3D Organ Motion Prediction for MR-Guided High Intensity Focused Ultrasound

Abstract

Abstract. MR-guided High Intensity Focused Ultrasound is an emerging non-invasive technique capable of depositing sharply localised energy deep within the body, without affecting the surrounding tissues. This, however, implies exact knowledge of the target's position when treating mobile organs. In this paper we present an atlas-based prediction technique that trains an atlas from time-resolved 3D volumes using 4DMRI, capturing the full patient specific motion of the organ. Based on a breathing signal, the respiratory state of the organ is then tracked and used to predict the target's future position. To additionally compensate for the non-periodic slower organ drifts, the static motion atlas is combined with a population-based statistical exhalation drift model. The proposed method is validated on organ motion data of 12 healthy volunteers. Experiments estimating the future position of the entire liver result in an average prediction error of 1.1 mm over time intervals of up to 13 minutes.

Model-Based Respiratory Motion Compensation in MRgHIFU

“…implanted surrogate markers). In [3] a prediction error of 1.1 mm is achieved by acquiring 3D information of the patient specific liver motion.…”

“…Due to the low sampling rate, the learning based algorithms would lead to considerable prediction errors at each ex-and inhalation position before adapting. Therefore we use a similar technique as proposed in [3], where a one-dimensional breathing model based on the measured pencil beam navigators is created. In contrast to the latter approach where the model is acquired in a training phase and then stays fixed, our respiratory model steadily grows even during increasing treatment time T .…”

“…Only recently, a combination of a pattern matching approach using a static subjectspecific model and a population-based statistical drift model for motion-compensated MRgHIFU treatment was described and evaluated on realistic 4DMRI data [3]. While the results are convincing, the acquisition of a patient-specific 3D motion atlas takes several minutes and the processing time is in the range of hours and thus is not acceptable for clinical use.…”

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

“…Detection of the organ drift is difficult by using external sensors. Arnold et al [62] developed a statistical exhalation drift model. They reported that estimation with the drift model showed significant improvement compared to estimation without the drift model.…”

Untitled

“…In minimally invasive surgery, the methods are mainly based on the use of intraoperative imaging and tracking systems [8][9][10]. One solution to the deformation produced by the movement of the organ was described by Zhang et al [11], who compensate organ motion using an electromagnetic tracking system.…”