Positron emission tomography (PET) is a widely used imaging modality for the diagnosis and treatment of oncologic diseases. In this study, we evaluated the performance of digital PET/CT systems using subcentimeter microsphere inserts in a NEMA IEC Body Phantom. The digital system was compared with a non-digital PET scanner using the same image reconstruction method. Results revealed that the digital system maintained higher detectability for smaller spheres with an average of 1 Likert score higher for lesions under 7.9mm, indicating its ability to detect smaller lesions more effectively than the non-digital system. Furthermore, we observed that the drop-off in contrast recovery occurs at smaller microspheres in the digital PET system compared with that for a non-digital PET scanner. This suggests that digital PET may require the use of smaller spheres in image quality testing to ensure accurate comparison of performance between digital systems. This implies that digital systems can more accurately and effectively distinguish subtle differences in image intensity and spatial distributions of intensity, leading to improved lesion visibility and detection, which is likely due to the superior imaging characteristics offered by underlying detection technology.
PET-CT scans using 18F-FDG are increasingly used to detect cancer, but interpretation can be challenging due to non-specific uptake and complex anatomical structures nearby. To aide this process, we investigate the potential of automated detection of lesions in 18F-FDG scans using deep learning tools. A 5-layer convolutional neural network (CNN) with 2x2 kernels, rectified linear unit (ReLU) activations and two dense layers was trained to detect cancerous lesions in 2D axial image segments from PET scans. Pre-contoured scans from a retrospective cohort study of 480 oesophageal cancer patients were split 80:10:10 into training, validation and test sets. These were then used to generate a total of ~14000 45×45 pixel image segments, where tumor present segments were centered on the marked lesion, and tumor absent segments were randomly located outside the marked lesion. ROC curves generated from the training and validation datasets produced an average AUC of ~<95%.
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