Non-local means resolution enhancement of lung 4D-CT data

Authors:
Yu Zhang;Guorong Wu;Pew-Thian Yap;Qianjin Feng;Jun Lian;Wufan Chen;Dinggang Shen
Affiliations:
School of Biomedical Engineering, Southern Medical University, Guang Zhou, China, Department of Radiology and BRIC, University of North Carolina, Chapel Hill;Department of Radiology and BRIC, University of North Carolina, Chapel Hill;Department of Radiology and BRIC, University of North Carolina, Chapel Hill;School of Biomedical Engineering, Southern Medical University, Guang Zhou, China;Department of Radiation Oncology, University of North Carolina, Chapel Hill;School of Biomedical Engineering, Southern Medical University, Guang Zhou, China;Department of Radiology and BRIC, University of North Carolina, Chapel Hill
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

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Abstract

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.