| Choice C: | Solves for the segments sequentially, one at a time. |
| Choice D: | All of the above. |
| Choice E: | None of the above. |
| Question 5: Which statement about the PDHG algorithm is FALSE?
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| Reference: | Chambolle, Antonin, and Thomas Pock. "A first-order primal-dual algorithm for convex problems with applications to imaging." Journal of Mathematical Imaging and Vision 40.1 (2011): 120-145.
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| Choice A: | PDHG updates both a primal variable and a dual variable at each iteration. |
| Choice B: | PDHG is a first-order algorithm. |
| Choice C: | PDHG efficiently solves a system of linear equations at each iteration. |
| Choice D: | PDHG relies on evaluation of the proximal mapping, or “prox operator”. |
| Choice E: | PDHG solves a problem of the form: minimize F(Kx) + G(x). |
| Question 6: Which of the following is correct?
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| Reference: | J. Kim, W. K. Jeong, and B. Nam, “Exploiting massive parallelism for indexing multi-dimensional datasets on the GPU,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 8, pp. 2258–2271, Aug 2015
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| Choice A: | Irrespective of data size, GPU memory is not a limitation in typical radiotherapy inverse planning implementations. |
| Choice B: | Adding more GPU cards to processing platform will always increase the processing speed. |
| Choice C: | There is an optimal number of GPUs for a computational problem which only depends on the hardware and GPU card specification. |
| Choice D: | There is an optimal number of GPUs for a computational problem which depends on the data size as well as the hardware specifications. |
| Question 7: Which one is not a typical efficient technique in data management for computational parallelization?
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| Reference: | A. Modiri; X. Gu; A. Hagan; A. Sawant, "Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization," IEEE Transactions on Biomedical Engineering, vol.PP, no.99, pp.1-1, June 2016. |
| Choice A: | Downsampling. |
| Choice B: | Sparsification. |
| Choice C: | Detecting one-time processes to be performed offline. |
| Choice D: | Increasing the size of data to be shared or transferred between CPU cores or GPUs. |
| Question 8: Introducing hard dose constraints in an inverse planning process, where a metaheuristic global optimization technique is used, ____________. |
| Reference: | K. Deb, “Multi-Objective Optimization using Evolutionary Algorithms”, WILEY, 2001.
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| Choice A: | Enhances the optimization speed in finding the global solution. |
| Choice B: | Guarantees finding the global solution. |
| Choice C: | Increases the chances of getting trapped in a local solution. |
| Choice D: | Does not change the optimization process, compared to an unconstrained optimization. |
| Question 9: Why does Monte Carlo dose calculation have a bigger impact in proton therapy compared to photon therapy?
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| Reference: | Paganetti H “Range uncertainties in proton therapy and the role of Monte Carlo simulations.” Physics in Medicine and Biology 2012 57: R99-R117
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| Choice A: | Because proton therapy uses more fields. |
| Choice B: | Because proton therapy uses fewer fields and because of range uncertainties. |
| Choice C: | Because proton therapy because cone-beam CT is not standard in proton therapy. |
| Choice D: | Because proton Monte Carlo codes are more accurate than photon Monte Carlo codes. |
| Question 10: What is biological optimization in proton therapy? |
| Reference: | Unkelbach J; Botas P; Giantsoudi D; Gorissen B and Paganetti H, “Reoptimization of intensity-modulated proton therapy plans based on linear energy transfer.” International Journal of Radiation Oncology, Biology and Physics 2016 96; 1097-1106
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| Choice A: | Biological optimization tries to reduce dose-averaged LET in the tumor. |
| Choice B: | Biological optimization tries to increase RBE in organs at risk. |
| Choice C: | Biological optimization tries to move elevated RBE values away from critical structures. |
| Choice D: | Biological optimization tries to reduce the number of individual pencils in intensity-modulated proton therapy. |
