Introduction

Cardiac self-gating (SG) approaches for free-running MRI of the heart^[1,2] have proven to be a robust and precise alternative to standard ECG gating that is based on R-wave detection.^[3] However, such SG methods typically only generate temporal triggers for binning the k-space data throughout the cardiac cycle, in contrast to respiratory SG signals, whose amplitude’s temporal evolution is exploited to inform binning for motion-resolved or motion-registered reconstructions.^[1-5] In this study, we investigated the hypothesis that the intervals of least variability of cardiac SG signals correspond to the resting period of the cardiac cycle during diastole. To this end, free-running data sets were acquired in healthy volunteers and patients, and two methods for retrospective reconstruction of 3D static images were investigated. First, conventional mid-diastolic data subset selection and reconstruction was performed using constant window width and subject-specific time delay after the trigger point. Second, using an adaptive, non-constant window width and time delay determined by the intervals of least variability of the cardiac SG signal for each individual cardiac cycle. Timing of selected data and quality of motion-suppression in reconstructed images were then quantitatively compared for both techniques.

Methods

This IRB approved study included 9 healthy volunteers (6F, 27.4±2.4y) and 5 patients (5M, 58.2±6.1y) who provided written informed consent. All examinations were performed on a clinical 1.5T MRI scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) with a prototype bSSFP free-running (non-ECG-triggered) 3D golden-angle radial sequence with (1.15mm)³ isotropic spatial resolution.^[1,4] Both respiratory and cardiac motion signals were extracted from the imaging data with a previously reported SG technique,^[1] and reconstructed as cardiac and respiratory motion-resolved 5D images using compressed sensing^[3] in order to visually confirm periods of quiescent cardiac motion. 3D static images were then reconstructed with 2 different approaches, both aiming at selecting about 12% (=30%×40% along the two independent cardiac and respiratory dimensions, respectively) of all acquired readouts, corresponding to 25% of the radial Nyquist limit, similar to previous 3D radial cardiac imaging techniques.^[4,6] For the first approach, a temporal window for readout selection was set with a fixed delay from the SG cardiac triggers, using the knowledge obtained from the 5D images, to reconstruct a diastolic 3D static image I_FIXED. For the second approach, without prior knowledge of the temporal localization of the diastolic phase within the acquired data, the defined fraction of all readouts was selected from the most quiescent portion of each cardiac cycle to reconstruct a 3D I_ADAPT image. To select the intervals of least variability of the cardiac SG signal $$$c$$$, we defined the following weighting function $$$w_{c}$$$:
$$w_{c}\left(i\right)=\sqrt{\left(\frac{c\left(i\right)-\bar{c}}{SD\left(c\left(i\right)-\bar{c}\right)}\right )^{2}+\left(\frac{{c}'\left(i\right)}{SD\left({c}'\left(i\right)\right)}\right)^{2}+\left(\frac{{c}''\left(i\right)}{SD\left({c}''\left(i\right)\right)}\right)^{2}}$$
where $$$i$$$ is the readout index, $$$\bar{c}$$$ is the mode of $$$c$$$ (i.e. most frequent value of $$$c$$$), $$${c}'$$$ and $$${c}''$$$ are the first and second derivatives of $$$c$$$, respectively, and $$$SD$$$ is the standard deviation. The 30% of readouts with the lowest weight were then selected (Fig.1). As for respiration, for both I_FIXED and I_ADAPT, the same 40% of end-expiratory data were selected based on the respiratory SG signal amplitude as previously reported.^[1-3,5] To test for consistency between the period of cardiac quiescence selected using a priori knowledge in I_FIXED and the automatic and adaptive selection in I_ADAPT, average delay and standard deviation from the corresponding cardiac SG triggers are reported. Then, image sharpness was measured for both reconstruction methods across all subjects using the slope of a sigmoid function fitted to multiple manually selected points at the left ventricular blood-myocardium interface and was statistically compared using a t-test with p<0.05 considered significant.^[7]

Results

On average and across all subjects, the temporal windows with a fixed delay for I_FIXED reconstructions were retrospectively selected at 604±74 ms after the cardiac SG triggers, while the readouts selected for I_ADAPT reconstructions were found at 594±94 ms (p=0.29) (Fig.2). Conversely, the readouts selected by the fixed window showed a significantly smaller deviation from the average trigger delay than the corresponding selection from the adaptive approach (85±9 ms vs 113±14 ms, p<0.0001) (Fig.3). The left ventricular blood-myocardium interface sharpness measurements showed highly consistent results with I_FIXED =2.71±0.31 and I_ADAPT =2.71±0.34 (p=0.99) (Fig.4-5).

Discussion

The proposed data selection procedure was able to robustly identify the resting phase of the cardiac cycle from the cardiac SG signal without image-based or ECG-derived a priori knowledge. The average, dynamic trigger delay that was automatically extracted for I_ADAPT matched that from I_FIXED very closely. The only difference was found in the delay variability of selected data, which was significantly higher for I_ADAPT due to the automated selection procedure that dynamically adapts to the variability of individual cardiac cycle durations throughout the entire acquisition. Blood-myocardium sharpness measurements also confirmed the effectiveness of the adaptive data selection as it perfectly corroborated the values measured for the fixed-window reconstructions.

Conclusion

This study suggests that there is currently unused information in cardiac SG signals that could be exploited to maximize the quality of motion-resolution in free-running cardiac images. This may help inform an adaptive, patient-specific binning strategy throughout the cardiac cycle that aims at optimizing cardiac motion suppression for all reconstructed phases, which may be of particular benefit for clinical cohorts displaying increased heart-rate variability.

Acknowledgements

No acknowledgement found.

References

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