Deep learning image reconstruction technique based on sinogram with 99m Tc-3PRGD2 chest SPECT

Abstract Purpose This study is to evaluate the accuracy of a deep learning reconstruction method based on sinogram with 99mTc-3PRGD2 chest SPECT. The aim is to shorten the local SPECT scanning time by 50% while preserving the quality of the images, allowing for faster completion of full-body SPECT scanning. Materials and Methods The images were selected from 33 patients diagnosed with lung cancer both clinically and pathologically. The full-projection and half-projection reconstruction techniques were used to create SPECT tomographic images. All the projection images were used as the " Ground Truth ", and half of the images were used to create full-projection SPECT images. A training dataset 28 for the building model and a test dataset 5 were used to evaluate the image quality by measuring the image error of the test dataset. Result The evaluation results of the image quality for the 99mTc-3PRGD2 chest SPECT images using the deep learning reconstruction method based on sinogram were based on 5 test datasets. The following metrics were calculated: mean absolute error (MAE), mean-square error (MSE), Peak signal to noise ratio (PSNR), structural similarity (SSIM), normalized root mean square error (NRSM), and normalized Mutual Information (NMI). The average values of PSNR and SSIM were found to be 46.43 ± 5.05 and 0.92 ± 0.02, respectively. The mean values for MAE, MSE, NRSM, and NMI were 1.04 ± 0.52, 9.54 ± 7.24, 0.07 ± 0.03, and 1.59 ± 0.04, respectively. Conclusion A novel approach to SPECT imaging involves using deep learning and selecting only half of the projections to reconstruct SPECT images directly from a sinogram. This technique has been shown to yield tomographic images of comparable quality to those obtained from full projection images while reducing scanning time for 99mTc-3PRGD2 chest SPECT by 50%.