Appendix
Data acquisition
We developed software capable of reading, decoding, analyzing, modifying and encoding
Inveon list-mode files, as described previously(1). We used this software to decode all gating
and time packets included in the list-mode file for export to a CSV file, in order to remove
contained respiratory trigger events and to encode the new ECG-based respiratory trigger
events. ECG- and respiratory-signals (RRS) were registered using a dedicated physiological
monitoring system (BioVet; m2m Imag. Corp., Cleveland, OH, USA). The BioVet system
analyzed the ECG and respiratory signals in on-line mode and produced trigger events as
input to the PET scanner, which encoded them into the PET list-mode data stream. In
addition the BioVet system allowed storage in a CSV file of the entire ECG and respiratory
signals with a sampling rate of 1 ms together with the trigger events.
Data preparation
Visualization and analysis of the trigger quality and processing of the ECG- and respiratorysignal was provided thorough MATLAB (The MathWorks, Natick, USA) software developed
in-house. The cardiac trigger events stored in the BioVet’s CSV file and the CSV file
extracted from the PET list-mode data were synchronized for further processing as described
previously(1). The following sections describe the essential processing steps for generating
ECG-based respiratory trigger events. The proposed algorithm exploits the modulations of
the ECG signal which are assessable by finding fluctuations in the ECG-amplitude and the
time-of-occurrence of the cardiac beats. The processing parameters reported in these
various steps (e.g. filter frequencies, etc.) were chosen based on earlier experience, and set
as identical for all animals. Illustrations in figures 1 and 2 show a 20- and a 5-second period
of time from the measurement of animal 1 in table A.1. ECG signals were initially corrected
for bias by filtering with a high-pass frequency filter with a cutoff frequency of 5 Hz. A global
bias correction of the respiratory signals (RRS) was performed by subtracting their global
mean, and noise caused by signal spill-over from the ECG was suppressed by applying a
moving average filter of 51 ms width. An example of the data resulting after preprocessing is
depicted in figure A.1 (top: ECG; bottom: RRS).
Peak detector
Trigger events were generated on each R-peak in the ECG signal and on each maximum in
the RRS signal. The peak detector algorithm was designed to be simple and robust. It was
based on finding upward peaks in a signal exceeding a certain threshold. Since biases were
already removed from both signals during preprocessing, we defined the threshold for the
peak search from each signal’s global mean plus 1.5 times its standard deviation. Any
maximum in a signal found above the corresponding threshold was considered as a valid
peak (i.e. R-peak, or end-inspiration) if two more conditions were fulfilled: the signal following
the maximum dropped about 1.5 (ECG), 2.0 (RRS) or 0.5 (ERSt and ERSa) times the
standard deviation of the global mean, and the previous peak was found at least 30 ms
(ECG) or 100 ms (RRS) prior to that event, as is implemented as the so-called “inhibit” in
many on-line peak detectors. Hereby artefacts or artificial TEs (e.g. small maxima in signals
between the true respiratory peaks) are excluded by using the inhibit during the processing
of the respiratory signal.
ECG-based respiratory curves
Figure 1 illustrates the basic signals which were generated and processed in this work.
Clearly noticeable are the respiration-induced changes of R-peak amplitude (fig. 1, top, red
line) and the almost simultaneously occurring alternations of the cardiac cycle period (fig. 1,
middle), both in approximate synchrony with the measured respiratory signal (fig. 1, bottom).
This figure demonstrates that two surrogate signals could be generated from the detected
ECG trigger events to estimate ECG-based respiratory signals: the first consisted of the
amplitudes of the R-peaks in the ECGs, the second consisted of the duration of each cardiac
cycle. The sequences of R-peak amplitudes and cardiac cycle periods were extracted,
normalized by division by a moving average filter with a window width of 51 ms so that
signals varied around a value of 1, and interpolated using a cubic spline function with 1 ms
sampling rate to generate the two ECG-based respiratory signals ERSa (amplitude) and
ERSt (time or duration). Both signals were subtracted by their global mean to receive signals
varying around a value of 0. If necessary a signal was inverted so that the respiratory peaks
associated with the end-inspiration phase pointed upward.
ECG-based respiratory triggers
The above described peak detector was applied to both signals to generate the ECG-based
respiratory trigger events. Figure 2 illustrates the final ECG-based respiratory signals ERSa
(top), ERSt (middle) and the original respiratory signal RRS (bottom) from the belt
measurements. For each signal the peak detector’s threshold and the resulting trigger events
are superimposed. As final step the time of each ECG-based respiratory trigger event was
stored in a list in CSV file format. The respiratory gating packets which were contained in the
original PET list-mode file were removed and the new ECG-based gating packets encoded to
allow for reconstruction of ECG-based respiration-gated images.
Representative ECG-based respiratory triggers processing of human data
We acquired representative ECG and respiratory signals from a healthy young human
subject using a clinical ECG trigger device (101NR patient monitor, Ivy Biomedical Systems,
Branford, CT) and respiratory trigger device (AZ-733V, Anzai Medical Co., Ltd, Tokio,
Japan). The signals from both devices were collected by software written in LabView
(National Instruments, Austin, TX) and stored in CSV files similar to the methods described
above. Processing of these data was performed as above for the generation of ECG-based
respiratory trigger events, with slight adaptions of the peak detector criteria as required for
this specific setup: in particular, the signal drop following a maximum had to be 2.0 times the
standard deviation of the global mean for the RRS, and the inhibit durations were set to 300
ms (ECG) and 500 ms (RRS). Since a paradoxical respiratory sinus arrhythmia had been
observed in isoflurane anaesthetized rats, the ERSt signal had to be inverted prior to
searching for respiratory peaks in awake human data.
In analogy to figure 1, figure 3 depicts the ECG and respiratory signals of the human subject
for a period of 120 seconds. Figure 4 illustrates the final ECG-based respiratory signals
ERSa (top), ERSt (middle) and the original respiratory signal RRS (bottom) from the belt
measurements. The peak detector’s threshold and the resulting trigger events, which were
utilized for retrospective respiratory gating of PET list-mode data, are superimposed.
Figure 1. Illustrative 20 second recordings of the physiological signals of rat 1 as investigated
in this work. Top: ECG signal (green), cardiac trigger events (blue dots) and spline
interpolation of cardiac trigger event amplitudes (red). Middle: Spline interpolation of cardiac
cycle periods. Bottom: Respiration signal from belt measurements.
Figure 2. Final respiratory signals of rat 1 based on ECG-amplitude (ERSa, top) and cardiac
cycle period (ERSt, middle) and the original respiratory curve from belt measurements
(bottom). Peak detector thresholds are shown as blue lines, the corresponding trigger events
as blue dots.
Figure 3. Illustrative 120 second recordings of the physiological signals of the human subject.
Top: ECG signal (green), cardiac trigger events (blue dots) and spline interpolation of cardiac
trigger event amplitudes (red). Middle: Spline interpolation of cardiac cycle periods. Bottom:
Respiration signal from belt measurements.
Figure 4. Final respiratory signals of the human subject based on ECG-amplitude (ERSa,
top) and inverted cardiac cycle period (ERSt, middle) and the original respiratory curve from
belt measurements (bottom). Peak detector thresholds are shown as blue lines, the
corresponding trigger events as blue dots.
Table 1:
Mean cardiac and respiratory cycle period are presented along with the corresponding
standard deviation (SD) for all 15 rodents. For respiratory cycle period the relative standard
deviation (RSD) is presented as well. Inadequate triggered datasets (marked in red) were not
used for calculating the overall means and standard deviations in the bottom row. Blank cells
indicate cases for which a reasonable delay could not be calculated.
Table 2:
Mean cardiac and respiratory cycle period are presented along with the corresponding
standard deviation (SD) in seven healthy volunteers. For respiratory cycle period the relative
standard deviation (RSD) is presented as well.
Table 3:
Estimates for EDV, ESV, SV and EF are presented as mean ± standard deviation for
respiratory gate 1, 2 and 3 in a subset of animals (N=8) which demonstrate the effects on the
heart during breathing. From gate 1 to gate 2 volumes increase in the expiration phase while
decreasing again during inspiration from gate 2 to 3, as discussed in the manuscript. Due to
the motion within the gates 1 and 3 (see also figure 1 in the manuscript) data seems to be
less reliable then data from gate 2 due to artifacts. Thus we opted for gate 2 for further
evaluation of the respiratory trigger signal.
For detailed information regarding the triggering algorithm please contact:
Dr. Guido Boening
Department of Nuclear Medicine, University of Munich
Marchioninistr. 15, 81377 Munich, Germany
+49-(0)89-4400-74657 (phone)
+49-(0)89-4400-74676 (fax)
Guido.Boening@med.uni-muenchen.de
(1)
Boning G, Todica A, Vai A, Lehner S, Xiong G, Mille E et al. Erroneous cardiac ECGgated PET list-mode trigger events can be retrospectively identified and replaced by
an offline reprocessing approach: first results in rodents. Phys Med Biol
2013;58:7937-59.