PET respiratory motion correction: quo vadis?

Abstract

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Evaluation of a respiratory motion-corrected image reconstruction algorithm in 2-[18F]FDG and [68Ga]Ga-DOTA-NOC PET/CT: impacts on image quality and tumor quantification

“…Our results showed that MCIR reduced motion artifacts on tumors in the regions vulnerable to respiratory motions. Respiratory motion is one of the major factors causing motion artifacts in PET/CT (7,(19)(20)(21). In this study, the observers identified lesion motions in 52.4-56.3% of patients, suggesting respiratory motion correction may potentially bring benefits to many patients.…”

“…The importance of respiratory motion correction for PET imaging has prompted a series of technical developments (7). Gated image reconstruction is a basic approach for respiratory motion correction in which PET data are allocated into several gates according to their phase or amplitude in the respiratory waveform.…”

“…However, the efficacy of MCIR on respiratory motion correction has not yet been evaluated, and the evaluation of MCIR on tracers beyond 2-deoxy-2-[ 18 F]fluoro-D-glucose {2-[ 18 F]FDG} has not been reported on. Therefore, this study aimed to investigate the impact of the MCIR algorithm on tumor evaluation and image quality using the data of a motion phantom and oncology patients undergoing 2-[ 18 F]FDG and 68 Ga-labeled [1,4,7,10-tetraazacyclododecane-1,4,7,10tetraacetic acid]-1-Nal3-octreotide {[ 68 Ga]Ga-DOTA-NOC} PET/computed tomography (CT). In this study, lesions' standardized uptake values (SUVs), volumes, and visual image quality scores were compared between reconstructions with and without MCIR.…”

Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI

“…To overcome this problem, motion estimation has been a subject of significant research interest in a range of imaging modalities [8,9,10,11,12]. Several computational techniques for acquisitions and correction of motion and motion-corrected image reconstruction have been developed in parallel to the advancement of hardware [13,14,15,16,17,18,19]. Image-based motion synchronization and detection has been studied in [20,21,22], where filtering is used to separate respiratory and cardiac signals in PET. In this paper we consider examples in dynamic MRI, where both deliberate imaging dynamics such as morphological motion or contrast inflow as well as undesired dynamics such as breathing motion play an important role.…”

“…In particular, image reconstruction for MRI with motion correction has been studied in detail, see [14,23,24] and the references therein. Most motion compensation strategies have not found widespread acceptance in clinical practice, mainly due to the complexity of their implementation and the associated cost or computational burden [13,14]. In this work we develop a method based on generator neural networks that exhibits very successful results in motion separation for cardiac MRI images with respiratory motion.…”