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Data-driven gated (DDG) CT: An automated respiratory gating method to enable DDG PET/CT.

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BACKGROUND The accuracy of PET quantification and localization can be compromised if a misregistered CT is used for attenuation correction (AC) in PET/CT. As data-driven gating (DDG) continues to grow… Click to show full abstract

BACKGROUND The accuracy of PET quantification and localization can be compromised if a misregistered CT is used for attenuation correction (AC) in PET/CT. As data-driven gating (DDG) continues to grow in clinical use, these issues are becoming more relevant with respect to solutions for gated CT. PURPOSE In this work, a new automated data-driven gated (DDG) CT method was developed to provide average CT and DDG CT for AC of PET and DDG PET, respectively. METHODS An automatic DDG CT was developed to provide the end-expiratory (EE) and end-inspiratory (EI) phases of images from low-dose cine CT images, with all phases being averaged to generate an average CT. The respiratory phases of EE and EI were determined according to lung region Hounsfield unit (HU) values and body outline contours. The average CT was used for AC of baseline PET and DDG CT at EE phase was used for AC of DDG PET at the quiescent or EE phase. The EI and EE phases obtained with DDG CT were used for assessing the magnitude of respiratory motion. The proposed DDG CT was compared to two commercial CT gating methods: 1) 4D CT (external device based) and 2) D4D CT (DDG based) in 38 patient data sets with respect to respiratory phase image selection, lung HU, lung volume, and image artifacts. In a separate set of twenty consecutive PET/CT studies containing a mix of 18 F-FDG, 68 Ga-Dotatate, and 64 Cu-Dotatate scans, the proposed DDG CT was compared with D4D CT for impacts on registration and quantification in DDG PET/CT. RESULTS In the EE phase, the images selected by DDG CT and 4D CT were identical 62.5±21.6% of the time, while DDG CT and D4D CT were 6.5±9.7%, and 4D CT and D4D CT were 8.6±12.2%. These differences in EE phase image selection were significant (p<0.0001). In the EI phase, the images selected by DDG CT and 4D CT were identical 68.2±18.9% of the time, DDG CT and D4D CT were 63.9±18.8%, and 4D CT and D4D CT were 61.2±19.8%. These differences were not significant. The mean lung HU and volumes were not statistically different (p > 0.1) among the three methods. In some studies, DDG CT was better than D4D or 4D CT in appropriate selection of the EE and EI phases, and D4D CT was found to reverse the EE and EI phases or not select the correct images by visual inspection. A statistically significant improvement of DDG CT over D4D CT for AC of DDG PET was also demonstrated with PET quantification analysis. When irregular breath cycles were present in the cine CT, DDG CT could be used to replace average CT for improved AC of baseline PET. CONCLUSION A new automatic DDG CT was developed to tackle the issues of misregistration and tumor motion in PET/CT imaging. DDG CT was significantly more consistent than D4D CT in selecting the EE phase images as the clinical standard of 4D CT. When compared to both commercial gated CT methods of 4D CT and D4D CT, DDG CT appeared to be more robust in the lower lung and upper diaphragm regions where misregistration and tumor motion often occur. DDG CT offered improved AC for DDG PET relative to D4D CT. In cases with irregular respiratory motion, DDG CT improved AC over average CT for baseline PET. The new DDG CT provides the benefits of 4D CT without the need for external device gating. This article is protected by copyright. All rights reserved.

Keywords: ddg; pet; ddg pet; lung; data driven; d4d

Journal Title: Medical physics
Year Published: 2022

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