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Established clinically available methods that employ body\u2010attached hardware for acquiring respiration signals rely on the assumption that external surface motion and internal organ motion are well correlated. In this paper, the authors present a markerless method comprising two Microsoft Kinects for determining the motion on the whole torso surface and aim to demonstrate its validity and usefulness\u2014including the potential to study the external\/internal correlation and to provide useful information for more advanced correction approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p>The data of two Kinects are used to calculate 3D representations of a patient's torso surface with high spatial coverage. Motion signals can be obtained for any position by tracking the mean distance to a virtual camera with a view perpendicular to the surrounding surface. The authors have conducted validation experiments including volunteers and a moving high\u2010precision platform to verify the method's suitability for providing meaningful data. In addition, the authors employed it during clinical <jats:sup>18<\/jats:sup>F\u2010FDG\u2010PET scans and exemplarily analyzed the acquired data of ten cancer patients. External signals of abdominal and thoracic regions as well as data\u2010driven signals were used for gating and compared with respect to detected displacement of present lesions. Additionally, the authors quantified signal similarities and time shifts by analyzing cross\u2010correlation sequences.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>The authors\u2019 results suggest a Kinect depth resolution of approximately 1 mm at 75 cm distance. Accordingly, valid signals could be obtained for surface movements with small amplitudes in the range of only few millimeters. In this small sample of ten patients, the abdominal signals were better suited for gating the PET data than the thoracic signals and the correlation of data\u2010driven signals was found to be stronger with abdominal signals than with thoracic signals (average Pearson correlation coefficients of 0.74 \u00b1 0.17 and 0.45 \u00b1 0.23, respectively). In all cases, except one, the abdominal respiratory motion preceded the thoracic motion\u2014a maximum delay of approximately 600 ms was detected.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>The method provides motion information with sufficiently high spatial and temporal resolution. Thus, it enables meaningful analysis in the form of comparisons between amplitudes and phase shifts of signals from different regions. In combination with a large field\u2010of\u2010view, as given by combining the data of two Kinect cameras, it yields surface representations that might be useful in the context of motion correction and motion modeling.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4917163","type":"journal-article","created":{"date-parts":[[2015,4,13]],"date-time":"2015-04-13T17:09:28Z","timestamp":1428944968000},"page":"2276-2286","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":28,"title":["A dual\u2010Kinect approach to determine torso surface motion for respiratory motion correction in PET"],"prefix":"10.1002","volume":"42","author":[{"given":"Mirco","family":"He\u00df","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"B\u00fcther","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Gigengack","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Dawood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus P.","family":"Sch\u00e4fers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2016,11,30]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1448824"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.rlu.0000093095.28642.2b"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2008.01.002"},{"key":"e_1_2_7_5_1","first-page":"1287","article-title":"The CT motion quantitation of lung lesions and its impact on PET\u2010measured SUVs","volume":"45","author":"Erdi Y. 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