Background: Respiratory-induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates.
Methods: A learning-based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates. Accordingly, appropriate motion models (multivariate, Ridge and Lasso regression models) were designed to correlate the liver motion with the abdominal motion, and trained to estimate the superior-inferior (SI) motion of the liver. Three subjects volunteered for 6 sessions of such that liver images acquired by magnetic resonance imaging (MRI) were recorded alongside camera-tracked external markers.
Results and conclusions: The proposed machine learning approach was validated in MRI on human subjects and the results show that the approach could estimate the respiratory-induced SI motion of the liver with a mean absolute error (MAE) accuracy below 2 mm.
Keywords: machine learning; magnetic resonance imaging (MRI); respiratory motion estimation (RME); respiratory-induced motion (RIM); surrogate signals.
© 2018 The Authors. The International Journal of Medical Robotics and Computer Assisted Surgery Published by John Wiley & Sons Ltd.