The Centre has a long history of developing new techniques for medical imaging (particularly in magnetic resonance imaging), transforming them from a primarily diagnostic modality into an interventional and therapeutic platform. This is facilitated by the Centre's strong engineering background in practical imaging and image analysis platform development, as well as advances in minimal access and robotic assisted surgery. Hamlyn has a strong tradition in pursuing basic sciences and theoretical research, with a clear focus on clinical translation.

In response to the current paradigm shift and clinical demand in bringing cellular and molecular imaging modalities to an in vivo – in situ setting during surgical intervention, our recent research has also been focussed on novel biophotonics platforms that can be used for real-time tissue characterisation, functional assessment, and intraoperative guidance during minimally invasive surgery. This includes, for example, SMART confocal laser endomicroscopy, time-resolved fluorescence spectroscopy and flexible FLIM catheters.

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    Zhou X, Riga C, Yang G, Lee Set al.,

    3D Shape Recovery of Deployed Stent Grafts from a Single X-ray Image based on Newly Designed Markers

    , MICCAI Workshop on CVII-STENT 2016
    Feng Y, Guo Z, Dong Z, Zhou XY, Kwok KW, Ernst S, Lee SLet al., 2017,

    An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning.

    , Int J Comput Assist Radiol Surg

    OBJECTIVE: A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provided to novice operators or even directly to catheter robots. METHODS: A learning from demonstration (LfD) framework, based upon previous cardiac mapping procedures performed by an expert operator, in conjunction with Gaussian process (GP) model-based active learning, was developed to efficiently perform voltage mapping over right ventricles (RV). The GP model was used to output the next best mapping point, while getting updated towards the underlying voltage data pattern as more mapping points are taken. A regularized particle filter was used to keep track of the kernel hyperparameter used by GP. The travel cost of the catheter tip was incorporated to produce time-efficient mapping sequences. RESULTS: The proposed strategy was validated on a simulated 2D grid mapping task, with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using the Stereotaxis Niobe(®) remote magnetic navigation system, and on a tele-operated catheter robot. In comparison with an existing geometry-based method, regression error was reduced and was minimized at a faster rate over retrospective procedure data. CONCLUSION: A new method of catheter mapping guidance has been proposed based on LfD and active learning. The proposed method provides real-time guidance for the procedure, as well as a live evaluation of mapping sufficiency.

    Lee S-L, 2017,

    Examining the use of a novel dynamic endovascular simulator to facilitate intelligent localization and robotic technologies

    , Vascular-Societies Annual Scientific Meeting, Publisher: WILEY, Pages: 16-16, ISSN: 0007-1323
    Vyas K, Hughes M, Leff DR, Yang G-Zet al., 2017,

    Methylene-blue aided rapid confocal laser endomicroscopy of breast cancer

    , JOURNAL OF BIOMEDICAL OPTICS, Vol: 22, ISSN: 1083-3668
    Zhang L, Ye M, Giataganas P, Hughes M, Bradu A, Podoleanu A, Yang GZet al., 2017,

    From Macro to Micro: Autonomous Multiscale Image Fusion for Robotic Surgery

    , IEEE Robotics and Automation Magazine, ISSN: 1070-9932

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