Non-invasive quantification of the brain [18F]FDG-PET using inferred blood input function learned from total-body data with physical constraint
Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for Ki (K1). In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:PP |
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| Enthalten in: |
IEEE transactions on medical imaging - PP(2024) vom: 22. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Zhenguo [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 22.02.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1109/TMI.2024.3368431 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368765873 |
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| 520 | |a Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for Ki (K1). In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Wu, Yaping |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Zeheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xinyi |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiaochen |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Hairong |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yongfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shanshan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Meiyun |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Tao |e verfasserin |4 aut | |
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