Liver intervention can become a challenging task due to the respiration induced motion. The latter causes misalignment between the interventional mapping obtaining pre-treatment and the changed anatomical parameters during application phase (liver biopsy or radiotherapy) leading to increased damage of healthy tissue as well as inaccurate targeting of hepatic tumors. In the presented work, Respiratory Motion Estimation is exploited where using external signals (surrogates), it is possible to estimate the liver actual motion.
The proposed work has been evaluated in several breathing patterns in comparison with previous studies making usage of ultrasound (US) sensor as surrogate, placed on the human’s abdominal region. Next, three regression models (simple linear regression, polynomial fitting, single layer perceptron) were utilized to correlate the liver motion with the US signal and consequent trained to estimate the superior-inferior (SI) motion of the liver upper border available in 2D Magnetic Resonance Imaging (MRI) sagittal images. Additionally, extending the conventional framework and taking advantage of Deep Learning (DL) and more specifically Long Short-Term Memory (LSTM) networks, it is feasible to predict the liver motion in a short future state combined with a classifier that can detect the performed respiration type.
The proposed DL approach has been validated in MRI on ten healthy human subjects when the findings revealing an estimation of the liver motion in SI direction with a Root Mean Square Error (RMSE) accuracy below 1.2 ± 0.2 mm (95% CI) and a capability of liver motion prediction for 6 sec ahead.