INSPIRE: Integration of Non-linear Sliding Processes into Image REgistration

本研究计划的目的是设计和研究新一代的非线性图像配准方法，使用一种新的约束优化方法的基础上集成的滑动运动的器官。尽管完全通用，但该方法将主要用于癌症CT成像中的呼吸肺运动补偿，其中与胸膜对接的肺的滑动运动在检测疾病和监测治疗或干预方面提出了特别具有挑战性的问题。我们相信，这种方法将在呼吸运动影响成像器官的广泛临床问题中找到应用。例如，可以跟踪乳房超声中肿块的滑动运动，以进行图像引导的活检。类似地，可以跟踪肝脏肿瘤以进行超声引导的靶向药物输送，从而补偿由于呼吸周期期间横膈膜收缩而导致的肝脏滑动。在该项目中开发的一个关键且完全新颖的想法是将配准约束为沿着肺表面的定向滑动运动，同时补偿由于在不同呼吸水平下发生的扩张或压缩而在肺内发生的变形。不是局部地正则化配准成本函数，而是将采取更有原则的方法，其嵌入肺的表面以及附近器官（如肝脏）的表面，以帮助以生理上合理的方式驱动配准过程。在配准中集成表面将使我们能够在平滑的运动场中对表面的滑动运动进行建模并因此恢复。我们的假设是，这种集成的，受约束的注册框架将在生理上更接地比目前，国家的最先进的运动校正方法，这在很大程度上是特设的。因此，拟议的研究将为改善诊断和疾病监测迈出重要一步。我们将证明我们的新的注册方法的好处，通过将其应用于呼吸运动校正的恶性胸膜间皮瘤患者，谁已经使用CT在化疗治疗过程中成像的连续CT肺癌成像。

The purpose of this research project proposal is to design and investigate a new generation of nonlinear image registration methodologies using a novel constrained optimization approach based on the integration of sliding motion of organs. Though completely generic, the approach will be explored primarily for respiratory lung motion compensation in cancer CT imaging, where the sliding motion of the lungs interfacing to the pleura poses a particularly challenging problem in detecting disease and in monitoring of treatment or intervention. We believe that this kind of approach will find application in a wide range of clinical problems where respiratory motion is affecting imaged organs. For example, the slipping motion of masses in breast ultrasound could be tracked for image-guided biopsies. Similarly, liver tumours could be tracked for ultrasound-guided targeted drug delivery, compensating for the sliding of the liver due to the diaphragm contraction during the breathing cycle. One key and entirely novel idea to be developed in this project is to constrain registration to directional sliding motion along the lung surface, while compensating for deformations occurring within the lungs due to expansion or compression occurring at varying levels of respiration. Rather than regularizing the registration cost function locally, a more principled approach will be taken which embeds surfaces of the lungs, and also of nearby organs like the liver, to help drive the registration process in a physiologically plausible manner. Integrating surfaces in the registration will enable us to model - and hence recover - slipping motion of surfaces within an otherwise smooth motion field. Our hypothesis is that this integrated, constrained registration framework will be physiologically more grounded than current, state-of-the-art motion correction approaches which are largely ad-hoc. Consequently the proposed research will provide a significant step towards improved diagnosis and disease monitoring. We will demonstrate the benefit of our new registration methodology by applying it to respiratory motion correction in serial CT lung cancer imaging for patients with malignant pleural mesothelioma, who have been imaged using CT over the course of chemotherapy treatment.