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Article

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating

1
Department of Future Technologies, University of Turku, 20500 Turku, Finland
2
Turku PET Centre, Turku University and Turku University Central Hospital, 20500 Turku, Finland
3
Department of Medical Physics, Turku University Central Hospital, 20500 Turku, Finland
4
Deparment of Biomedicine, University of Turku, 20500 Turku, Finland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4137; https://doi.org/10.3390/s19194137
Received: 12 July 2019 / Revised: 6 September 2019 / Accepted: 18 September 2019 / Published: 24 September 2019
(This article belongs to the Collection Multi-Sensor Information Fusion)
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 ± 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. View Full-Text
Keywords: data fusion; dual gating; MEMS accelerometer and gyroscope; cardiac PET data fusion; dual gating; MEMS accelerometer and gyroscope; cardiac PET
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MDPI and ACS Style

Jafari Tadi, M.; Lehtonen, E.; Teuho, J.; Koskinen, J.; Schultz, J.; Siekkinen, R.; Koivisto, T.; Pänkäälä, M.; Teräs, M.; Klén, R. A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating. Sensors 2019, 19, 4137. https://doi.org/10.3390/s19194137

AMA Style

Jafari Tadi M, Lehtonen E, Teuho J, Koskinen J, Schultz J, Siekkinen R, Koivisto T, Pänkäälä M, Teräs M, Klén R. A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating. Sensors. 2019; 19(19):4137. https://doi.org/10.3390/s19194137

Chicago/Turabian Style

Jafari Tadi, Mojtaba, Eero Lehtonen, Jarmo Teuho, Juho Koskinen, Jussi Schultz, Reetta Siekkinen, Tero Koivisto, Mikko Pänkäälä, Mika Teräs, and Riku Klén. 2019. "A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating" Sensors 19, no. 19: 4137. https://doi.org/10.3390/s19194137

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