ET with fluorine 18 (18 F) fluorodeoxyglucose (FDG) has a substantial impact on the diagnosis and clinical decisions of oncological diseases. 18 F-FDG uptake highlights regions of high glucose metabolism that include both pathologic and physiologic processes (1,2). 18 F-FDG PET/CT adds value to the initial diagnosis, detection of recurrent tumor, and evaluation of response to therapy in lung cancer and lymphoma (3-6). In lung cancer or lymphoma staging, 18 F-FDG PET images are interpreted by trained nuclear medicine readers to help identify foci positive for 18 F-FDG uptake (hereafter, referred to as 18 F-FDG2positive foci) that are suspicious for tumor. This classification of 18 F-FDG2positive foci is particularly challenging for malignant tumors with a low avidity, unusual tumor sites, and motion and attenuation artifacts, and the wide range of 18 F-FDG uptake related to inflammation, infection, or physiologic glucose consumption (7-9). Whereas the intra-and interobserver interpretation of 18 F-FDG PET/CT findings has a high level of agreement in studies involving single site and experienced readers for lymphoma, lung, and head and neck cancer (10-12), there remains an unmet need to assist the reader in analyzing these examinations more efficiently. Convolutional neural networks (CNNs) are a branch of machine learning that is finding applications 18

Purpose To study the feasibility and impact of respiratory gating in positron emission tomographic (PET) imaging in a clinical trial comparing conventional hardware-based gating with a data-driven approach and to describe the distribution of determined parameters. Materials and Methods This prospective study was approved by the ethics committee of the University Hospital of Münster (AZ 2014-217-f-N). Seventy-four patients suspected of having abdominal or thoracic fluorine 18 fluorodeoxyglucose (FDG)-positive lesions underwent clinical whole-body FDG PET/computed tomographic (CT) examinations. Respiratory gating was performed by using a pressure-sensitive belt system (belt gating [BG]) and an automatic data-driven approach (data-driven gating [DDG]). PET images were analyzed for lesion uptake, metabolic volumes, respiratory shifts of lesions, and diagnostic image quality. Results Forty-eight patients had at least one lesion in the field of view, resulting in a total of 164 lesions analyzed (range of number of lesions per patient, one to 13). Both gating methods revealed respiratory shifts of lesions (4.4 mm ± 3.1 for BG vs 4.8 mm ± 3.6 for DDG, P = .76). Increase in uptake of the lesions compared with nongated values did not differ significantly between both methods (maximum standardized uptake value [SUVmax], +7% ± 13 for BG vs +8% ± 16 for DDG, P = .76). Similarly, gating significantly decreased metabolic lesion volumes with both methods (-6% ± 26 for BG vs -7% ± 21 for DDG, P = .44) compared with nongated reconstructions. Blinded reading revealed significant improvements in diagnostic image quality when using gating, without significant differences between the methods (DDG was judged to be inferior to BG in 22 cases, equal in 12 cases, and superior in 15 cases; P = .32). Conclusion Respiratory gating increases diagnostic image quality and uptake values and decreases metabolic volumes compared with nongated acquisitions. Data-driven approaches are clinically applicable alternatives to belt-based methods and might help establishing routine respiratory gating in clinical PET/CT. (©) RSNA, 2016 Online supplemental material is available for this article.

first line of Materials and Methods should read as follows: This retrospective study was approved by the ethics committee of the Ärztekammer Westfalen-Lippe and the University of Münster (Az 2014-217-fN).

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