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Open Access Green as soon as Postprint is submitted to ZB.
Regional lung ventilation estimation based on supervoxel tracking.
Proc. SPIE 10576 (2018)
In the case of lung cancer, an assessment of regional lung function has the potential to guide more accurate radiotherapy treatment. This could spare well-functioning parts of the lungs, as well as be used for follow up. In this paper we present a novel approach for regional lung ventilation estimation from dynamic lung CT imaging, which might be used during radiotherapy planning. Our method combines a supervoxel-based image representation with deformable image registration, performed between peak breathing phases, for which we track changes in intensity of previously extracted supervoxels. Such a region-oriented approach is expected to be more physiologically consistent with lung anatomy than previous methods relying on voxel-wise relationships, as it has the potential to mimic the lung anatomy. Our results are compared with static ventilation images acquired from hyperpolarized Xenon129 MRI. In our study we use three patient datasets consisting of 4DCT and XeMRI. We achieve higher correlation (0.487) compared to the commonly used method for estimating ventilation performed in a voxel-wise manner (0.423) on average based on global correlation coefficients. We also achieve higher correlation values for our method when ventilated/non-ventilated regions of lungs are investigated. The increase of the number of layers of supervoxels further improves our results, with one layer achieving 0.393, compared to 0.487 for 15 layers. Overall, we have shown that our method achieves higher correlation values compared to the previously used approach, when correlated with XeMRI.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Image Registration ; Lung Ventilation Estimation ; Supervoxels ; Tracking
ISSN (print) / ISBN
0277-786X
e-ISSN
1996-756X
Journal
Proceedings of SPIE
Quellenangaben
Volume: 10576
Publisher
SPIE
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)