Direct attenuation correction for 99mTc-3PRGD2 chest SPECT lung cancer images using deep learning

IntroductionThe attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. 99mTc-3PRGD2 is a novel radiotracer for the early diagnosis and evaluation of treatment...

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Veröffentlicht in:Frontiers in oncology 2023-05, Vol.13, p.1165664-1165664
Hauptverfasser: Xing, Haiqun, Wang, Tong, Jin, Xiaona, Tian, Jian, Ba, Jiantao, Jing, Hongli, Li, Fang
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Sprache:eng
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Zusammenfassung:IntroductionThe attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. 99mTc-3PRGD2 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 99mTc-3PRGD2 chest SPECT images. MethodsRetrospective analysis was performed on 53 patients with pathological diagnosis of lung cancer who received 99mTc-3PRGD2 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). ResultsSPECT 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. ConclusionOur preliminary research results indicate that using the DL-AC method to directly correct 99mTc-3PRGD2 chest SPECT images is highly accurate and feasible for SPECT without configuration with CT or treatment effect evaluation using multiple SPECT/CT scans.
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2023.1165664