Abstract
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.
| Original language | English |
|---|---|
| Article number | 6213122 |
| Pages (from-to) | 1993-2005 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Medical Imaging |
| Volume | 31 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
| Externally published | Yes |
Fingerprint
Keywords
- Adaptive dictionary
- four-dimensional computed tomography (4D-CT) lung data
- hierarchical patch-based sparse representation
- resolution enhancement
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Radiological and Ultrasound Technology
- Software
Cite this
Hierarchical patch-based sparse representation-a new approach for resolution enhancement of 4D-CT lung data. / Zhang, Yu; Wu, Guorong; Yap, Pew Thian; Feng, Qianjin; Lian, Jun; Chen, Wufan; Shen, Dinggang.
In: IEEE Transactions on Medical Imaging, Vol. 31, No. 11, 6213122, 01.12.2012, p. 1993-2005.Research output: Contribution to journal › Article
}
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
PY - 2012/12/1
Y1 - 2012/12/1
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
UR - http://www.scopus.com/inward/record.url?scp=84877301335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877301335&partnerID=8YFLogxK
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 -