Quantitative Oncological PET Image Generation and Analysis

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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.