Quantitative Oncological PET Image Generation and Analysis

目标：我的研究计划的长期目标是显着推进定量断层扫描核医学（PET 和 SPECT 成像），并帮助定量分析成为常规实践的一个组成部分，显着改善癌症评估并帮助实现精准（个性化）治疗。我提案的短期目标是开发和优化应用于 PET 成像的先进 (i) 数据采集、(ii) 图像重建和 (iii) 图像分析方法。******方法：我于 2018 年从约翰霍普金斯大学招聘，在那里我领导了高分辨率 PET 成像物理项目 13 年。我在 UBC / BC Cancer 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。我们的研究工作将集中在 3 个前沿领域：*** 1) 数据采集：我的实验室率先提出了动态全身 (DWB) PET 成像的概念，最终发布了 PET 供应商临床产品（西门子于 2017 年推出的多参数 PET 套件）。与此同时，这个框架也面临着挑战，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 DWB PET 的新方案，从而能够在常规成像中得到广泛采用。*** 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、字典学习以及核方法来生成具有卓越质量和定量准确性的图像。另外，重要的是，我们将研究是否可以通过显着降低剂量来获得质量相当的图像。*** 3) 图像量化：我们的实验室一直处于放射组学分析的前沿，可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，我们将追求两个框架：（i）使用明确定义的放射组学特征，然后应用机器学习来构建用于评估癌症的先进模型；影响：我们的努力旨在改变常规成像中使用的 PET 方案，目前该方案面向定性而不是定量评估。我们提出的 PET 数据采集和重建方法能够生成更恰当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例，在常规评估（例如肿瘤缺氧）中量化肿瘤形状和摄取的异质模式。总体而言，我们的放射组学和机器学习工作旨在改变 PET 图像处理和定量格局，并对 PET 图像进行更全面的评估，最终可以显着改善癌症的管理和治疗。 **

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.**