Optimizing (18)F-DPA-714 PET Image Reconstruction for Drug-resistant Focal Epilepsy: HYPER Iterative Versus Ordered Subset Expectation Maximization Algorithm in Image Quality and Epileptogenic Zone Detection.

内容摘要

Optimized reconstruction algorithms can enhance the image quality and signal-to-noise ratio (SNR) of positron emission tomography (PET) images. This study aimed to compare the impacts of the ordered subset expectation maximization (OSEM) and regularized expectation maximization image reconstruction (HYPER Iterative) algorithms on the image quality and epileptogenic zone (EZ) detection sensitivity in fluorine-18-DPA-714 (18F-DPA-714) images of drug-resistant focal epilepsy patients. Drug-resistant focal epilepsy patients who underwent presurgical brain 18F-DPA-714 PET imaging between May 2023 and July 2024 were retrospectively included. PET images were reconstructed using the OSEM (number of iterations ranging from 2 to 6) and HYPER Iterative algorithms [penalization factors (β) of 0.3, 0.5, and 0.8]. Image quality was evaluated by subjective (overall image quality, image noise, and EZ conspicuity) and quantitative assessment [mean standardized uptake value (SUVmean), SUVmax, contrast-to-noise ratio (CNR), SNR, and standardized uptake value ratio (SUVr)]. The sensitivity of detecting EZs in optimal PET images reconstructed by the OSEM and HYPER Iterative algorithms was compared, with histopathology as the gold standard. Thirty patients were included. The subjective assessment revealed that the HYPER Iterative algorithms outperformed OSEM in overall image quality, with HY0.8 achieving the highest score. Among OSEM algorithms, O5 had the top score and outperformed O6 significantly. Image noises scores were higher in HY0.5 and HY0.8 than in HY0.3 and OSEM algorithms. EZ conspicuity scores were better for HYPER Iteratives, O5, and O6 than for O2 - O4. Quantitative assessment revealed that the SNR was higher in O2, HY0.5, and HY0.8 than in O6, with HY0.8 significantly higher SNR than O5. The SUVr was higher in O6 and HY0.3 compared with O2. No significant differences were observed in SUVmean, SUVmax, mean standardized uptake value of cerebellar gray matter (SUVb) or CNR across all groups. For EZ detection, O5 and HY0.8 were optimal for the OSEM and HYPER Iterative algorithms, respectively. HY0.8 achieved a numerically 13.3% higher sensitivity for EZ detection than O5(80.0% vs. 66.7%), though this difference did not reach statistical significance (p = 0.125). The HYPER Iterative algorithm is capable of reducing image noise, enhancing EZ conspicuity, and improving the overall image quality in 18F-DPA-714 PET for patients with drug-resistant focal epilepsy, further increasing visual interpretability. A penalization factor of 0.8 results in a numerical improvement in the detection sensitivity of EZ, thus facilitating readers' confidence in detecting EZs. The online version contains supplementary material available at 10.1007/s13139-025-00981-7.