• Adam L Kesner, Jonathan H Chung, Kimberly E Lind, Jennifer J Kwak, David Lynch, Darrell Burckhardt, and Phillip J Koo.
• Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, University of Colorado, School of Medicine, Aurora, Colorado 80045.
• Med Phys. 2016 Mar 1; 43 (3): 1451-61.

PurposeRespiratory gating is a strategy for overcoming image degradation caused by patient motion in Positron Emission Tomography (PET) imaging. Traditional methods for sorting data, namely, phase-based gating or amplitude-based gating, come with an inherent trade-off between resolution improvements and added noise present in the subjugated data. If the goal of motion correction in PET is realigned from creating 4D images that attempt to mimic nongated images, towards ideal utilization of the information available, then new paths for data management emerge. In this work, the authors examine the application of a method in a new class of frequency based data subjugation algorithms, termed gating +. This strategy utilizes data driven information to locally adapt signal to its optimal segregation, thereby creating a new approach to 4D data utilization PET.Methods189 (18)F-fluorodeoxyglucose (FDG) PET scans were acquired at a single bed position centered on the thorax region. 4D gated image sets were reconstructed using data driven gating. The gating+ signal optimization algorithm, previously presented in small animal PET images and simulations, was used to segregate data in frequency space to generate optimized 4D images in the population-the first application and analysis of gating+ in human PET scans. The nongated, phase gated, and gating+ representations of the data were compared using FDG uptake analysis in the identified lesions and noise measurements from background regions.ResultsOptimized processing required less than 1 min per scan on a standard PC (plus standard reconstruction time), and yielded entire 4D optimized volumes plus motion maps. Optimized scans had noise characteristics similar to nongated images, yet also contained much of the resolution and motion information found in the gated images. The average SUVmax increase in the lesion sample between gated/nongated and gating+/nongated (±SD in population) was 35.8% ± 34.6% and 28.6% ± 27.9%, respectively. The average percent standard deviation (%SD ± SD in population) in liver volumes of interest (VOIs) across the sample for the nongated, gated, and gating+ scans was 6.7% ± 2.4%, 13.6% ± 3.3%, and 7.1% ± 2.5%, respectively. In all cases, the noise in the gating+ liver VOIs was closer to the nongated measurements than to the gated.ConclusionsThe gating+ algorithm introduces the notion of conforming 4D data segregation to the local information and statistics that support it. By segregating data in frequency space, the authors are able to generate low noise motion information rich image sets, derived solely from selective use of raw data. Their work shows that the gating+ algorithm can be robustly applied in populations, and across varying qualities of motion and scans statistics, and be integrated as part of a fully automated motion correction workflow. Furthermore, the idea of smart signal utilization underpins a new concept of low risk or even risk-free motion correction application in PET.

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