BIM 252/EEC 205 Computational Imaging Winter 2022 Project Description The project is to provide you opportunities to explore contemporary issues in image reconstruction beyond what have been covered in class. The project consists of two parts: 1. The first part is a paper review. You will pick one or more paper(s) on an image reconstruction topic and write a critical review on the topic. It can be something related to your Ph.D. work or some problem that you found interesting. The topic should be beyond what we already covered in the lectures. 2. The second part is an experimental component. You can either implement a new reconstruction method or perform a more in-depth study of a reconstruction method that we covered in the lectures. The experimental study can be related to the topic that is reviewed in part 1 (for example, you review a new reconstruction method in part 1 and implement it in part 2), but it can also be independent of part 1. The scope of the problem should be something beyond what you have done in the homework. However, it should also be restricted in scope, to fit the limited time available. You can use any program language and any online resources, but you need to perform your own experiments. You are encouraged to team up with another student on the project. The projects will be assessed based on the difficulty and importance of the problem, and on how well you do with your problem. Both parts are required, although you can choose to put more effort on one component than the other. It is OK to have a small experimental study if you already have an extensive in-depth review, or to have a short review but with an extensive experimental component. Submissions: 1. (20%) a short description of the project plan due Feb 27. It should describe the topic that you will review and the experiment study you will perform. The entire description should only be a paragraph or so. 2. (80%) project report due March 15. Your project report should be about 10 pages covering at least the following components: a) Paper review should include a. an introduction to the topic b. explanation of the new method proposed in the paper c. discussion on its advantages and limitations. b) Experimental section should include a. description of the method and computer implementation b. results from your program c. analysis and discussion on your results. If you use any existing codes that are either in public domain or borrowed from your friend, please provide proper acknowledgement. A few papers are listed below as examples. One topic is Fourier rebinning of Time-of-flight (TOF) PET data. Another paper deals with different backprojector in FDK reconstruction. Other papers are related to model-based iterative reconstruction and learning based reconstruction. You can also find many other papers on image reconstruction. Sample papers: Fourier rebinning of TOF PET A unified Fourier theory for time-of-flight PET data Yusheng Li et al Physics in Medicine and Biology 2016 61 601 IOPscience MAP reconstruction for Fourier rebinned TOF-PET data Bing Bai et al Physics in Medicine and Biology 2014 59 925 IOPscience Exact and approximate Fourier rebinning of PET data from time-of-flight to non time-of-flight – http://dx.doi.org/10.1088/0031-9155/54/3/001 Phys Med Biol. 2009 Feb 7;54(3):467-84. doi: 10.1088/0031-9155/54/3/001. Ahn S, Cho S, Li Q, Lin Y and Leahy R 2011 Optimal rebinning of time-of-flight PET data IEEE Trans. Med. Imaging 30 1808–18 CrossRef Distance-driven Projector Bruno De Man and Samit Basu. Distance-driven projection and backprojection in three dimensions. Phys. Med. Biol. 49 No 11 (7 June 2004) 2463-2475 Acrobat PDF (256 KB) Image reconstruction from sparse views Emil Y Sidky and Xiaochuan Pan. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization Phys. Med. Biol. 53 No 17 (7 September 2008) 4777-4807 Acrobat PDF (1.70 MB) Fast reconstruction algorithms Se Young Chun; Dewaraja, Y.K.; Fessler, J.A., “Alternating Direction Method of Multiplier for Tomography With Nonlocal Regularizers,” in Medical Imaging, IEEE Transactions on , vol.33, no.10, pp.1960-1968, Oct. 2014 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp tp=&arnumber=6825888&isnumber=6913038 Image regularization G. Wang and J. Qi, “PET Image Reconstruction Using Kernel Method,” in IEEE Transactions on Medical Imaging, vol. 34, no. 1, pp. 61-71, Jan. 2015, doi: 10.1109/TMI.2014.2343916. K. Gong et al., “Iterative PET Image Reconstruction Using Convolutional Neural Network Representation,” in IEEE Transactions on Medical Imaging, vol. 38, no. 3, pp. 675-685, March 2019, doi: 10.1109/TMI.2018.2869871. K. Gong, C. Catana, J. Qi and Q. Li, “PET Image Reconstruction Using Deep Image Prior,” in IEEE Transactions on Medical Imaging, vol. 38, no. 7, pp. 1655-1665, July 2019, doi: 10.1109/TMI.2018.2888491. Learning based reconstruction Zhu, B., Liu, J., Cauley, S. et al. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018). https://doi.org/10.1038/nature25988 H ggstr m I, Schmidtlein CR, Campanella G, Fuchs TJ. DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019 May;54:253-262. doi: 10.1016/j.media.2019.03.013. A. Mehranian and A. J. Reader, “Model-Based Deep Learning PET Image Reconstruction Using Forward– Backward Splitting Expectation–Maximization,” in IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 1, pp. 54-64, Jan. 2021, doi: 10.1109/TRPMS.2020.3004408. Whiteley W, Luk WK, Gregor J. DirectPET: full-size neural network PET reconstruction from sinogram data. J Med Imaging (Bellingham). 2020 May;7(3):032503. doi: 10.1117/1.JMI.7.3.032503. Epub 2020 Feb 28. PMID: 32206686; PMCID: PMC7048204.
