期刊
MEDICAL IMAGE ANALYSIS
卷 18, 期 5, 页码 740-751出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2014.03.006
关键词
Respiratory motion compensation; Statistical motion model; Spatio-temporal prediction; 4D-MRI; Ultrasound
类别
资金
- Swiss National Science Foundation [CRSII2-127549]
- Swiss National Science Foundation (SNF) [CRSII2_127549] Funding Source: Swiss National Science Foundation (SNF)
With the availability of new and more accurate tumour treatment modalities such as high-intensity focused ultrasound or proton therapy, accurate target location prediction has become a key issue. Various approaches for diverse application scenarios have been proposed over the last decade. Whereas external surrogate markers such as a breathing belt work to some extent, knowledge about the internal motion of the organs inherently provides more accurate results. In this paper, we combine a population-based statistical motion model and information from 2d ultrasound sequences in order to predict the respiratory motion of the right liver lobe. For this, the motion model is fitted to a 3d exhalation breath-hold scan of the liver acquired before prediction. Anatomical landmarks tracked in the ultrasound images together with the model are then used to reconstruct the complete organ position over time. The prediction is both spatial and temporal, can be computed in real-time and is evaluated on ground truth over long time scales (5.5 min). The method is quantitatively validated on eight volunteers where the ultrasound images are synchronously acquired with 4D-MRI, which provides ground-truth motion. With an average spatial prediction accuracy of 2.4 mm, we can predict tumour locations within clinically acceptable margins. (C) 2014 Elsevier B.V. All rights reserved.
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