Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET

Abstract

Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with F-FPDTBZ and one withF-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.

Comparison of two elastic motion correction approaches for whole-body PET/CT: motion deblurring vs gate-to-gate motion correction

“…These methods are usually based on gated sinograms allowing to perform a gate-to-gate motion correction. Even more advanced methods of PET motion correction have been introduced to apply non-rigid corrections during image reconstruction on an event-to-event basis [16]. As the advanced MC techniques make use of all acquired data, the resulting image quality is comparable to static image reconstructions using the same raw data.…”

Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data

“…We first determine which of the 8 respiratory phases best matches the CT, which will be used as the reference phase for PET motion correction. Then, a nonrigid motion model is built to describe a continuous relationship between the Anzai displacement and the movement of each voxel (11). Finally, the event-by-event motion-corrected reconstruction generates the final image with all the counts corrected to the reference phase (11).…”

“…This nonrigid model can be used to describe a continuous relationship between the Anzai trace displacement and the movement of each voxel, which were subsequently incorporated into nonrigid INTEX-MOLAR to perform event-by-event motioncompensated list-mode ordered-subsets expectation maximization reconstruction (3 iterations · 21 subsets). Further details on the nonrigid INTEX-MOLAR method were described previously (11). Decay, scatter, and random corrections were performed.…”

“…Although the MLACF images may not be quantitative (9), they can be useful for estimating nonrigid motion between gates. Second, to address the intragate motion, we incorporated nonrigid motion information derived from MLACF-reconstructed gated PET images into the framework of a nonrigid internal-external (INTEX) motion correlation technique (11) integrated with a motioncompensation ordered-subsets expectation maximization list-mode algorithm for resolution-recovery reconstruction (MOLAR) (12), for both inter-and intragate respiratory motion correction in an event-by-event fashion (11). Third, the PET reference gate, to which all the events were corrected, is automatically determined by choosing the gate whose MLAA-derived attenuation map best matches the helical CT image.…”

Motion Dependent and Spatially Variant Resolution Modeling for PET Rigid Motion Correction

“…As pointed out by Chan et al [20], two different approaches to perform motion correction in PET reconstruction using ML-EM can be considered. The first involves transformation of the reconstruction image from the reference (motion-free) pose to the original pose of measurement before forward projection of the image, followed by the inverse image transformation of the correction image.…”