Pushing the limits of detection with PET

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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.