Quantitative Oncological PET Image Generation and Analysis

定量肿瘤 PET 图像生成和分析

• 批准号: RGPIN-2019-06467
• 负责人: Rahmim, Arman
• 金额: $ 3.64万
• 依托单位: University of British Columbia
• 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=715661
• 关键词: Quantitative; Oncological; PET; Image; Generation

Objectives: The long-term objective of my research program is to significantly advance quantitative tomographic nuclear medicine (both PET and SPECT imaging), and to help make quantitative analysis an integral part of routine practice, significantly improving assessment of cancer and to help make precision (personalized) therapy a reality. The short-term objective of my proposal is to develop and optimize advanced (i) data acquisition, (ii) image reconstruction, and (iii) image analysis methods as applied to PET imaging. Methods: I was recruited in 2018 from the Johns Hopkins University, where I led a program of high-resolution PET imaging physics for 13 years. My program at UBC / BC Cancer aims to develop powerful paradigms of quantitative PET imaging that can translate to routine imaging of cancer. Our research efforts will be on 3 frontiers: 1) Data acquisition: My lab pioneered the concept of dynamic whole-body (DWB) PET imaging, culminating in a PET vendor clinical product release (Multiparametric PET Suite by Siemens in 2017). At the same time, there is a challenge to this framework, in that routine PET scans are performed in increasingly short durations: we aim to investigate and discover novel protocols for DWB PET that can enable wide adoption in routine imaging. 2) Image reconstruction: We will develop and validate advanced 3D and spatiotemporal 4D reconstruction algorithms, including advanced models, dynamic as well as motion information. We will utilize machine learning, deep learning, dictionary learning, as well as kernel methods, to produce images with superior quality and quantitative accuracy. Alternatively, and importantly, we will study whether images of comparable quality can be obtained by significantly lower doses. 3) Image quantification: Our lab has been at the forefront of radiomics analysis, which generates shape and texture features to better quantify radiological images. In the proposed research program, we will pursue two frameworks: (i) use of explicitly-defined radiomic features followed by application of machine learning to construct advanced models for assessment of cancer; and (ii) direct use of deep learning methods to implicitly derive important patterns of uptake in PET. Impact: Our efforts aim to alter PET protocols as used in routine imaging, presently geared towards qualitative as opposed to quantitative assessment. Our proposed PET data acquisition and reconstruction methods enable generation of quantitative images that more appropriately reflect the underlying physiological processes. Our work also enables a paradigm where heterogeneous patterns of tumour shape and uptake are quantified in routine assessment (e.g. tumour hypoxia). Overall, our radiomics and machine learning efforts aim to alter the PET image processing and quantitation landscape, and to enable more comprehensive assessment of PET images, that can ultimately result in significant improvements in management and treatment of cancer.

目的：我的研究计划的长期目标是显著推进定量断层扫描核医学(包括PET和SPECT成像)，并帮助使定量分析成为常规实践中不可或缺的一部分，显著改善对癌症的评估，并帮助使精确(个性化)治疗成为现实。我建议的短期目标是开发和优化先进的(I)数据采集、(Ii)图像重建和(Iii)图像分析方法，如应用于PET成像。 方法：我于2018年从约翰·霍普金斯大学招聘，在那里我领导了一个高分辨率PET成像物理项目13年。我在不列颠哥伦比亚大学/不列颠哥伦比亚大学癌症研究所的项目旨在开发强大的定量PET成像范例，可以转化为癌症的常规成像。我们的研究工作将在三个方面展开： 1)数据获取：我的实验室率先提出了动态全身(DWB)PET成像的概念，最终推出了PET供应商的临床产品(西门子于2017年发布了多参数PET套件)。与此同时，这一框架面临着一个挑战，因为常规PET扫描的持续时间越来越短：我们的目标是研究和发现新的DWB PET协议，使其能够在常规成像中广泛采用。 2)图像重建：我们将开发和验证先进的3D和时空4D重建算法，包括先进的模型、动态和运动信息。我们将利用机器学习、深度学习、词典学习以及核心方法来生成高质量和定量准确的图像。或者，也是重要的，我们将研究是否可以用低得多的剂量获得质量相当的图像。 3)图像量化：我们实验室一直处于放射组学分析的前沿，它生成形状和纹理特征，以更好地量化放射图像。在拟议的研究计划中，我们将遵循两个框架：(I)使用明确定义的放射学特征，然后应用机器学习来构建用于评估癌症的高级模型；以及(Ii)直接使用深度学习方法来隐含地推导出PET中的重要摄取模式。 影响：我们的努力旨在改变常规成像中使用的PET方案，目前侧重于定性，而不是定量评估。我们建议的PET数据采集和重建方法能够生成更合适地反映潜在生理过程的定量图像。我们的工作还实现了一种范式，在这种范式中，在常规评估中(例如，肿瘤缺氧)，不同的肿瘤形状和摄取模式被量化。总体而言，我们的放射组学和机器学习努力旨在改变PET图像处理和量化的格局，并实现对PET图像的更全面评估，从而最终显著改善癌症的管理和治疗。