| Question 1: Requesting a transfer of images from a source to target system is defined in which DICOM object. |
| Reference: | Reference: Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging W. Dean Bidgood, Jr, MD, MS, Steven C. Horii, MD, Fred W. Prior, PhD, Donald E. Van Syckle Journal of the American Medical Informatics Association, Volume 4, Issue 3, May 1997, Pages 199–212, https://doi.org/10.1136/jamia.1997.0040199 |
| Choice A: | DICOM C-GET |
| Choice B: | DICOM C-MOVE |
| Choice C: | DICOM C-STORE |
| Choice D: | DICOM C-FIND |
| Question 2: Jupyter notebooks allow for reproducible research by storing in a JSON format the following types of objects. |
| Reference: | Reference: Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks Adam Rule,Amanda Birmingham,Cristal Zuniga,Ilkay Altintas,Shih-Cheng Huang,Rob Knight,Niema Moshiri,Mai H. Nguyen,Sara Brin Rosenthal,Fernando Pérez,Peter W. Rose
https://doi.org/10.1371/journal.pcbi.1007007 |
| Choice A: | Computational code |
| Choice B: | HTML markup code |
| Choice C: | Output results from computational code |
| Choice D: | All of the above |
| Question 3: De-identifying medical images requires |
| Reference: | Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy K. Y. E. Aryanto, M. Oudkerk & P. M. A. van Ooijen European Radiology volume 25, pages3685–3695 (2015) |
| Choice A: | DICOM data elements that contain patient identifiable information |
| Choice B: | Removing any patient identifiable information ‘burned’ into pixel data |
| Choice C: | Removing any facially identifiable features in MR and CT head volumetric datasets. |
| Choice D: | All of the above |
| Question 4: Grand challenges provide value to the medical imaging research community by: |
| Reference: | Reference: Armato SG III, Drukker K, Li F, Hadjiiski L, Tourassi GD, Engelmann RM, Giger ML, Redmond G, Farahani K, Kirby JS, Clarke LP: The LUNGx Challenge for computerized lung nodule classification. Journal of Medical Imaging 3: 044506-1–044506-9, 2016. |
| Choice A: | Making available a common set of images to all participating groups |
| Choice B: | Advertising a specific clinical need |
| Choice C: | Increasing the intellectual property value of participants’ methods |
| Choice D: | Connecting academic groups with industry partners |
| Question 5: The generalizability of artificial intelligence (AI) techniques can be limited by: |
| Reference: | Reference: Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3, 199–217 (2021). https://doi.org/10.1038/s42256-021-00307-0 |
| Choice A: | Lack of compute power |
| Choice B: | The expected clinical workload |
| Choice C: | Biases in the data used to train the AI system |
| Choice D: | AI techniques do not suffer from lack of generalizability |
| Question 6: What is an advantage of the deep learning based methods for image quality enhancement? |
| Reference: | 1. Sahiner, Berkman, et al. "Deep learning in medical imaging and radiation therapy." Medical physics 46.1 (2019): e1-e36.
2. Jiang, Zhuoran, Yingxuan Chen, Yawei Zhang, Yun Ge, Fang-Fang Yin, and Lei Ren. "Augmentation of CBCT reconstructed from under-sampled projections using deep learning." IEEE transactions on medical imaging 38, no. 11 (2019): 2705-2715. |
| Choice A: | Recovers anatomical structures that are completely lost in the input low quality images. |
| Choice B: | Eliminates the need for high quality ground-truth images in the training data. |
| Choice C: | Has very short prediction time, making it applicable for clinical usage. |
| Choice D: | Has very short training time (around seconds), making it fast to be retrained for different patient cohorts. |
| Question 7: Which of the following is true regarding deep learning based deformable image registration? |
| Reference: | Reference: Jiang, Zhuoran, Fang-Fang Yin, Yun Ge, and Lei Ren. "A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration." Physics in Medicine & Biology 65, no. 1 (2020): 015011. |
| Choice A: | Slow speed |
| Choice B: | Require manual tuning for each dataset |
| Choice C: | The results are user dependent |
| Choice D: | Its registration accuracy is not affected by the image quality |
| Choice E: | Ground truth deformation field may not be needed for model training |
| Question 8: 1. The 3 overarching aims of implementation sciences are: |
| Reference: | Reference: Owen N, Goode A, Sugiyama T, Koohsari MJ, Healy G, Fjeldsoe B, Eakin E. Designing for dissemination in chronic disease prevention and management. In: Dissemination and implementation research in health. Brownson RC, Colditz GA, Proctor EK (Eds). Oxford University Press, New York, NY. 2018; pp 107-120. |
| Choice A: | Involve only the engineers building the system |
| Choice B: | Involve relevant stakeholders as early in the process as possible |
| Choice C: | Involve only those who will be responsible for purchasing the technology |
| Choice D: | Involve social media and other marketing avenues |
| Question 9: EPIS framework involves: |
| Reference: | Reference: Moullin JC, Dickson KS, Stadnick NA, Becan JE, Wiley T, Phillips J, Match M, Aarons GA. Exploration, Preparation, Implementation, Sustainment (EPIC) framework. In: Handbook on Implementation Science. Nilsen P, Birken SA (Eds). Edward Elgar Publishing, Inc. Northhampton, MA. 2020; pp 32-61. |
| Choice A: | Engagement, Preparation, Implementation, Sustainment |
| Choice B: | Exploration, Preparation, Investigation, Sustainment |
| Choice C: | Exploration, Participation, Implementation, Sustainment |
| Choice D: | Exploration, Preparation, Implementation, Sustainment |
| Question 10: To effectively disseminate new technologies and encourage effective use, you should: |
| Reference: | Reference: Owen N, Goode A, Sugiyama T, Koohsari MJ, Healy G, Fjeldsoe B, Eakin E. Designing for dissemination in chronic disease prevention and management. In: Dissemination and implementation research in health. Brownson RC, Colditz GA, Proctor EK (Eds). Oxford University Press, New York, NY. 2018; pp 107-120. |
| Choice A: | Involve only the engineers building the system |
| Choice B: | Involve relevant stakeholders as early in the process as possible |
| Choice C: | Involve only those who will be responsible for purchasing the technology |
| Choice D: | Involve social media and other marketing avenues |
