Ultrafast Multi-tracer Total-body PET Imaging Using a Transformer-Based Deep Learning Model.
Authors
Affiliations (6)
Affiliations (6)
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211 Geneva, Switzerland (H.S., A.S., Y.S., C.E.D., C.I., H.Z.); Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.).
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211 Geneva, Switzerland (H.S., A.S., Y.S., C.E.D., C.I., H.Z.).
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.).
- Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Department of Nuclear Medicine, Nanfang Hospital Southern Medical University, Guangzhou 510515, China (Y.H., H.W.).
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China (H.S., W.Y., L.L.); Pazhou Lab, Guangzhou 510330, China (L.L.).
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211 Geneva, Switzerland (H.S., A.S., Y.S., C.E.D., C.I., H.Z.); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, Netherlands (H.Z.); Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark (H.Z.); University Research and Innovation Center, Óbuda University, Budapest, Hungary (H.Z.). Electronic address: [email protected].
Abstract
Reducing PET scan acquisition time to minimize motion-related artifacts and improving patient comfort is always demanding. This study proposes a deep-learning framework for synthesizing diagnostic-quality PET images from ultrafast scans in multi-tracer total-body PET imaging. A retrospective analysis was conducted on clinical uEXPLORER PET/CT datasets from a single institution, including [<sup>18</sup>F]FDG (N=50), [<sup>18</sup>F]FAPI (N=45) and [<sup>68</sup>Ga]FAPI (N=60) studies. Standard 300-s acquisitions were performed for each patient, with ultrafast scan PET images (3, 6, 15, 30, and 40 s) generated through list-mode data truncation. We developed two variants of a 3D SwinUNETR-V2 architecture: Model 1 (PET-only input) and Model 2 (PET+CT fusion input). The proposed methodology was trained and tested on all three datasets using 5-fold cross-validation. The proposed Model 1 and Model 2 significantly enhanced subjective image quality and lesion detectability in multi-tracer PET images compared to the original ultrafast scans. Model 1 and Model 2 also improved objective image quality metrics. For the [¹⁸F]FDG datasets, both approaches improved peak signal-to-noise ratio (PSNR) metrics across ultra-short acquisitions: 3 s: 48.169±6.121 (Model 1) vs. 48.123±6.103 (Model 2) vs. 44.092±7.508 (ultrafast), p < 0.001; 6 s: 48.997±5.960 vs. 48.461±5.897 vs. 46.503±7.190, p < 0.001; 15 s: 50.310±5.674 vs. 50.042±5.734 vs. 49.331±6.732, p < 0.001. The proposed Model 1 and Model 2 effectively enhance image quality of multi-tracer total-body PET scans with ultrafast acquisition times. The predicted PET images demonstrate comparable performance in terms of image quality and lesion detectability.