A tissue-informed deep learning-based method for positron range correction in preclinical 68Ga PET imaging.

Positron range (PR) limits spatial resolution and quantitative accuracy in PET imaging, particularly for high-energy positron-emitting radionuclides like 68Ga. We propose a deep learning method using 3D residual encoder-decoder convolutional neural networks (3D RED-CNNs), incorporating tissue-dependent anatomical information through a u-map-dependent loss function. Models were trained with realistic simulations and, using initial PET and CT data, generated positron range corrected images. We validated the models in simulations and real acquisitions. Three 3D RED-CNN architectures, Single-channel, Two-channel, and DualEncoder, were trained on simulated PET datasets and evaluated on synthetic and real PET acquisitions from 68Ga-FH and 68Ga-PSMA-617 mouse studies. Performance was compared to a standard Richardson-Lucy-based positron range correction (RL-PRC) method using metrics such as mean absolute error (MAE), structural similarity index (SSIM), contrast recovery (CR), and contrast-to-noise ratio (CNR). CNN-based methods achieved up to 19 percent SSIM improvement and 13 percent MAE reduction compared to RL-PRC. The Two-Channel model achieved the highest CR and CNR, recovering lung activity with 97 percent agreement to ground truth versus 77 percent for RL-PRC. Noise levels remained stable for CNN models (approximately 5.9 percent), while RL-PRC increased noise by 5.8 percent. In preclinical acquisitions, the Two-Channel model achieved the highest CNR across tissues while maintaining the lowest noise level (9.6 percent). Although no ground truth was available for real data, tumor delineation and spillover artifacts improved with the Two-Channel model. These findings highlight the potential of CNN-based PRC to enhance quantitative PET imaging, particularly for 68Ga. Future work will improve model generalization through domain adaptation and hybrid training strategies.