Artificial Intelligence for PET and SPECT Image Enhancement

Abstract

Nuclear medicine imaging modalities such as PET and SPECT are confounded by high noise levels and low spatial resolution, necessitating postreconstruction image enhancement to improve their quality and quantitative accuracy. Artificial intelligence (AI) models such as convolutional neural networks, U-Nets, and generative adversarial networks have shown promising outcomes in enhancing PET and SPECT images. This review article presents a comprehensive survey of state-of-the-art AI methods for PET and SPECT image enhancement and seeks to identify emerging trends in this field. We focus on recent breakthroughs in AI-based PET and SPECT image denoising and deblurring. Supervised deep-learning models have shown great potential in reducing radiotracer dose and scan times without sacrificing image quality and diagnostic accuracy. However, the clinical utility of these methods is often limited by their need for paired clean and corrupt datasets for training. This has motivated research into unsupervised alternatives that can overcome this limitation by relying on only corrupt inputs or unpaired datasets to train models. This review highlights recently published supervised and unsupervised efforts toward AI-based PET and SPECT image enhancement. We discuss cross-scanner and cross-protocol training efforts, which can greatly enhance the clinical translatability of AI-based image enhancement tools. We also aim to address the looming question of whether the improvements in image quality generated by AI models lead to actual clinical benefit. To this end, we discuss works that have focused on task-specific objective clinical evaluation of AI models for image enhancement or incorporated clinical metrics into their loss functions to guide the image generation process. Finally, we discuss emerging research directions, which include the exploration of novel training paradigms, curation of larger task-specific datasets, and objective clinical evaluation that will enable the realization of the full translation potential of these models in the future.

Application of PET/MRI in Gynecologic Malignancies

“…PET images often suffer from noise and limited spatial resolution. AI models, including convolutional neural networks, U-Nets, and generative adversarial networks, have demonstrated improvements in denoising and image enhancement [143][144][145]. These advancements can potentially reduce radiotracer doses and scan times, as well as improve workflow efficiency [144,145].…”

“…AI models, including convolutional neural networks, U-Nets, and generative adversarial networks, have demonstrated improvements in denoising and image enhancement [143][144][145]. These advancements can potentially reduce radiotracer doses and scan times, as well as improve workflow efficiency [144,145]. Deep learning methods can be utilized to transform MR images into pseudo-CT images, which are necessary for attenuation correction in PET/MRI [145].…”

Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying

“…The recent emergence of AI/ML technologies has been increasingly producing novel applications in the healthcare field, including the nuclear medicine practice, creating new opportunities and bringing about transformative changes [ 12 ]. AI/ML impacted the entire nuclear medicine imaging pipeline from acquisition and image generation, reconstructions in SPECT and PET, image processing and analysis including radiomics, quantification, segmentation, and image enhancement (denoising, deblurring, partial volume correction) [ 13 , 14 , 15 , 16 , 17 , 18 ]. At the basic physics level, AI/ML has generated novel applications related to scatter correction, photon depth of interaction, and time of flight PET imaging [ 13 , 16 ].…”