Image resolution in 4D-CT is a crucial bottleneck that needs to be overcome for improved dose planning in radiotherapy for lung cancer. In this paper, we propose a novel patch-based algorithm to enhance the image quality of 4D-CT data. Our premise is that anatomical information missing in one phase can be recovered from complementary information embedded in other phases. We employ a patch-based mechanism to propagate information across phases for reconstruction of intermediate slices in the axial direction, where resolution is normally the lowest. Specifically, structurally-matching and spatially-nearby patches are combined for reconstruction of each patch. For greater sensitivity to anatomical nuances, we further employ a quad-tree technique to adaptively partition each slice of the image in each phase for more fine-grained refinement. Our evaluation based on a public 4D-CT lung data indicates that our algorithm gives very promising results with significantly enhanced image structures.
|Title of host publication||Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|
|Number of pages||9|
|Publication status||Published - 2012 Dec 1|
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