Intelligent imaging informatics for surgery

手术智能成像信息学

• 批准号: RGPIN-2015-06639
• 负责人: Khan, Ali
• 金额: $ 1.97万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708811
• 关键词: Intelligent; imaging; informatics; surgery

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.

脑外科手术治疗耐药性癫痫或脑癌是非常具有挑战性的，因为外科医生需要在完全切除疾病和保留健康的功能组织之间做出权衡。计划治疗大脑的多少需要神经通路和异常区域的知识，然而这可能很困难，因为边界并不总是很明确。错误地确定要治疗的区域可能会带来很大的风险；如果过小，手术效果不佳，如果过大，功能可能会发生负面改变。磁共振成像（MRI）试图非侵入性地解决这些问题，然而，缺失的拼图部分是在手术期间和之后收集的信号信息的整合。临床医生通常依靠这些信号信息，如放置在大脑上的电极的功能记录，或组织的显微图像，来确认只能用MRI来怀疑的东西。将这些生物医学信号与MR成像融合在一起，闭合了环路，使我们能够更深入地了解如何使用图像分析来精确地突出目标。