Abstract
Purpose
Four-dimensional CT imaging is widely used to account for motion-related effects during radiotherapy planning of lung cancer patients. However, 4D CT often contains motion artifacts, cannot be used to measure motion variability, and leads to higher dose exposure. In this article, we propose using 4D MRI to acquire motion information for the radiotherapy planning process. From the 4D MRI images, we derive a time-continuous model of the average patient-specific respiratory motion, which is then applied to simulate 4D CT data based on a static 3D CT.
Methods
The idea of the motion model is to represent the average lung motion over a respiratory cycle by cyclic B-spline curves. The model generation consists of motion field estimation in the 4D MRI data by nonlinear registration, assigning respiratory phases to the motion fields, and applying a B-spline approximation on a voxel-by-voxel basis to describe the average voxel motion over a breathing cycle. To simulate a patient-specific 4D CT based on a static CT of the patient, a multi-modal registration strategy is introduced to transfer the motion model from MRI to the static CT coordinates.
Results
Differences between model-based estimated and measured motion vectors are on average 1.39 mm for amplitude-based binning of the 4D MRI data of three patients. In addition, the MRI-to-CT registration strategy is shown to be suitable for the model transformation.
Conclusions
The application of our 4D MRI-based motion model for simulating 4D CT images provides advantages over standard 4D CT (less motion artifacts, radiation-free). This makes it interesting for radiotherapy planning.
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Acknowledgments
This work is supported by Deutsche Forschungsgemeinschaft (HA 2355/9-2).
Conflict of interest
Mirko Marx, Jan Ehrhardt, René Werner, Heinz-Peter Schlemmer, and Heinz Handels declare that they have no conflict of interest. Informed consent was obtained from all patients for being included in the study.
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Marx, M., Ehrhardt, J., Werner, R. et al. Simulation of spatiotemporal CT data sets using a 4D MRI-based lung motion model. Int J CARS 9, 401–409 (2014). https://doi.org/10.1007/s11548-013-0963-y
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DOI: https://doi.org/10.1007/s11548-013-0963-y