TY - JOUR
T1 - Hierarchical patch-based sparse representation-a new approach for resolution enhancement of 4D-CT lung data
AU - Zhang, Yu
AU - Wu, Guorong
AU - Yap, Pew Thian
AU - Feng, Qianjin
AU - Lian, Jun
AU - Chen, Wufan
AU - Shen, Dinggang
N1 - Funding Information:
Manuscript received March 26, 2012; revised May 07, 2012; accepted May 21, 2012. Date of publication June 06, 2012; date of current version October 26, 2012. This work was supported in part by the Major State Basic Research Development Program of China under Grant 2010CB732500, in part by the National Natural Science Foundation of China under Grant 31271067, in part by the Science and Technology Planning Project of Guangzhou under Grant 2010JE471, and in part by the National Institutes of Health under Grant CA140413. Asterisk indicates corresponding author.
Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Four-dimensional computed tomography (4D-CT) plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.
AB - Four-dimensional computed tomography (4D-CT) plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.
KW - Adaptive dictionary
KW - four-dimensional computed tomography (4D-CT) lung data
KW - hierarchical patch-based sparse representation
KW - resolution enhancement
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U2 - 10.1109/TMI.2012.2202245
DO - 10.1109/TMI.2012.2202245
M3 - Article
C2 - 22692897
AN - SCOPUS:84877301335
VL - 31
SP - 1993
EP - 2005
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 11
M1 - 6213122
ER -
