Image guided surgery through spatio-temporal signal amplification

通过仪器和高分辨率数字视频的进步，手术技术越来越微创。减少手术创伤对患者有许多好处，例如减少住院、疤痕、合并症和术后疼痛。然而，限制外科医生进入手术部位不可避免地增加了手术的复杂性。在临床上，在微创手术期间增强可视化是至关重要的，特别是使外科医生能够看到暴露的器官表面下的结构并观察组织的功能特征。在手术过程中实时获得这些信息可以帮助外科医生防止对关键解剖结构的损伤并保持健康组织的活力。 关于血管位置的信息实际上以运动的形式固有地嵌入在来自微创手术的内窥镜视频信号中。当血管较大且靠近组织表面时，这可以容易地观察到。然而，当血管很小并且嵌入组织内时，运动可能非常微妙并且不能被肉眼自然地看到，因为人类视觉系统被调谐到特定的频率和运动幅度。在内窥镜视频的辐射测量通道中也存在类似的变化，其中颜色波动是与心动周期相关的组织灌注变化的替代测量。这些微妙的时空视频变化可以通过计算检测和测量，这构成了所提出的项目的重点。在内窥镜视频中暴露细微变化的困难在于手术部位是高度可变形的和动态的，这通常掩盖了表面下血管的位置。为了补偿大场景动态，我们将使用配准和跟踪技术的组合，在时间上对齐组织的特定区域。一旦识别出微小的变化，通过立体内窥镜或移动设备从不同角度获取的观察结果将有助于组织下血管的定位。在数学上，将利用稀疏正则化和非光滑正则化理论来解决血管位置，利用断层成像中的现有研究，例如使用非凸罚分和自适应算法。将开发的计算技术，以逆问题的形式，用于恢复和定位细微运动或颜色变化的来源，对许多图像和视觉计算问题具有广泛的适用性。特别是，需要考虑和消除大的动态效应的应用程序的先验和问题是根据确定由于调查的结构的复杂性。

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.