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Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
INTRODUCTION: The attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. (99m)Tc-3PRGD(2) is a novel radiotracer for the early diagnosis and evaluation of trea...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10218122/ https://www.ncbi.nlm.nih.gov/pubmed/37251952 http://dx.doi.org/10.3389/fonc.2023.1165664 |
Sumario: | INTRODUCTION: The attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. (99m)Tc-3PRGD(2) is a novel radiotracer for the early diagnosis and evaluation of treatment effects of lung cancer. This study preliminary discusses the deep learning method to directly correct the attenuation of (99m)Tc-3PRGD(2) chest SPECT images. METHODS: Retrospective analysis was performed on 53 patients with pathological diagnosis of lung cancer who received (99m)Tc-3PRGD(2) chest SPECT/CT. All patients’ SPECT/CT images were reconstructed with CT attenuation correction (CT-AC) and without attenuation correction (NAC). The CT-AC image was used as the reference standard (Ground Truth) to train the attenuation correction (DL-AC) SPECT image model using deep learning. A total of 48 of 53 cases were divided randomly into the training set, the remaining 5 were divided into the testing set. Using 3D Unet neural network, the mean square error loss function (MSELoss) of 0.0001 was selected. A testing set is used to evaluate the model quality, using the SPECT image quality evaluation and quantitative analysis of lung lesions tumor-to-background (T/B). RESULTS: SPECT imaging quality metrics between DL-AC and CT-AC including mean absolute error (MAE), mean-square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized root mean square error (NRMSE), and normalized Mutual Information (NMI) of the testing set are 2.62 ± 0.45, 58.5 ± 14.85, 45.67 ± 2.80, 0.82 ± 0.02, 0.07 ± 0.04, and 1.58 ± 0.06, respectively. These results indicate PSNR > 42, SSIM > 0.8, and NRMSE < 0.11. Lung lesions T/B (maximum) of CT-AC and DL-AC groups are 4.36 ± 3.52 and 4.33 ± 3.09, respectively (p = 0.81). There are no significant differences between two attenuation correction methods. CONCLUSION: Our preliminary research results indicate that using the DL-AC method to directly correct (99m)Tc-3PRGD(2) chest SPECT images is highly accurate and feasible for SPECT without configuration with CT or treatment effect evaluation using multiple SPECT/CT scans. |
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