2013
DOI: 10.1118/1.4821545
|View full text |Cite
|
Sign up to set email alerts
|

An optimization algorithm for 3D real-time lung tumor tracking during arc therapy using kV projection images

Abstract: Purpose: To develop a real-time markerless 3D tumor tracking using kilovoltage (kV) cone-beam CT (CBCT) projection images during volumetric modulated arc therapy (VMAT) treatment of lung tumors. Methods:The authors have developed a method to identify the position of lung tumors during VMAT treatment, where the current mean 3D position is detected and subsequently the real time 3D position is obtained. The mean position is evaluated by iteratively minimizing an observation error function between the tumor coord… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
9
0

Year Published

2015
2015
2019
2019

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(9 citation statements)
references
References 49 publications
0
9
0
Order By: Relevance
“…Several authors, including Zhuang et al 19 9 reported tracking errors of 0.9 mm (mean), 0.5 mm (1 SD), 2.7 mm (max), and RMS error of 1.0 mm. Our differences between tracking software and manual scoring of tumor position (validation data) were 1.1 mm (mean), 0.5 mm (1 SD), 2.5 mm (maximum), with a RMS of 1.2 mm (case A) and 0.7 mm (mean), 0.5 mm (1 SD), 1.7 mm (maximum), and a RMS of 0.3 mm (case B, in the two selected segments where tracking was possible).…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Several authors, including Zhuang et al 19 9 reported tracking errors of 0.9 mm (mean), 0.5 mm (1 SD), 2.7 mm (max), and RMS error of 1.0 mm. Our differences between tracking software and manual scoring of tumor position (validation data) were 1.1 mm (mean), 0.5 mm (1 SD), 2.5 mm (maximum), with a RMS of 1.2 mm (case A) and 0.7 mm (mean), 0.5 mm (1 SD), 1.7 mm (maximum), and a RMS of 0.3 mm (case B, in the two selected segments where tracking was possible).…”
Section: Discussionmentioning
confidence: 99%
“…17 Finally, several authors have reported on kV-based markerless tumor tracking in three-dimensions during full 360 • gantry rotation. [18][19][20] Hugo et al 18 used a CBCT sinogram and several template tracking based methods, including robust correlation to try and overcome internal (e.g., ribs and spine) or external (e.g., couch) occlusions. They tracked an oscillating phantom and peripheral lung tumors in two patients and reported a mean position estimation accuracy of 1.4 mm in the phantom and agreement to within 2 mm in comparison with a respiratory correlated reference dataset for the patient data.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Template matching with one or multiple templates generated from four-dimensional computed tomography (4D-CT) is a well-established technique for lung tumor tracking in xray images. [4][5][6][7][8] The metrics used for the template matching are mostly normalized cross-correlation (NCC) 4,5 and (normalized) mutual information. 6,8 Poisson maximum likelihood performs slightly better compared to NCC in ultra-low-dose (ULD) kV images which suffer from high levels of Poisson noise.…”
Section: Introductionmentioning
confidence: 99%
“…[4][5][6][7][8] The metrics used for the template matching are mostly normalized cross-correlation (NCC) 4,5 and (normalized) mutual information. 6,8 Poisson maximum likelihood performs slightly better compared to NCC in ultra-low-dose (ULD) kV images which suffer from high levels of Poisson noise. 9 Similarly tumor tracking in MV-images is more challenging due to the low imaging contrast.…”
Section: Introductionmentioning
confidence: 99%