Pushing the limits of detection with PET

突破 PET 检测极限

• 批准号: RGPIN-2020-04741
• 负责人: Klein, Ran
• 金额: $ 2.4万
• 依托单位: University of Ottawa
• 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=716928
• 关键词: Pushing; limits; detection; PET

Hybrid positron emission tomography (PET) and x-ray computed tomography (CT) is the most sensitive non-invasive medical imaging modality for detecting many types of cancer. With ongoing improvements in PET/CT devices and image reconstruction algorithms, the interpreting physicians have become a key factor limiting diagnostic accuracy, clinical throughput and cost. Artificial intelligence (AI), especially based on machine-learning (ML), is promising to aid or even supplant physicians. But AI development in PET has been lagging, largely due to insufficient image data with well labelled lesions. Our goal is to objectively optimize PET/CT for the task of lesion detection. To that end, we have been developing methods for synthesizing realistic and accurately characterized lesions into PET/CT of real patients. We can therefore generate image libraries with accurate ground truth of lesion properties on an unprecedented scale to fuel development of powerful new lesion detection AI. Furthermore, by manipulating lesion properties, we can test the limits-of-detection (LOD). LOD measures can inform us how to optimize PET and how to integrate between physicians and AI to improve detection of cancer. This research program addresses the following objectives: 1) Develop the tools to easily generate well characterized, realistic lesions in real patient data acquired on current and future PET systems. 2) Establish parametric models for objectively quantifying the LOD. 3) Identify and optimize key factors influencing lesion detectability including: image reconstruction methods, image display technologies, conditions of the human-reader and image reading strategies. 4) Developing lesion detection AI using very large libraries of synthetic lesions and apply LOD as figure-of-merit to compare between AI and human observer. The research program leverages existing collaborations with industry, to accelerate research and to foster technology transfer to clinical practice. The program emphasizes collaboration with subject matter experts in the domains of PET simulation, PET reconstruction, psychophysics and virtual-reality technologies. The program will create freely-available repositories of synthetic lesion images for use by others to foster innovation and to enable benchmarking of lesion detection solutions, including our own. Trainees in this program will be enrolled in the departments of Physics and Engineering at Carleton University and the University of Ottawa. The program is attractive to top-tier graduate and post-graduate level researchers due to industry collaboration and use of trending technologies including AI, ML, image analysis, simulation and virtual-reality. Trainees will be embedded in an active clinical environment and will benefit from frequent interactions with researchers and clinicians. Diversity and inclusion are emphasized during student and research participant enrollment, and through research collaborations.

混合正电子发射断层扫描（PET）和X射线计算机断层扫描（CT）是检测许多类型癌症的最灵敏的非侵入性医学成像模式。随着PET/CT设备和图像重建算法的不断改进，解释医生已成为限制诊断准确性、临床吞吐量和成本的关键因素。人工智能（AI），特别是基于机器学习（ML）的人工智能，有望帮助甚至取代医生。但PET中的AI开发一直滞后，主要是由于标记良好的病变的图像数据不足。 我们的目标是客观地优化PET/CT的病变检测任务。为此，我们一直在开发将逼真且准确表征的病变合成到真实的患者的PET/CT中的方法。因此，我们可以以前所未有的规模生成具有准确的病灶属性基础事实的图像库，以推动强大的新病灶检测AI的开发。此外，通过操纵病变属性，我们可以测试检测限（LOD）。LOD测量可以告诉我们如何优化PET，以及如何在医生和AI之间进行整合，以改善癌症检测。 该研究计划涉及以下目标： 1)开发工具，以便在当前和未来PET系统上采集的真实的患者数据中轻松生成表征良好的真实病变。 2)建立参数化模型，客观量化LOD。 3)识别并优化影响病变可检测性的关键因素，包括：图像重建方法、图像显示技术、阅片人条件和图像阅读策略。 4)使用非常大的合成病变库开发病变检测AI，并将LOD作为品质因数，以比较AI和人类观察者。 该研究计划利用与行业的现有合作，加速研究并促进技术转移到临床实践。该计划强调与PET模拟，PET重建，心理物理学和虚拟现实技术领域的主题专家合作。该计划将创建合成病变图像的免费存储库，供他人使用，以促进创新，并实现病变检测解决方案的基准测试，包括我们自己的。 该计划的学员将在卡尔顿大学和渥太华大学的物理和工程系就读。该计划对顶级研究生和研究生水平的研究人员具有吸引力，因为行业合作和使用趋势技术，包括AI，ML，图像分析，模拟和虚拟现实。学员将被嵌入在一个活跃的临床环境中，并将受益于与研究人员和临床医生的频繁互动。多样性和包容性强调在学生和研究参与者招生，并通过研究合作。