000221543 001__ 221543
000221543 005__ 20190317000537.0
000221543 037__ $$aARTICLE
000221543 245__ $$aCompressed Sensing and Adaptive Graph Total Variation for Tomographic Reconstructions
000221543 336__ $$aJournal Articles
000221543 520__ $$aCompressed Sensing (CS) and Total Variation (TV)- based iterative image reconstruction algorithms have received increased attention recently. This is due to the ability of such methods to reconstruct from limited and noisy data. Local TV methods fail to preserve texture details and fine structures, which are tedious for the method to distinguish from noise. In many cases local methods also create additional artifacts due to over smoothing. Non-Local Total Variation (NLTV) has been increasingly used for medical imaging applications. However, it is not updated in every iteration of the algorithm, has a high computational complexity and depends on the scale of pairwise parameters. In this work we propose using Adaptive Graph- based TV in combination with CS (ACSGT). Similar to NLTV our proposed method goes beyond spatial similarity between different regions of an image being reconstructed by establishing a connection between similar regions in the image regardless of spatial distance. However, it is computationally much more efficient and scalable when compared to NLTV due to the use of approximate nearest neighbor search algorithm. Moreover, our method is adaptive, i.e, it involves updating the graph prior every iteration making the connection between similar regions stronger. Since TV is a special case of graph TV the proposed method can be seen as a generalization of CS and TV methods. We test our proposed algorithm by reconstructing a variety of different phantoms from limited and corrupted data and observe that we achieve a better result with ACSGT in every case.
000221543 6531_ $$aTomography
000221543 6531_ $$aTotal Variation
000221543 6531_ $$aGraphs
000221543 6531_ $$aIterative Image Reconstruction
000221543 6531_ $$aNon-local Total Variation
000221543 6531_ $$aNon-local im- age processing
000221543 6531_ $$aCompressive Sensing
000221543 6531_ $$aNon-local denoising
000221543 6531_ $$aNon- local Regularization
000221543 700__ $$aMahmood, Faisal
000221543 700__ $$0248142$$g232886$$aShahid, Nauman
000221543 700__ $$aSkoglund, Ulf
000221543 700__ $$g120906$$aVandergheynst, Pierre$$0240428
000221543 773__ $$tIEEE Transaction on Medical Imaging
000221543 8564_ $$uhttps://infoscience.epfl.ch/record/221543/files/graph_tomo_1.pdf$$zn/a$$s5850910$$yn/a
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000221543 937__ $$aEPFL-ARTICLE-221543
000221543 973__ $$rNON-REVIEWED$$sSUBMITTED$$aEPFL
000221543 980__ $$aARTICLE