Correcting for Soft Tissue Deformation in Image-Guided Liver Surgery

描述（由申请人提供）：在某种程度上，使用软组织建模来更新图像引导导航系统尚未得到主流科学界的认可。它最近才在神经导航领域得到应用（尽管还没有可用的商业系统），并且仍在研究中。这种挫败感很大程度上不是由于方法论数量不断增加，而是由于对模型更新目标的误解以及无法测试和验证。对于前者，认为建模可以解释所有精细尺度变形的想法是天真的。然而，需要回答的问题是，在手术切缘的范围内，模型更新能否显着影响手术切除？这是本申请中要研究的中心研究假设。该应用的与众不同之处在于，如果实现了这些里程碑，其结果可能会导致用于图像引导肝脏手术系统的软组织变形校正系统，该系统可以立即投入商业用于患者护理。更具体地说，在这项工作结束时，将生成一种能够进行变形校正的图像引导肝脏手术系统，将建立这些校正保真度的初步经验，并且该技术将向获得机构审查委员会（IRB）批准的早期采用者商业化。这是可能的，因为该应用程序将利用 Pathfinder Therapeutics Incorporated (PTI) 正在进行的一项临床试验，该试验将于 2007 年下半年开始测试其图像引导肝脏手术 (IGLS) 系统。PTI 已同意分享其临床数据，以支持本文提出的新型组织变形校正策略。模型可用于校正 IGLS 内变形的假设将得到三个具体目标的支持，其中包括：（1）开发变形补偿策略，该策略涉及将驻留在适应性变形校正计算节点上的组合配准和形状校正技术，（2）根据来自三个独立临床试验的数据对该方法进行回顾性测试，以及（3）改进更新过程的计算机模型的研究。该应用程序的第二阶段将涉及升级早期采用者的系统，以包含我们的变形校正计算节点，然后前瞻性地在临床上测试其保真度。肝脏内的原发性和转移性癌症变得越来越常见。有重要证据表明，腹腔内肝脏手术可以延长患有转移性疾病的患者的生存时间。目前，由于该手术的复杂性，患者人数受到限制。更好的可视化和指导将为外科医生提供更多信心，并增加这些患者的手术候选人数量和结果。如果这一应用成功，这将导致第一个商用图像引导肝脏手术系统能够矫正软组织变形。所提出的“变形校正计算节点”将通过轻松适应具有类似数据的其他手术系统而产生更广泛的影响。此外，如果可以获得有关器官形状的信息，该策略也将与微创手术兼容。目前，矫正软组织变形的唯一商业手段是使用术中磁共振 (iMR) 和计算机断层扫描 (iCT)。这些系统的费用相当高，需要人员，并且维护成本高昂。由于其笨重的性质，患者的吞吐量也比传统手术室要少得多。 iCT 自 1980 年代中期以来一直可用，iMR 自 1990 年代中期以来一直可用，但全世界仍然只有少数系统在使用。虽然这些都是缺点，但应该指出的是，这些系统不会被放弃，并将继续开发。然而，这些设施很可能成为最危急病例的转诊中心，而不是作为主流技术提供。使用“变形校正计算节点”增强现有图像引导系统的策略成本非常低，可能与 iMR/iCT 解决方案一样有效，并且可移植到任何具有图像引导手术系统的医疗中心。该应用程序将在弥补这些稀疏的转诊中心与普通大众可用的各种当地医疗中心之间的脱节方面发挥重要作用。

DESCRIPTION (provided by applicant): To a degree, the use of soft-tissue modeling for updating image-guided navigational systems has not been embraced by the mainstream scientific community. It has only just recently found application within the neuronavigation community (although no commercial systems are available yet) and is still under investigation. Much of this frustration is not due to the growing number of methodologies but rather to a misunderstanding of the goals of model-updating, and an inability to test and validate. With respect to the former, it is na¿ve to believe that modeling can account for all fine-scale deformations. However, the question to be answered is, within the confines of surgical margin, can model-updating significantly impact surgical resection? This is a central research hypothesis to be investigated within this application. What sets this application apart is that if the milestones are achieved, the outcome could result in a soft-tissue deformation correction system for image- guided liver surgery systems that could be immediately commercially available for patient care. More specifically, at the conclusion of this work, an image-guided liver surgical system capable of deformation correction will be generated, a preliminary experience with the fidelity of those corrections will be established, and the technology will be commercially available to early adopters that secure approval from their Institutional Review Board (IRB). This is possible because this application will leverage an ongoing clinical trial being performed by Pathfinder Therapeutics Incorporated (PTI) that is in the process of testing their image-guided liver surgery (IGLS) system to start in the latter half of 2007. PTI has agreed to share their clinical data to support the novel tissue deformation correction strategy proposed herein. The hypothesis that models can be used to correct for deformation within IGLS will be supported by three specific aims which involve: (1) the development of deformation compensation strategy that involves a combined registration and shape correction technique that will reside on an adaptable deformation correction compute node, (2) the retrospective testing of this approach on data from three separate clinical trials, and (3) an investigation to improve the computer model for the updating process. Phase II for this application would involve upgrading the systems of the early adopters to include our deformation correction compute node and then prospectively test its fidelity clinically. Primary and metastatic cancer within the liver is becoming increasingly common. There is significant evidence that intra-abdominal liver surgery improves survival times for patients afflicted with metastatic disease. Currently the patient population is limited largely due to the complexity of this procedure. Better visualization and guidance would provide surgeons more confidence and would increase the number of surgical candidates and outcome for these patients. If this application is successful, it would lead to the first commercially available image-guided liver surgery system capable of soft-tissue deformation correction. The proposed "deformation correction compute node" would have more widespread impact by being readily adaptable to other surgical systems with similar data. In addition, the strategy would also be compatible with minimally invasive surgeries provided that information regarding organ shape can be acquired. Currently, the only commercial means to correct for soft-tissue deformation is to use intraoperative magnetic resonance (iMR) and computed tomography (iCT). These systems are of considerable expense, require staff, and can be costly to maintain. Due to their cumbersome nature, the patient through-put is also considerably less than a conventional operating room. iCT has been available since the mid-1980's and iMR has been available since the mid- 1990's, yet there are still only a handful of systems being used throughout the world. While these are disadvantages, it should be noted that these systems will not be dispensed with and will continue to be developed. However, it is highly probable that these facilities will become referral centers for the most critical cases rather than available as a mainstream technology. The strategy of augmenting an existing image- guidance system with a "deformation correction compute node" is very low cost, may be as effective as the iMR/iCT solution, and is translatable to any medical center with an image-guided surgery system. This application will play an important role in remedying a disconnection between these sparse referral centers and the vast assortment of local medical centers available to the general population.