5 years ago

Non-rigid Event-by-event Continuous Respiratory Motion Compensated List-mode Reconstruction for PET.

Papademetris, Jian, Carson, Liu, Onofrey, Germino, Chan
Respiratory motion during PET/CT imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this study, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-byevent (NR-EBE) respiratory motion compensated list-mode reconstruction algorithm. The proposed method consists of 2 components, the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation (NR-INTEX). 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. Non-rigid EBE motion compensated image reconstruction using both components were then validated on three human studies injected with 18F-FPDTBZ and one with 18F-FDG tracers. The human results were compared to 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 18F-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.

Publisher URL: http://doi.org/10.1109/TMI.2017.2761756

DOI: 10.1109/TMI.2017.2761756

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