Deep Learning-based Scatter Estimation for Time-of-Flight PET

• Main Authors: Maier, J., Berker, Y., Schulte, A., Conti, M., Cabello, J., Bharkhada, D., Kalemis, A., Pan, L., Dimitrakopoulou-Strauss, A., Kachelrieb, M.
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Costs; Estimation; Microwave integrated circuits; Monte Carlo methods; Neural networks; Semiconductor detectors
• ISSN: 2577-0829
• DOI: 10.1109/NSS/MIC/RTSD57108.2024.10655823

Accurate scatter correction is crucial for quantitative positron emission tomography (PET). While the gold standard, Monte Carlo simulation, is usually too slow to be used routinely, faster alternatives often come at the cost of lower accuracy. To avoid this trade-off between accuracy and computational performance, deep learning-based approaches have recently indicated great potential. Here, we focus on the latest generation of PET scanners and provide an extension of prior work that is dedicated to time-of-flight PET in long axial field-of-view scanners. In particular, we train a U-net-like neural network to reproduce the outcome of our in-house Monte Carlo (MC) simulation as a function of the TOF PET emission data and the CT-based PET attenuation correction factors. Trained on data of 23 patients and tested on another 3 patients, our deep scatter estimation (DSE) yields scatter distributions that differ by 6.4% from the MC ground truth and clearly outperform single-scatter-simulations (error of 21.3%). These errors translate to 6.7% (DSE) and 24.6% (SSS) for scatter-corrected PET reconstructions, demonstrating the potential of DSE for fast and accurate scatter correction in clinical practice.