A patient specific 4D MRI liver motion model based on sparse imaging and registration

Introduction: Image-guided minimally invasive procedures are becoming increasingly popular. Currently, High-Intensity Focused Ultrasound (HIFU) treatment of lesions in mobile organs, such as the liver, is in development. A requirement for such treatment is automatic motion tracking, such that the position of the lesion can be followed in real time. We propose a 4D liver motion model, which can be used during planning of this procedure. During treatment, the model can serve as a motion predictor. In a similar fashion, this model could be used for radiotherapy treatment of the liver. Method: The model is built by acquiring 2D dynamic sagittal MRI data at six locations in the liver. By registering these dynamics to a 3D MRI liver image, 2D deformation fields are obtained at every location. The 2D fields are ordered according to the position of the liver at that specific time point, such that liver motion during an average breathing period can be simulated. This way, a sparse deformation field is created over time. This deformation field is finally interpolated over the entire volume, yielding a 4D motion model. Results: The accuracy of the model is evaluated by comparing unseen slices to the slice predicted by the model at that specific location and phase in the breathing cycle. The mean Dice coefficient of the liver regions was 0.90. The mean misalignment of the vessels was 1.9 mm. Conclusion: The model is able to predict patient specific deformations of the liver and can predict regular motion accurately.