Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study
This study aimed to investigate the feasibility of positron range correction based on three different convolutional neural network (CNN) models in preclinical PET imaging of Ga-68. The first model (CNN1) was originally designed for super-resolution recovery, while the second model (CNN2) and the thi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/12/2275 |
| _version_ | 1797505488845275136 |
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| author | Ching-Ching Yang |
| author_facet | Ching-Ching Yang |
| author_sort | Ching-Ching Yang |
| collection | DOAJ |
| description | This study aimed to investigate the feasibility of positron range correction based on three different convolutional neural network (CNN) models in preclinical PET imaging of Ga-68. The first model (CNN1) was originally designed for super-resolution recovery, while the second model (CNN2) and the third model (CNN3) were originally designed for pseudo CT synthesis from MRI. A preclinical PET scanner and 30 phantom configurations were modeled in Monte Carlo simulations, where each phantom configuration was simulated twice, once for Ga-68 (CNN input images) and once for back-to-back 511-keV gamma rays (CNN output images) with a 20 min emission scan duration. The Euclidean distance was used as the loss function to minimize the difference between CNN input and output images. According to our results, CNN3 outperformed CNN1 and CNN2 qualitatively and quantitatively. With regard to qualitative observation, it was found that boundaries in Ga-68 images became sharper after correction. As for quantitative analysis, the recovery coefficient (RC) and spill-over ratio (SOR) were increased after correction, while no substantial increase in coefficient of variation of RC (CV<sub>RC</sub>) or coefficient of variation of SOR (CV<sub>SOR</sub>) was observed. Overall, CNN3 should be a good candidate architecture for positron range correction in Ga-68 preclinical PET imaging. |
| first_indexed | 2024-03-10T04:19:18Z |
| format | Article |
| id | doaj.art-bdc5adab87234ea5a71790fb5785146e |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T04:19:18Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-bdc5adab87234ea5a71790fb5785146e2023-11-23T07:53:49ZengMDPI AGDiagnostics2075-44182021-12-011112227510.3390/diagnostics11122275Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation StudyChing-Ching Yang0Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 807, TaiwanThis study aimed to investigate the feasibility of positron range correction based on three different convolutional neural network (CNN) models in preclinical PET imaging of Ga-68. The first model (CNN1) was originally designed for super-resolution recovery, while the second model (CNN2) and the third model (CNN3) were originally designed for pseudo CT synthesis from MRI. A preclinical PET scanner and 30 phantom configurations were modeled in Monte Carlo simulations, where each phantom configuration was simulated twice, once for Ga-68 (CNN input images) and once for back-to-back 511-keV gamma rays (CNN output images) with a 20 min emission scan duration. The Euclidean distance was used as the loss function to minimize the difference between CNN input and output images. According to our results, CNN3 outperformed CNN1 and CNN2 qualitatively and quantitatively. With regard to qualitative observation, it was found that boundaries in Ga-68 images became sharper after correction. As for quantitative analysis, the recovery coefficient (RC) and spill-over ratio (SOR) were increased after correction, while no substantial increase in coefficient of variation of RC (CV<sub>RC</sub>) or coefficient of variation of SOR (CV<sub>SOR</sub>) was observed. Overall, CNN3 should be a good candidate architecture for positron range correction in Ga-68 preclinical PET imaging.https://www.mdpi.com/2075-4418/11/12/2275Ga-68 preclinical PET imagingpositron range correctionconvolutional neural network |
| spellingShingle | Ching-Ching Yang Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study Diagnostics Ga-68 preclinical PET imaging positron range correction convolutional neural network |
| title | Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study |
| title_full | Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study |
| title_fullStr | Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study |
| title_full_unstemmed | Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study |
| title_short | Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study |
| title_sort | compensating positron range effects of ga 68 in preclinical pet imaging by using convolutional neural network a monte carlo simulation study |
| topic | Ga-68 preclinical PET imaging positron range correction convolutional neural network |
| url | https://www.mdpi.com/2075-4418/11/12/2275 |
| work_keys_str_mv | AT chingchingyang compensatingpositronrangeeffectsofga68inpreclinicalpetimagingbyusingconvolutionalneuralnetworkamontecarlosimulationstudy |