Imaging
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 articleKong L, Evans C, Su L, et al., 2022,
Special issue on translational biophotonics
, Journal of Physics D: Applied Physics, Vol: 55, ISSN: 0022-3727This special issue on 'Translational Biophotonics' was initiated when COVID-19 started to spread worldwide in early 2020, with the aim of introducing the advances in optical tools that have the ability to transform clinical diagnostics, surgical guidance, and therapeutic approaches that together can have a profound impact on global health. This issue achieves this goal comprehensively, covering various topics including optical techniques for clinical diagnostics, monitoring and treatment, in addition to fundamental studies in biomedicine.
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Journal articleWang D, Qi J, Huang B, et al., 2022,
Polarization-based smoke removal method for surgical images
, Biomedical Optics Express, Vol: 13, Pages: 2364-2364, ISSN: 2156-7085Smoke generated during surgery affects tissue visibility and degrades image quality, affecting surgical decisions and limiting further image processing and analysis. Polarization is a fundamental property of light and polarization-resolved imaging has been studied and applied to general visibility restoration scenarios such as for smog or mist removal or in underwater environments. However, there is no related research or application for surgical smoke removal. Due to differences between surgical smoke and general haze scenarios, we propose an alternative imaging degradation model by redefining the form of the transmission parameters. The analysis of the propagation of polarized light interacting with the mixed medium of smoke and tissue is proposed to realize polarization-based smoke removal (visibility restoration). Theoretical analysis and observation of experimental data shows that the cross-polarized channel data generated by multiple scattering is less affected by smoke compared to the co-polarized channel. The polarization difference calculation for different color channels can estimate the model transmission parameters and reconstruct the image with restored visibility. Qualitative and quantitative comparison with alternative methods show that the polarization-based image smoke-removal method can effectively reduce the degradation of biomedical images caused by surgical smoke and partially restore the original degree of polarization of the samples.
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Journal articleHan J, Davids J, Ashrafian H, et al., 2022,
A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches
, International Journal of Medical Robotics and Computer Assisted Surgery, Vol: 18, Pages: 1-11, ISSN: 1478-5951BackgroundFrom traditional open surgery to laparoscopic surgery and robot-assisted surgery, advances in robotics, machine learning, and imaging are pushing the surgical approach to-wards better clinical outcomes. Pre-clinical and clinical evidence suggests that automation may standardise techniques, increase efficiency, and reduce clinical complications.MethodsA PRISMA-guided search was conducted across PubMed and OVID.ResultsOf the 89 screened articles, 51 met the inclusion criteria, with 10 included in the final review. Automatic data segmentation, trajectory planning, intra-operative registration, trajectory drilling, and soft tissue robotic surgery were discussed.ConclusionAlthough automated surgical systems remain conceptual, several research groups have developed supervised autonomous robotic surgical systems with increasing consideration for ethico-legal issues for automation. Automation paves the way for precision surgery and improved safety and opens new possibilities for deploying more robust artificial intelligence models, better imaging modalities and robotics to improve clinical outcomes.
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Conference paperHan J, Gu X, Lo B, 2021,
Semi-supervised contrastive learning for generalizable motor imagery eeg classification
, 17th IEEE International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEEElectroencephalography (EEG) is one of the most widely used brain-activity recording methods in non-invasive brain-machine interfaces (BCIs). However, EEG data is highly nonlinear, and its datasets often suffer from issues such as data heterogeneity, label uncertainty and data/label scarcity. To address these, we propose a domain independent, end-to-end semi-supervised learning framework with contrastive learning and adversarial training strategies. Our method was evaluated in experiments with different amounts of labels and an ablation study in a motor imagery EEG dataset. The experiments demonstrate that the proposed framework with two different backbone deep neural networks show improved performance over their supervised counterparts under the same condition.
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Journal articleKedrzycki MS, Leiloglou M, Chalau V, et al., 2021,
The impact of temporal variation in indocyanine green administration on tumor identification during fluorescence guided breast surgery.
, Annals of Surgical Oncology, Vol: 28, Pages: 5617-5625, ISSN: 1068-9265BACKGROUND: On average, 21% of women in the USA treated with Breast Conserving Surgery (BCS) undergo a second operation because of close positive margins. Tumor identification with fluorescence imaging could improve positive margin rates through demarcating location, size, and invasiveness of tumors. We investigated the technique's diagnostic accuracy in detecting tumors during BCS using intravenous indocyanine green (ICG) and a custom-built fluorescence camera system. METHODS: In this single-center prospective clinical study, 40 recruited BCS patients were sub-categorized into two cohorts. In the first 'enhanced permeability and retention' (EPR) cohort, 0.25 mg/kg ICG was injected ~ 25 min prior to tumor excision, and in the second 'angiography' cohort, ~ 5 min prior to tumor excision. Subsequently, an in-house imaging system was used to image the tumor in situ prior to resection, ex vivo following resection, the resection bed, and during grossing in the histopathology laboratory to compare the technique's diagnostic accuracy between the cohorts. RESULTS: The two cohorts were matched in patient and tumor characteristics. The majority of patients had invasive ductal carcinoma with concomitant ductal carcinoma in situ. Tumor-to-background ratio (TBR) in the angiography cohort was superior to the EPR cohort (TBR = 3.18 ± 1.74 vs 2.10 ± 0.92 respectively, p = 0.023). Tumor detection reached sensitivity and specificity scores of 0.82 and 0.93 for the angiography cohort and 0.66 and 0.90 for the EPR cohort, respectively (p = 0.1051 and p = 0.9099). DISCUSSION: ICG administration timing during the angiography phase compared with the EPR phase improved TBR and diagnostic accuracy. Future work will focus on image pattern analysis and adaptation of the camera system to targeting fluorophores specific to breast cancer.
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