| Question 1: What are the advantages of deep learning reconstruction? |
| Reference: | Lønning, K., Putzky, P. & Welling, M. Recurrent Inference Machines for Accelerated MRI Reconstruction. Int. Conf. Med. Imaging with Deep Learn. 1–11 (2018). |
| Choice A: | Speed of reconstruction compared to analytic methods |
| Choice B: | Possibility for reconstruction of (very) sparse data |
| Choice C: | It is certain the precise details are preserved |
| Question 2: Which of the following are deep learning models which can directly reconstruct MRI based on the k-space data? |
| Reference: | Lønning, K., Putzky, P. & Welling, M. Recurrent Inference Machines for Accelerated MRI Reconstruction. Int. Conf. Med. Imaging with Deep Learn. 1–11 (2018).
Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R. & Rosen, M. S. Image reconstruction by domain- |
| Choice A: | Automap |
| Choice B: | Recurrent inference machines |
| Choice C: | U-nets |
| Choice D: | Sensitivity encoding (SENSE) |
| Choice E: | A and B |
| Question 3: What is the advantage of deep learning based DBT reconstruction |
| Reference: | Moriakov, N. et al. Deep learning framework for digital breast tomosynthesis reconstruction. in SPIE Medical Imaging (2019). |
| Choice A: | Superior contrast |
| Choice B: | Better in-plane resolution |
| Choice C: | Better coronal resolution |
| Question 4: Model-enforced post-processing can be advised if, (multiple possible) |
| Reference: | J. Adler and O. Öktem. "Solving ill-posed inverse problems using iterative deep neural networks." Inverse Problems 33.12 (2017): 124007.
A. Hauptmann, et al. "Realâ€time cardiovascular MR with spatioâ€temporal artifact suppression using deep learning� |
| Choice A: | large amounts of training data are available. |
| Choice B: | reconstruction artefacts are uncorrelated/noiselike. |
| Choice C: | quantitative values are important. |
| Choice D: | short computation times are required. |
| Choice E: | superior reconstruction quality is desired. |
| Choice F: | A, B and D. |
| Question 5: What is the major limitation in high-dimensional iterative reconstructions? |
| Reference: | A. Hauptmann, et al. "Model-based learning for accelerated, limited-view 3-d photoacoustic tomography." IEEE transactions on medical imaging 37.6 (2018): 1382-1393.
A. Jonas, and O. Öktem. "Learned primal-dual reconstruction." IEEE transactions on medical imaging 37.6 (2018): 1322-1332. |
| Choice A: | Available memory on the GPU |
| Choice B: | Computational cost of imaging model |
| Choice C: | Acquisition of training data |
| Question 6: In supervised tomographic image reconstruction, what are suitable loss functions? |
| Reference: | E. Kang, J. Min, and J. C. Ye. "A deep convolutional neural network using directional wavelets for lowâ€dose Xâ€ray CT reconstruction." Medical physics 44.10 (2017): e360-e375. |
| Choice A: | Hinge loss |
| Choice B: | Mean squared error |
| Choice C: | Cross entropy |
| Choice D: | Absolute differences |
| Choice E: | Logarithmic loss |
| Choice F: | B and D |
| Question 7: Which network architecture is especially popular for segmentation in medical imaging? |
| Reference: | O. Ronneberger, P. Fischer, and T. Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015. |
| Choice A: | LeNet |
| Choice B: | AlexNet |
| Choice C: | ResNet |
| Choice D: | U-net |
| Choice E: | YOLO |
| Question 8: Generative adversarial nets |
| Reference: | I. Goodfellow et al. Generative Adversarial Nets, arXiv 2014 |
| Choice A: | are convolutional networks designed for supervised learning |
| Choice B: | are fully connected networks designed for unsuvervised learning |
| Choice C: | are neural networks designed for unsupervised learning |
| Question 9: Can a neural network be trained to reduce image noise by just receiving pairs of noisy images without seeing a clean (high dose) image |
| Reference: | J. Lehtinen et al. Noise2Noise: Learning Image Restoration without Clean Data, arXiv 2018 |
| Choice A: | Yes. The network learning itself is a statistical process and thus a noisy image as input and a matched noisy image as target are sufficient for the training to converge to the ensamble average. |
| Choice B: | Of course not! For supervised learning, matched pairs of low dose and high dose images are necessary to enable the network to learn how to reduce noise. For unsupervised learning, e.g. using GANs, high dose images need to be available to allow the discrimator to learn how low noise images look like. Noisy images as targets are thus useless! |
| Choice C: | No. Otherwise someone would have already applied that to CT images. |
| Question 10: The real-time calculation of accurate patient dose distributions can be accurately carried out with a 3D U-net |
| Reference: | J. Maier et al. IEEE Medical Imaging Conference Program, M-03-178, November 2018. |
| Choice A: | using the CT volume as the only input to the net |
| Choice B: | using the CT volume and a first order dose estimate as network inputs |
| Choice C: | using the first order dose estimate as the only network input |
