{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:47:01Z","timestamp":1760240821089,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T00:00:00Z","timestamp":1569283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["314483"],"award-info":[{"award-number":["314483"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 \u00b1 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET\/CT imaging based on dual inertial sensors.<\/jats:p>","DOI":"10.3390\/s19194137","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"4137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating"],"prefix":"10.3390","volume":"19","author":[{"given":"Mojtaba","family":"Jafari Tadi","sequence":"first","affiliation":[{"name":"Department of Future Technologies, University of Turku, 20500 Turku, Finland"}]},{"given":"Eero","family":"Lehtonen","sequence":"additional","affiliation":[{"name":"Department of Future Technologies, University of Turku, 20500 Turku, Finland"}]},{"given":"Jarmo","family":"Teuho","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University and Turku University Central Hospital, 20500 Turku, Finland"}]},{"given":"Juho","family":"Koskinen","sequence":"additional","affiliation":[{"name":"Department of Future Technologies, University of Turku, 20500 Turku, Finland"}]},{"given":"Jussi","family":"Schultz","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University and Turku University Central Hospital, 20500 Turku, Finland"}]},{"given":"Reetta","family":"Siekkinen","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University and Turku University Central Hospital, 20500 Turku, Finland"},{"name":"Department of Medical Physics, Turku University Central Hospital, 20500 Turku, Finland"}]},{"given":"Tero","family":"Koivisto","sequence":"additional","affiliation":[{"name":"Department of Future Technologies, University of Turku, 20500 Turku, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6108-9975","authenticated-orcid":false,"given":"Mikko","family":"P\u00e4nk\u00e4\u00e4l\u00e4","sequence":"additional","affiliation":[{"name":"Department of Future Technologies, University of Turku, 20500 Turku, Finland"}]},{"given":"Mika","family":"Ter\u00e4s","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Turku University Central Hospital, 20500 Turku, Finland"},{"name":"Deparment of Biomedicine, University of Turku, 20500 Turku, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0982-8360","authenticated-orcid":false,"given":"Riku","family":"Kl\u00e9n","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University and Turku University Central Hospital, 20500 Turku, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.jcmg.2010.04.007","article-title":"Cardiac PET Imaging for the Detection and Monitoring of Coronary Artery Disease and Microvascular Health","volume":"3","author":"Schindler","year":"2010","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7345","DOI":"10.1088\/0031-9155\/54\/24\/007","article-title":"The impact of respiratory motion on tumor quantification and delineation in static PET\/CT imaging","volume":"54","author":"Liu","year":"2009","journal-title":"Phys. Med. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1118\/1.1448824","article-title":"Effect of respiratory gating on reducing lung motion artifacts in PET imaging of lung cancer","volume":"29","author":"Nehmeh","year":"2002","journal-title":"Med. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7390","DOI":"10.1118\/1.4766876","article-title":"The effect of breathing irregularities on quantitative accuracy of respiratory gated PET\/CT","volume":"39","author":"Teo","year":"2012","journal-title":"Med. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1118\/1.2349696","article-title":"The management of respiratory motion in radiation oncology report of AAPM Task Group 76 a","volume":"33","author":"Keall","year":"2006","journal-title":"Med. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1118\/1.1739671","article-title":"Quantitation of respiratory motion during 4D PET\/CT acquisition","volume":"31","author":"Nehmeh","year":"2004","journal-title":"Med. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1118\/1.2748104","article-title":"Respiratory gating in positron emission tomography: A quantitative comparison of different gating schemes","volume":"34","author":"Dawood","year":"2007","journal-title":"Med. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"674","DOI":"10.2967\/jnumed.108.059204","article-title":"List Mode\u2013Driven Cardiac and Respiratory Gating in PET","volume":"50","author":"Dawood","year":"2009","journal-title":"J. Nucl. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1007\/s12350-015-0094-7","article-title":"Imaging moving heart structures with PET","volume":"23","author":"Slomka","year":"2016","journal-title":"J. Nucl. Cardiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8080","DOI":"10.1088\/1361-6560\/aa8b09","article-title":"A novel dual gating approach using joint inertial sensors: Implications for cardiac PET imaging","volume":"62","author":"Tadi","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TBME.2015.2411155","article-title":"Seismocardiography-Based Detection of Cardiac Quiescence","volume":"62","author":"Wick","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5297","DOI":"10.1088\/0031-9155\/61\/14\/5297","article-title":"Echocardiography as an indication of continuous-time cardiac quiescence","volume":"61","author":"Wick","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2017.2708100","article-title":"Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection","volume":"5","author":"Yao","year":"2017","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2018.2869141","article-title":"An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks","volume":"6","author":"Yao","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/JBHI.2013.2274211","article-title":"Robust sensor fusion of unobtrusively measured heart rate","volume":"18","author":"Wartzek","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","first-page":"6","article-title":"Accelerometer-based method for extracting respiratory and cardiac gating information for dual gating during nuclear medicine imaging","volume":"2014","author":"Tadi","year":"2014","journal-title":"J. Biomed. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TBME.2018.2856700","article-title":"An independent component analysis approach to motion noise cancelation of cardio-mechanical signals","volume":"66","author":"Yang","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/72.761722","article-title":"Fast and robust fixed-point algorithms for independent component analysis","volume":"10","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/TITB.2012.2198071","article-title":"A System for Seismocardiography-Based Identification of Quiescent Heart Phases: Implications for Cardiac Imaging","volume":"16","author":"Wick","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: Algorithms and applications","volume":"13","author":"Oja","year":"2000","journal-title":"Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1109\/JBHI.2014.2361732","article-title":"Ballistocardiography and Seismocardiography: A Review of Recent Advances","volume":"19","author":"Inan","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6823","DOI":"10.1038\/s41598-017-07248-y","article-title":"Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables","volume":"7","author":"Lehtonen","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hurnanen, T., Kaisti, M., Tadi, M.J., V\u00e4h\u00e4-Heikkil\u00e4, M., Nieminen, S., Iftikhar, Z., Paukkunen, M., P\u00e4nk\u00e4\u00e4l\u00e4, M., and Koivisto, T. (2017). Heartbeat detection using multidimensional cardiac motion signals and dynamic balancing. EMBEC & NBC 2017, Springer.","DOI":"10.1007\/978-981-10-5122-7_224"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1088\/0967-3334\/37\/11\/1885","article-title":"A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms","volume":"37","author":"Lehtonen","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"588","DOI":"10.3390\/a5040588","article-title":"An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals","volume":"5","author":"Scholkmann","year":"2012","journal-title":"Algorithms"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-Time QRS Detection Algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","first-page":"97843Y","article-title":"Demons versus level-set motion registration for coronary 18F-sodium fluoride PET","volume":"9784","author":"Rubeaux","year":"2016","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/S0140-6736(13)61754-7","article-title":"18F-fluoride positron emission tomography for identification of ruptured and high-risk coronary atherosclerotic plaques: A prospective clinical trial","volume":"383","author":"Joshi","year":"2014","journal-title":"Lancet"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2011). Principal Component Analysis, Springer.","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1002\/mp.12115","article-title":"Technical Note: Fast respiratory motion estimation using sorted singles without unlist processing: A feasibility study","volume":"44","author":"Jaewon","year":"2017","journal-title":"Med. Phys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:23:44Z","timestamp":1760189024000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,24]]},"references-count":30,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194137"],"URL":"https:\/\/doi.org\/10.3390\/s19194137","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,24]]}}}