Segmentation of Single-Figure Objects by Deformable M-reps
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Registration-derived estimates of local lung expansion as surrogates for regional ventilation
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Generation of a mean motion model of the lung using 4D-CT image data
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
An implicit inter-subject shape driven image deformation model for prostate motion estimation
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
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Intensity modulated radiation therapy (IMRT) for cancers in the lung remains challenging due to the complicated respiratory dynamics. We propose a shape-navigated dense image deformation model to estimate the patient-specific breathing motion using 4D respiratory correlated CT (RCCT) images. The idea is to use the shape change of the lungs, the major motion feature in the thorax image, as a surrogate to predict the corresponding dense image deformation from training. To build the statistical model, dense diffeomorphic deformations between images of all other time points to the image at end expiration are calculated, and the shapes of the lungs are automatically extracted. By correlating the shape variation with the temporally corresponding image deformation variation, a linear mapping function that maps a shape change to its corresponding image deformation is calculated from the training sample. Finally, given an extracted shape from the image at an arbitrary time point, its dense image deformation can be predicted from the pre-computed statistics. The method is carried out on two patients and evaluated in terms of the tumor and lung estimation accuracies. The result shows robustness of the model and suggests its potential for 4D lung radiation treatment planning.