ESWT - tracking organs during focused ultrasound surgery

Abstract

We report here our results in a multi-sensor setup reproducing the conditions of an automated focused ultrasound surgery environment. The aim is to continuously predict the position of an internal organ (here the liver) under guided and non-guided free breathing, with the accuracy required by surgery. We have performed experiments with 16 healthy human subjects, two of those taking part in full-scale experiments involving a 3 Tesla MRI machine recording a volume containing the liver. For the other 14 subjects we have used the optical tracker as a surrogate target. All subjects where volunteers who agreed to participate in the experiments after being thoroughly informed about it. For the MRI sessions we have analyzed semi-automatically offline the images in order to obtain the ground truth, the true position of the selected feature of the liver. The results we have obtained with continuously updated random forest models are very promising, we have obtained good prediction-target correlation coefficients for the surrogate targets (0.71 ± 0.1) and excellent for the real targets in the MRI experiments (over 0.91), despite being limited to a lower model update frequency, once every 6.16 seconds

Motion Correction Techniques for MR-Guided HIFU Ablation of Abdominal Organs

“…The use of external sensors such as respiratory bellows, 24, 51, 52 nasal airflow prong or spirometer 51 have been suggested as potential surrogate of the respiratory motion. Optical imaging has also been proposed 53 using a shielded MR-compatible optical camera.…”

“…51,52,54,[58][59][60]62 However, intra-procedural acquisition of 3D+t training data is challenging, time consuming and requires computationally intensive post-processing which would substantially extend the duration of the intervention. Therefore, pre-procedural acquisition of training data (i.e.…”

Respiratory motion model based on the noise covariance matrix of a receive array

“…For a reliable motion model, the surrogate signal must be consistent between the two steps of the imaging session (i.e., the training set for the model formation and the interventional procedure), and have sufficient temporal resolution to resolve the physiological motion. In the scope of respiratory motion, the use of external sensors such as respiratory bellows , nasal air flow prong, spirometer , or optical imaging using a shielded MR‐compatible optical camera have been suggested. The advantage of these methods is their ability to provide surrogate motion information with high temporal resolution and their independence from the imaging system.…”