Abstract
Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuation-corrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high-energy photon attenuation. Deep-learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT- and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.
Highlights
M ANY physical and patient factors influence the image quality and quantitative accuracy of ionizing radiationbased tomographic imaging techniques
In positron emission tomography (PET), which involves the collection of two high energy (511 keV) annihilation photons emitted from positronemitting radioisotopes; photoelectric absorption and Compton scattering of high-energy annihilation photons are among the major physical factors that degrade the reconstructed images (Fig. 1) [1]
The atlas-based approaches exhibited suitable performances in the brain PET/magnetic resonance imaging (MRI) studies, with the exception of postoperative patients and patients with implants, the application of the atlas-based approaches to the entire-body PET/MRI studies is still challenging due to their limitations with respect to the significant anatomical variations of the organs in the chest and abdomen, especially in cancer patients
Summary
M ANY physical and patient factors influence the image quality and quantitative accuracy of ionizing radiationbased tomographic imaging techniques. With the advances of machine learning in medical imaging fields, various machine-learning approaches for the improvement of PET AC have been proposed [25]–[51] Among these approaches, deep-learning (DL)-based methods have attracted significant research attention as alternatives to conventional AC methods. Other approaches that are not dependent on the anatomical images (CT or MRI) can overcome limitations with respect to current CT- and MRI-based ACs and allow for more accurate PET quantification in standalone PET scanners for the realization of low radiation doses [25]–[33].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
