Deep-Learning Based Positron Range Correction of PET Images

Abstract

Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.

Moving the goalposts while scoring―the dilemma posed by new PET technologies

“…In non-PSF PET, this results in a steady decrease in spatial resolution from the FOV center to its periphery, which is compensated for by PSF reconstruction [26]. Notably, PSF reconstruction usually refers to the correction for F-18 while optimal PSF compensation for other radionuclides would require integrating their different PSF based on specific positron ranges [27,28].…”

Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

“…These can be grouped into two approaches: (1) Incorporating positron range models in the reconstruction algorithm (reconstruction-based correction, n = 5) and (2) approaches applying post-reconstruction positron range correction (post-reconstruction correction, n = 3), as seen in Figure 2 . Reconstruction-based corrections generally used blurring kernels to model the positron range during the iterative reconstruction process [ 16 , 17 , 18 , 19 , 20 ], while post-reconstruction corrections applied deblurring kernels or deep learning techniques on the reconstructed images [ 21 , 22 , 23 ].…”

Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study

“…Fourier devolution techniques have been applied to compensate the positron range effects in PET imaging [ 21 ], which inspired us to investigate the possibility of using CNN methods for positron range correction. According to Herraiz et al [ 22 ], their study published in 2021 was the first work to successfully combine deep learning and positron range correction in a coherent framework. In our opinion, more studies are needed in this field.…”

“…Herraiz et al have presented a work which adapts the U-Net network to correct positron range effects of Ga-68 in preclinical PET imaging [ 22 ]. In their work, the input data to CNN were Ga-68 images, while the label data were the F-18 images.…”

“…In Reference [ 22 ], it was assumed that the reconstruction method already incorporated positron range correction for F-18, and their image data for CNN training, testing, and validation were generated from numerical models of mice from a repository. In this work, the CNN output images were back-to-back 511-keV gamma rays, which were not affected by the positron range effects.…”