Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images

作者：Qianyi Yang, Wenbo Li, Zhenxing Huang, Zixiang Chen, Wenjie Zhao, Yunlong Gao, Xinlan Yang, Yongfeng Yang, Hairong Zheng, Dong Liang, Jianjun Liu, Ruohua Chen, Zhanli Hu · 发表于：EJNMMI Physics · 年份：2024 · DOI：10.1186/s40658-024-00698-0 · 被引用次数：2 · 研究领域：Medical Imaging Techniques and Applications、Peptidase Inhibition and Analysis、Prostate Cancer Treatment and Research

Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ( $$\:\ge\:$$ 60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time. On the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1–6 and frames 25–30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: $$\:{K}_{1}$$ , 68Ga-PSMA: $$\:{K}_{i}$$ ) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model. Both the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.7...