Abstract
A critical concern with lung 4D-CT is the low superior-inferior resolution, due to the consideration of radiation dose. We propose a resolution enhancement approach that reconstructs missing intermediate slices by exploiting the idea that information lost in one respiratory phase can be found in others, according to the complimentary nature of inter-phase information. Our approach is based on a patch-based framework that explores the role of group-sparsity involving groups of similar neighbouring patches. We discuss the regularizing role of group-sparsity, which helps in reducing the effect of noise and enables better enhancement of anatomical structures. Our results positively demonstrate the potential of group-sparsity for 4D-CT resolution enhancement.
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Keywords
- Sparse Representation
- Resolution Enhancement
- Respiratory Phasis
- Deformable Image Registration
- Dictionary Atom
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Bhavsar, A., Wu, G., Shen, D. (2013). Harnessing Group-Sparsity Regularization for Resolution Enhancement of Lung 4D-CT. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_18
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DOI: https://doi.org/10.1007/978-3-642-40760-4_18
Publisher Name: Springer, Berlin, Heidelberg
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