Article
A Computational Framework for Data Fusion in
MEMS-Based Cardiac and Respiratory Gating
Mojtaba Jafari Tadi
1,
*, Eero Lehtonen
1
, Jarmo Teuho
2
, Juho Koskinen
1
, Jussi Schultz
2
,
Reetta Siekkinen
2,3
, Tero Koivisto
1
, Mikko Pänkäälä
1
, Mika Teräs
3,4
and Riku Klén
2
1
Department of Future Technologies, University of Turku, 20500 Turku, Finland;
eero.lennart.lehtonen@utu.fi (E.L.); juanko@utu.fi (J.K.); tejuko@utu.fi (T.K.); mtpank@utu.fi (M.P.)
2
Turku PET Centre, Turku University and Turku University Central Hospital, 20500 Turku, Finland;
Jarmo.Teuho@tyks.fi (J.T.); jischu@utu.fi (J.S.); Reetta.Siekkinen@tyks.fi (R.S.); riku.klen@utu.fi (R.K.)
3
Department of Medical Physics, Turku University Central Hospital, 20500 Turku, Finland; mika.teras@tyks.fi
4
Deparment of Biomedicine, University of Turku, 20500 Turku, Finland
* Correspondence: mojtaba.jafaritadi@utu.fi; Tel.: +358-409339252
Received: 12 July 2019; Accepted: 18 September 2019; Published: 24 September 2019
Abstract:
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.
Keywords: data fusion; dual gating; MEMS accelerometer and gyroscope; cardiac PET
Sensors 2019, 19, 4137; doi:10.3390/s19194137 www.mdpi.com/journal/sensors
