Pushing the limits of detection with PET

混合正电子发射断层扫描（PET）和x射线计算机断层扫描（CT）是检测多种癌症的最灵敏的非侵入性医学成像方式。随着PET/CT设备和图像重建算法的不断改进，口译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），尤其是基于机器学习（ML）的人工智能，有望帮助甚至取代医生。但人工智能在PET领域的发展一直滞后，主要原因是病变标记良好的图像数据不足。我们的目标是客观地优化PET/CT的病变检测任务。为此，我们一直在开发将真实且准确表征病变的方法合成为真实患者的PET/CT。因此，我们可以以前所未有的规模生成具有准确病灶属性的图像库，以推动强大的新型病变检测AI的发展。此外，通过控制病变特性，我们可以测试检测限（LOD）。LOD措施可以告诉我们如何优化PET，以及如何整合医生和人工智能来提高癌症的检测。本研究计划旨在实现以下目标：1)开发工具，以便在当前和未来PET系统上获得的真实患者数据中轻松生成具有良好特征的真实病变。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、人类读者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作为人工智能与人类观察者之间比较的优值。该研究项目利用与工业界的现有合作，加速研究并促进技术向临床实践的转移。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。该项目将创建免费的合成病变图像存储库，供其他人使用，以促进创新，并实现病变检测解决方案的基准测试，包括我们自己的解决方案。该项目的学员将被卡尔顿大学和渥太华大学的物理系和工程系录取。由于行业合作以及人工智能、机器学习、图像分析、仿真和虚拟现实等趋势技术的使用，该项目对顶级研究生和研究生水平的研究人员具有吸引力。受训者将融入活跃的临床环境，并将受益于与研究人员和临床医生的频繁互动。在学生和研究参与者入学期间以及通过研究合作，强调多样性和包容性。

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.