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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