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ESWT - tracking organs during focused ultrasound surgery

 
: Grozea, C.; Lübke, D.; Dingeldey, F.; Schiewe, M.; Gerhardt, J.; Schumann, C.; Hirsch, J.

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Santamaría, Ignacio (Ed.) ; IEEE Signal Processing Society; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 : Santander, Spain, 23 - 26 September 2012
Piscataway/NJ: IEEE, 2012
ISBN: 978-1-4673-1025-3 (Online)
ISBN: 978-1-4673-1024-6 (Print)
ISBN: 978-1-4673-1026-0
S.60-65
International Workshop on Machine Learning for Signal Processing (MLSP) <22, 2012, Santander>
Englisch
Konferenzbeitrag
Fraunhofer FIRST ()
Fraunhofer MEVIS ()
biomedical MRI; biomedical ultrasonics; liver; sensor fusion; surgery; target tracking; automated focused ultrasound surgery environment; internal organ position prediction; free breathing; FUS; HIFU; machine learning; signal processing

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.

: http://publica.fraunhofer.de/dokumente/N-246754.html