Real-time data-driven motion correction in PET

Abstract

PET imaging has been, and continues to be, an evolving diagnostic technology. In recent years, the modernizing digital landscape has opened new opportunities for data-driven innovation. One such facet has been data-driven motion correction (DDMC) in PET. As both research and industry propel this technology forward, we can recognize prospects and opportunities for further development. The concept of clinical practicality is supported by DDMC approaches—it is what sets them apart from traditional hardware-driven motion correction strategies that have largely not gained acceptance in routine diagnostic PET; the ease of use of DDMC may help propel acceptance of motion correction solutions in clinical practice. As we reflect on the present field, we should consider that DDMC can be made even more practical, and likely more impactful, if further developed to fit within a real-time acquisition framework. This vision for development is not new, but has been made more feasible with contemporary electronics, and has begun to be revisited in contemporary literature. The opportunities for development lie on a new forefront of innovation where medical physics integrates with engineering, data science, and modern computing capacities. Real-time DDMC is a systems integration challenge, and achieving it will require cooperation between hardware and software developers, and likely academia and industry. While challenges for development do exist, it is likely that we will see real-time DDMC come to fruition in the coming years. This effort may establish groundwork for developing similar innovations in the emerging digital innovation age.

Clinical Evaluation of a Data-Driven Respiratory Gating Algorithm for Whole-Body PET with Continuous Bed Motion

“…First clinical studies already have demonstrated that DDG methods are equivalent to BG methods in terms of image quality and lesion quantification (14,15); accordingly, vendors have started incorporating DDG algorithms into their PET systems (17). However, more studies are needed to ensure that DDG works well in a wide range of applications, radiotracers, and scanning modes (31).…”

Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS)

“…4 ). This problem affects particularly small lesions in the upper abdomen, such as accessory spleens, IPAS and pNET 47 – 50 .…”

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

“…DL methods have been used for improving PET image quality, reducing noise [54], removing streak artifacts from CT [55], and developing novel techniques for tomographic image reconstruction based on a reduced amount of acquired data. Other promising applications are a generation of synthetic images, such as synthetic CT from MRI [56], virtual contrast-enhanced images [57], and rigid/deformable intramodal and multimodal image registration [58], and extraction of the respiratory signal [21] that could be used for breathing motion compensation of images [59].…”