Abstract
Laparoscopic Ultrasound (LUS) can enhance the safety of laparoscopic liver resection by providing information on the location of major blood vessels and tumours. Since many tumours are not visible in ultrasound, registration to a pre-operative CT has been proposed as a guidance method. In addition to being multi-modal, this registration problem is greatly affected by the differences in field of view between CT and LUS, and thus requires an accurate initialisation. We propose a novel method of registering smaller field of view slices to a larger volume globally using a Content-based retrieval framework. This problem is under-constrained for a single slice registration, resulting in non-unique solutions. Therefore, we introduce kinematic priors in a Bayesian framework in order to jointly register groups of ultrasound images. Our method then produces an estimate of the most likely sequence of CT images to represent the ultrasound acquisition and does not require tracking information nor an accurate initialisation. We demonstrate the feasibility of this approach in multiple LUS acquisitions taken from three sets of clinical data.
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References
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Acknowledgements
JR was supported by the EPSRC CDT in Medical Imaging [EP/L016478/1] and EPSRC grant [EP/P034454/1]. MJC, DH and DB were supported by the Welcome/EPSRC [203145Z/16/Z]. BD was supported by the NIHR Biomedical Research Centre at University College London Hospitals NHS Foundations Trust and University College London. The imaging data used for this work was obtained with funding from the Health Innovation Challenge Fund (HICF-T4-317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the author(s) and not necessarily those of the Wellcome Trust or the Department of Health.
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Ramalhinho, J. et al. (2019). Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT Using Content-Based Retrieval and Kinematic Priors. In: Wang, Q., et al. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. PIPPI SUSI 2019 2019. Lecture Notes in Computer Science(), vol 11798. Springer, Cham. https://doi.org/10.1007/978-3-030-32875-7_2
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