Augmented-Reality for Guiding Laparoscopic Surgery

1.研究背景的简要说明，包括潜在的影响胆石症是全世界最常见的外科问题之一，可导致各种不同的并发症，从胆绞痛到危及生命的情况，如上升性胆管炎和胰腺炎。有症状的胆结石是手术的常见适应症。磁共振胆胰管成像(MRCP)是一种用于检测胆道梗阻的非侵入性成像检查，已被推荐为发现胆总管结石的良好方法。然而，在切除胆囊的手术中，很难识别胆道解剖结构，而且可能会出现错误，造成严重的后果。这项研究是在NIHR患者安全研究合作的更广泛项目中启动的，我们的目标是使这种极其常见的手术更安全，并降低风险。我们之前提供了一个增强现实(AR)肝脏手术系统，在腹腔镜视频上显示术前计算机断层扫描(CT)的3D模型，以指导外科医生。我们将把这一系统推广到胆道手术中。从长远来看，这种系统的影响将是使手术对接受胆囊切除、肝脏切除或肾脏切除的患者更安全。目的和目的本研究的目的是提供影像指导，使外科医生能够更有信心地识别胆道结构。该项目的具体目标可能包括：MRCP扫描的图像处理和与其他术前图像的图像对齐从腹腔镜视频实时识别胆道解剖从腹腔镜视频或超声重建内部解剖术前数据与术中图像对齐提供可量化指标以辅助手术，例如深度到靶点3。研究方法的新颖性这些目标将需要机器学习方法的新应用，特别是深度学习。我们将采用流行的变压器网络进行视频图像处理，并采用强化学习来解决对齐问题。医学成像中的深度学习是一个快速发展的领域，每天都有新的算法出现。我们将与惠康/EPSRC介入和外科科学中心(Weiss)和医学图像计算中心(CMIC)的其他研究人员合作，开发最先进的方法。与EPSRC的战略和研究领域保持一致该项目与EPSRC医疗保健技术战略保持良好的一致性，并特别侧重于“发现和加快新干预措施的开发”。使用AR技术进行手术将提供更安全和更有针对性的干预。参与该项目的任何公司或合作者都是由国家卫生研究院(NIHR)在NIHR中心伦敦患者安全研究合作基金项下共同资助的。

1. Brief description of the context of the research including potential impactGallstone disease is one of the most common surgical problems worldwide and can cause a variety of different complications varying from biliary colic to life-threatening conditions such as ascending cholangitis and pancreatitis. Symptomatic gallstones are a common indication for surgery. Magnetic Resonance Cholangiopancreatography (MRCP) is a non-invasive imaging test that is used to detect biliary obstruction and has been recommended as a good way of detecting common bile duct stones. However, during surgery for removing gallbladder (cholecystectomy) it is difficult to identify biliary anatomy, and errors can occur with dire consequences.This research has been initiated within the wider project of the NIHR Patient Safety Research Collaboration, and we will aim to make this extremely common surgery safer and to reduce risks.We have previously delivered a system for Augmented Reality (AR) liver surgery, that displays a 3D model from pre-operative Computed Tomography (CT) scans on top of laparoscopic video, to guide the surgeon. We will extend this system for the purpose of gall bladder surgery. In the longer term, the impact of such a system would be to make surgery safer for patients undergoing gallbladder removal, liver resection or kidney excision.2. Aims and ObjectivesThe aim is to provide image-guidance to enable the surgeon to identify biliary structures with more confidence. The specific aims of the project may include:Image processing of MRCP scans and image-alignment with other pre-operative imagingReal-time identification of biliary anatomy from laparoscopic videoReconstruction of internal anatomy from laparoscopic video or ultrasoundAlignment of pre-operative data to intra-operative imagingProvision of quantifiable metrics to aid during surgery, e.g. depth to target3. Novelty of Research MethodologyThese objectives will require the novel application of machine learning methods and particularly deep learning. We will adapt popular transformer networks for video image-processing and reinforcement learning for the alignment problem. Deep learning in medical imaging is a fast moving field, with novel algorithms appearing daily. We will work with other researchers at the Wellcome/EPSRC Centre of Interventional and Surgical Sciences (WEISS) and Centre for Medical Image Computing (CMIC) to develop state of the art methods.4. Alignment to EPSRC's strategies and research areasThe project is well aligned with EPSRC Healthcare Technologies Strategy, and specifically focusses on the "discovering and accelerating the development of new interventions". The use of AR technologies for surgery will provide safer and more targeted interventions.5. Any companies or collaborators involvedThe project is co-funded by the National Institute of Health Research (NIHR) under the NIHR Central London Patient Safety Research Collaboration grant.