CT-Less TOF PET: challenges and innovations for quantitative imaging

More than a decade has passed since the seminal work of Defriseet al(2012Phys. Med. Biol.57885-99), which demonstrated that time-of-flight (TOF) PET can, under appropriate conditions, be intrinsically self-correcting for several physical degradation factors, most notably attenuation, which in contemporary clinical practice is typically derived from CT in PET/CT systems. The work of Defriseet al(2012Phys. Med. Biol.57885-99) popularized the concept of maximum-likelihood reconstruction of attenuation and activity (MLAA) and stimulated extensive subsequent methodological and clinical investigations. The many studies that were done on this subject, led to early clinical evaluations of joint activity-attenuation reconstruction algorithms based on advanced optimization strategies. In recent years, however, self-attenuation correction has received comparatively less attention, as the field has increasingly shifted toward the synthesis of attenuation maps using deep learning (DL) often in combination with physics-based constraints. Many of the proposed DL-based methods worked with non-attenuation-corrected PET images, sometimes supplemented by additional sources of information. Within this paradigm, attenuation estimation has effectively become an image-to-image translation problem, well suited to convolutional neural networks, generative adversarial networks, and more recently emerging diffusion-based models. These approaches benefit from the widespread availability of PET/CT datasets, which enable the construction of large training cohorts, although the inherently sequential nature of PET and CT acquisitions can introduce misregistration and associated biases. Against this backdrop, the DL evolution raises a fundamental question: what level of attenuation information can truly be recovered from suboptimal, incomplete or noisy PET emission data alone? Addressing this question challenges not only the expressive power of data-driven methods, but also their physical interpretability and robustness. More broadly, it invites reflection on whether decades of physics-based modeling, analytical reconstruction theory, and detector-driven innovation will be complemented, or reinterpreted within increasingly more powerful machine learning frameworks. In this review, we examine these competing and often complementary approaches to CT-less quantitative PET imaging, with a particular emphasis on TOF-enabled methods, while recognizing that definitive answers to these questions may only emerge through continued theoretical, technological, and clinical investigation.