Intelligent imaging informatics for surgery

治疗耐药性癫痫或脑癌的脑外科手术非常具有挑战性，因为外科医生需要在完全切除疾病和保留健康功能组织之间进行权衡。 计划治疗多少大脑需要了解神经通路和异常区域，但这可能很困难，因为边界并不总是很好地定义。 不正确地定义要治疗的区域可能会带来很大的风险;如果太小，手术将不会有效，如果太大，功能可能会发生负面改变。磁共振成像（MRI）试图以非侵入性的方式解决这些问题，然而，缺少的拼图是手术期间和手术后收集的信号信息的整合。临床医生通常依赖于这种信号信息，例如放置在大脑上的电极的功能记录，或组织的显微图像，以确认只能用MRI怀疑的东西。 将这些生物医学信号与MR成像融合在一起，使我们能够更深入地了解如何使用图像分析来精确突出目标。** 要了解这种融合的生物医学信息，我们需要解决三个关键挑战：1）鲁棒地提取关于大脑中哪些神经通路是功能性的并且需要避免的信息，2）找到在手术之前、期间和之后拍摄的图像之间的空间对应，以及3）使用构建到信息学数据库中的所有这些信息，来概述新患者的靶点以及该预测的不确定性。拟议的研究计划的目标是开发复杂的软件工具，以应对这些挑战。这项工作将是第一次系统地将手术中的功能和生物信息与术前图像进行对齐和融合，并将允许在局部范围内比较这些信息的前所未有的能力。在计算图像分析的贡献也将是更广泛的生物医学图像处理社区的意义，因为它们解决了许多关键的挑战，图像配准的鲁棒性和如何建模的不确定性。最终，这项工作将改变图像引导手术的执行方式，并为提供更精确和微创的治疗铺平道路。

Brain surgery to treat drug-resistant epilepsy or brain cancer is very challenging, as the surgeon needs to trade-off completely removing the disease, with sparing healthy functional tissue. Planning how much of the brain to treat requires knowledge of the neurological pathways and abnormal regions, however this can be difficult as the boundaries are not always well defined. Incorrectly defining the region to treat can carry a great deal of risk; if too small, the surgery will not be effective, and if too large, function may be altered negatively. Magnetic resonance imaging (MRI) has attempted to solve these problems non-invasively, however, the puzzle piece that has been missing is the integration of signal information that is collected during and after surgery. Clinicians commonly rely on this signal information, such as functional recordings from electrodes placed on the brain, or microscopic images of tissue, to confirm what can only be suspected with MRI. Fusing these biomedical signals with MR imaging closes the loop, and allows us to gain a deeper understanding of how image analysis can be used to precisely highlight the target. ******Gaining an understanding of this fused biomedical information requires us to address three key challenges: 1) robustly extracting information on what neurological pathways in the brain are functional and need to be avoided, 2) finding spatial correspondence between images taken before, during, and after surgery, and 3) using all this information, built into an informatics database, to outline the target in a new patient and the uncertainty in this prediction. The goal of the proposed research program is to develop sophisticated software tools that address these challenges. This work will be the first to systematically align and fuse functional and biological information from surgery with pre-operative images, and will allow for an unprecedented ability to compare this information at a local scale. The contributions in computational image analysis will also be of significance to the wider biomedical image processing community, as they address many key challenges related to robustness in image registration and how uncertainty is modelled. Ultimately, this work will transform how image-guided surgery is performed, and will pave the way for the delivery of more precise and minimally-invasive treatment.**