Image guided surgery through spatio-temporal signal amplification

点击翻译按钮获取中文摘要

Through advances in instrumentation and high resolution digital video, surgical techniques are increasingly becoming more minimally invasive. Reducing the access trauma of surgery has many advantages for the patient such as reduced hospitalisation, scarring, co-morbidity and post-operative pain. However, limiting the surgeon's access to the surgical site inevitably increases the complexity of operations. Clinically, it is crucial to enhance visualisation during minimally invasive surgery and in particular to enable the surgeon to see structures underneath the exposed organ surface and to observe the functional characteristics of tissues. The availability of this information in real-time during surgery can assist the surgeon to prevent damage to critical anatomical structures and to preserve the viability of healthy tissues. Information about the location of blood vessels is actually inherently embedded in the endoscopic video signal from minimally invasive surgery in the form of motion. This can be observed easily when the vessel is large and near the tissue's surface. However, when the vessel is small and embedded within the tissue, the motion may be very subtle and not naturally visible by the naked eye because the human visual system is tuned to specific frequencies and motion amplitudes. Similar variations are present in the radiometric channels of endoscopic video, where colour fluctuations are surrogate measures of changes in tissue perfusion linked to the cardiac cycle. These subtle spatio-temporal video variations can be computationally detected and measured which constitutes the focus of the proposed project. The difficulty in exposing subtle variations in endoscopic video is that the surgical site is highly deformable and dynamic, which typically obscures the location of sub-surface vessels. To compensate for large scene dynamics, we will use a combination of registration and tracking techniques that temporally align specific regions of tissue. Once small variations have been identified, observations from different angles acquired either by a stereo endoscope or by a moving device will facilitate the localisation of vessels under the tissue. Mathematically, the theory of sparsity regularization and non-smooth regularisation will be exploited to solve for the vessel position leveraging existing research in tomography imaging e.g. using non-convex penalties and adaptive algorithms.The computational techniques to be developed, in the form of inverse problems for recovering and localising the source of subtle motion or colour variations, have wide applicability to many image and vision computing problems. In particular, applications where large dynamic effects need to be considered and removed apriori and where the problem is under determined due to the complexity of the structures under investigation.