During the last years, researchers work on improving the accuracy of imaging techniques which are also used for image-guided interventions. For example, liver intervention can become a challenging task due to the respiration motion causing misalignment between the interventional mapping obtaining pre-treament and the changed anatomical parameters during application phase. In the presented thesis, the main task pays attention to the estimation of the liver and hepatic tumor motion.
The proposed models to work on targeting to a patient specific respiratory motion approach where ex-ternal signals called surrogates are used because of their non-invasive nature and due to their advantageous characteristics such as the high frame rate, compensating with the drawbacks of the current imaging tech-niques. A supervised Machine Learning algorithm will be used for mitigating the aforementioned problem and more specifically, a Long Short-Term Memory (LSTM) neural network will be utilized focusing on locating the dependencies and finding the common hidden features between long-term motion sequences. In contrast to many previous studies where there are three independent workflow blocks, briefly: model calibration, model formation and model application, in the presented work, a novel methodology that has been proposed by Baumgartner et al. in 2017 will be also investigated for its promising results. The main difference is located on the back-propagation action on the model application phase which keeps continuously updated the model based on the changing breathing patterns.
At the end, regarding the image modalities and the available datasets, MRI images of healthy subjects along with CT scanning data of patients will be utilized for this study to generalize the model by including inter- and intra-variations parameters.