A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.
Purpose: Prolonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images from dual-time-window protocols, thereby shortening...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 4; pp. 1448 - 1460 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Mar2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183131176&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183131176 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Mar2025 vid: 52 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183131176 181697987 10.1007/s00259-024-07012-1 183131176 ppf: 1448 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A deep learning method for total-body dynamic PET imaging with dual-time-window protocols. aug: au: Ding, Wenxiang Wang, Hanzhong Qiao, Xiaoya Li, Biao Huang, Qiu affil: https://ror.org/0220qvk04 School of Biomedical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China sug: ab: Purpose: Prolonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images from dual-time-window protocols, thereby shortening the scanning time. Methods: This study includes 70 patients (mean age ± standard deviation, 53.61 ± 13.53 years; 32 males) diagnosed with pulmonary nodules or breast nodules between 2022 to 2024. Each patient underwent a 65-min dynamic total-body [18F]FDG PET/CT scan. Acquisitions using early-stop protocols and dual-time-window protocols were simulated to reduce the scanning time. To predict the missing frames, we developed a bidirectional sequence-to-sequence model with attention mechanism (Bi-AT-Seq2Seq); and then compared the model with unidirectional or non-attentional models in terms of Mean Absolute Error (MAE), Bias, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) of predicted frames. Furthermore, we reported the comparison of concordance correlation coefficient (CCC) of the kinetic parameters between the proposed method and traditional methods. Results: The Bi-AT-Seq2Seq significantly outperform unidirectional or non-attentional models in terms of MAE, Bias, PSNR, and SSIM. Using a dual-time-window protocol, which includes a 10-min early scan followed by a 5-min late scan, improves the four metrics of predicted dynamic images by 37.31%, 36.24%, 7.10%, and 0.014% respectively, compared to the early-stop protocol with a 15-min acquisition. The CCCs of tumor' kinetic parameters estimated with recovered full time-activity-curves (TACs) is higher than those with abbreviated TACs. Conclusion: The proposed algorithm can accurately generate a complete dynamic acquisition (65 min) from dual-time-window protocols (10 + 5 min). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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