Evaluation of the prostate cancer and its metastases in the 68-Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing

Objective: This study aims to demonstrate the feasibility and benefits of using a deep learning-based approach for attenuation correction in 68-Ga-PSMA whole-body PET scans. Materials & Methods: A dataset comprising 700 patients (a mean age: 67.6±5.9 years old, range: 45-85 years) with prostate cancer who underwent 68-Ga-PSMA PET/CT examinations was collected. A deep learning model was trained on 700 whole-body68-Ga-PSMA clinical images to perform attenuation correction (AC) in the image domain. To assess the quantitative accuracy of the developed deep learning model, clinical data from 92 patients were used as a reference for CT-based PET AC (PET-CTAC). Standard quantification metrics, including mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) were calculated in terms of standard uptake value (SUV) to gauge the accuracy of the model. For clinical evaluation, three specialists conducted a blinded assessment of synthesized PET images’ quality in terms of lesion detectability across 50 clinical subjects, comparing them with PET-CTAC images. Results: Quantitative analysis of the deep learning AC (DLAC) model revealed ME, MAE, and RMSE values of -0.007±0.032, 0.08±0.033, and 0.252±125 (SUV), respectively. Additionally, regarding lesion detection analysis, the deep learning model demonstrated superior image quality for 16 subjects out of 50 compared to the PET-CT AC images. In 56% of cases, PET-DLAC and PET-CTAC images exhibited closely comparable image quality and lesion delectability. Conclusion: This study emphasizes the significant improvement in image quality and lesion detection capabilities achieved through the integration of deep learning-based attenuation correction in 68-Ga-PSMA PET imaging. This innovation not only provides a compelling solution to the challenges posed by bladder radioactivity but also a promising way to minimize patient radiation exposure through the coordinated integration of low-dose CT and deep learning-based AC, while simultaneously increasing the image quality.