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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  • Journal article
    Kedrzycki MS, Elson DS, Leff DR, 2020,

    ASO author reflections: fluorescence-guided sentinel node biopsy for breast cancer

    , Annals of Surgical Oncology, ISSN: 1068-9265
  • Conference paper
    Zhan J, Cartucho J, Giannarou S, 2020,

    Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation

    , 2020 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 11147-11154

    In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation to characterise the state of the tissue. However, scanning of large tissue surfaces in the presence of motion is a challenging task for the surgeon. Recently, robot-assisted local tissue scanning has been investigated for motion stabilisation of imaging probes to facilitate the capturing of good quality images and reduce the surgeon's cognitive load. Nonetheless, these approaches require the tissue surface to be static or translating with periodic motion. To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue motion. The 3D structure of the surgical scene is recovered, and a feature-based method is proposed to estimate the motion of the tissue in real-time. The desired scanning trajectory is manually defined on a reference frame and continuously updated using projective geometry to follow the tissue motion and control the movement of the robotic arm. The advantage of the proposed method is that it does not require the learning of the tissue motion prior to scanning and can deal with free-form motion. We deployed this framework on the da Vinci ® surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning. Our framework can be easily extended to other probe-based imaging modalities.

  • Journal article
    Kim JA, Wales DJ, Yang G-Z, 2020,

    Optical spectroscopy for in vivo medical diagnosis—a review of the state of the art and future perspectives

    , Progress in Biomedical Engineering, Vol: 2, Pages: 042001-042001
  • Journal article
    Hooshmand S, Kargozar S, Ghorbani A, Darroudi M, Keshavarz M, Baino F, Kim H-Wet al., 2020,

    Biomedical Waste Management by Using Nanophotocatalysts: The Need for New Options

    , MATERIALS, Vol: 13
  • Journal article
    Huang B, Tsai Y-Y, Cartucho J, Vyas K, Tuch D, Giannarou S, Elson DSet al., 2020,

    Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe

    , International Journal of Computer Assisted Radiology and Surgery, Vol: 15, Pages: 1389-1397, ISSN: 1861-6410

    PurposeIn surgical oncology, complete cancer resection and lymph node identification are challenging due to the lack of reliable intraoperative visualization. Recently, endoscopic radio-guided cancer resection has been introduced where a novel tethered laparoscopic gamma detector can be used to determine the location of tracer activity, which can complement preoperative nuclear imaging data and endoscopic imaging. However, these probes do not clearly indicate where on the tissue surface the activity originates, making localization of pathological sites difficult and increasing the mental workload of the surgeons. Therefore, a robust real-time gamma probe tracking system integrated with augmented reality is proposed.MethodsA dual-pattern marker has been attached to the gamma probe, which combines chessboard vertices and circular dots for higher detection accuracy. Both patterns are detected simultaneously based on blob detection and the pixel intensity-based vertices detector and used to estimate the pose of the probe. Temporal information is incorporated into the framework to reduce tracking failure. Furthermore, we utilized the 3D point cloud generated from structure from motion to find the intersection between the probe axis and the tissue surface. When presented as an augmented image, this can provide visual feedback to the surgeons.ResultsThe method has been validated with ground truth probe pose data generated using the OptiTrack system. When detecting the orientation of the pose using circular dots and chessboard dots alone, the mean error obtained is 0.05∘and 0.06∘, respectively. As for the translation, the mean error for each pattern is 1.78 mm and 1.81 mm. The detection limits for pitch, roll and yaw are 360∘,360∘ and 8∘–82∘∪188∘–352∘.ConclusionThe performance evaluation results show that this dual-pattern marker can provide high detection rates, as well as more accurate pose estimation and a larger workspace than the previously proposed hyb

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