Introduction

The sequences of various methods have been proposed for whole heart coronary magnetic resonance angiography (WHC-MRA)1-4. In conventional WHC-MRA, it is typically performed during free-breathing, and respiratory motion is prospectively compensated by using a 1D-right-diaphragmatic-navigator (NAV), measuring motion in the FH-direction5. However, the limitations of the NAV-method are the cumbersomeness of setting the NAV on the right-diaphragm and giving stresses to patients by wrapping the compression-band around the chest/abdomen for minimizing respiratory artifacts. Image-based-2D-navigator (iNAV) has the possibility to solve these problems. The iNAV allows prospective correction of translational motion of the heart in two-directions (R-L, F-H) by using 2D-real-time imaging to track the region surrounding the coronary arteries throughout the cardiac cycle5 (Fig. 1-2). Furthermore, this method allows direct motion estimation and correction of respiratory induced motion of the heart, obviating the need for a diaphragmatic motion model6. Consequently, the iNAV enables more robust suppression of respiratory motion artifacts for WHC-MRA compared with conventional NAV, and images can be acquired in shorter time and with improved image quality5-7. Therefore, by using iNAV, we hypothesized that the image quality of WHC-MRA can be maintained while shortening the scan time even if the abdominal compression-band is loosened. In this study, we investigated the feasibility of iNAV with a loosely wrapped compression-band for WHC-MRA by comparing it with conventional NAV.

Methods

A total of six volunteers (6 males; age range: 23~44) were examined on a 1.5T MRI (Ingenia CX, Philips Healthcare). The study was approved by the local IRB, and written informed consent was obtained from all subjects. We used 3D-balanced-TFE (bTFE) for WHC-MRA images. Using bTFE as a base sequence, we compared NAV-approach and iNAV-approach with loosely or tightly wrapped abdominal compression-band (NAV-loose/NAV-tight/iNAV-loose/iNAV-tight). For iNav scans, motion corrections for both head-foot and left-right directions were enabled. For respiratory gating in iNav, we applied Constant Respiratory efficiency UsIng Single End-expiratory threshold (CRUISE) algorithm8. CRUISE assumes a fixed scan efficiency of 50%. Therefore, the total scan time will be exactly double of the displayed time. The imaging-parameters of bTFE: FOV=300×300mm, voxel-size=1.6×1.6×1.6mm, SENSE-factor=2.4, TR/TE/FA=2.2/1.1/80. Scan time was recorded for each sequence. Image quality was evaluated by visual score at the 10-points (RCA: #1/2/3/4 LAD: #5/6/7/8 CX: #11/13) based on American-Heart-Association classification. We evaluated them as 4-point grades (grade “4” was excellent, “1” was severe) by two blinded readers. For quantitative comparison, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured. The SNR was assessed in the blood, epicardial fat and myocardium. To allow quantitative SNR measurements, we used a noise-measurement-method proposed by Zwanenburg et al9. Each sequence was repeated with exactly the same receiver gain, but without any RF and gradient-pulses. The reconstructed images showed only noise, including the noise added due to the SENSE-reconstruction. The standard-deviation of a region of interest of the corresponding area in the noise image was used as metric for the noise. SNRblood, SNRepicardial fat and SNRmyocardium were then calculated as follows: SNRA = SI(A) / SDnoise(A) Where SI are the mean average signal intensity of the blood, epicardial fat and myocardium respectively, and the corresponding SDnoise is the standard-deviation at the same location on the noise images. Subsequently, we measured the CNR for comparing image contrast quantitatively. The CNR was estimated for blood and epicardial fat (CNRblood-epicardial fat). The CNRblood-epicardial fat was calculated by the following equations: CNRA-B = [SI(A) - SI(B)] / 0.5 [SDnoise(A) + SDnoise(B)] The SNR and CNR were assessed by one-way repeated measures analysis of variance (ANOVA) and the post- hoc Tukey-Kramer test.

Results

Figure 3 shows the representative images using NAV-loose, NAV-tight, iNAV-loose and iNAV-tight. The results of visual score and average scan time are shown in Figure 4. The iNAV-approach showed significantly higher than NAV-approach regarding RCA, LAD, except for the comparison between NAV-tight and iNAV-loose. Regarding CX, there was no significant difference in the comparison of all sequences. Average scan time was significantly shorter iNAV-approach compared with NAV-approach, except for the comparison between NAV-tight and iNAV-loose. Figure 5 shows SNR and CNR comparison among four sequences. For the SNRblood, there was no significant difference in the comparison of all sequences. For the SNRepicardial fat, iNAV-approach showed significantly lower value compared to NAV-approach. For the SNRmyocardium, there was no significant difference in the comparison of all sequences. For the CNRblood-epicardial fat, iNAV-approach showed significantly lower value compared to NAV-approach.

Discussion

From the results of SNR and CNR, it is considered that iNAV-approach has a better contrast between arteries and background fat tissues than NAV-approach. We considered that iNAV-approach has higher fat suppression effect than NAV-approach because the additional SPIR-pulse in the iNAV might affect the results of fat suppression. Therefore, it is considered that iNAV-approach showed a higher value in the visual score than NAV-approach.

Conclusion

By using iNAV for WHC-MRA, image quality could be maintained without tightly wrapping the abdominal compression-band, and the scan time could be shortened compared to the conventional NAV-approach. Therefore, iNAV can reduce patients’ stress. In addition, iNAV eliminates the cumbersomeness of setting the NAV on the right diaphragm, reducing the burden on the scanner. Therefore, iNAV could improve the workflow of WHC-MRA.

Acknowledgements

No acknowledgements found.

References

(1) Kazuo Kodaira, et al. Whole heart coronary MRA with 3D non-selective bSSFP-DIXON: comparison with conventional methods. Proceedings of the Virtual Conference & Exhibbition of ISMRM (2020). (2) Oliver M. Weber, PhD et al. Free-Breathing, Three-Dimensional Coronary Artery Magnetic Resonance Angiography: Comparison of Sequences. Journal of Magenetic Resonance Imaging. 2004; 20: 395-402. (3) Yuji Iyama et al. Single-Breath-Hold Whole-heart Unenhanced Coronary MRA Using Multi-shot Gradient Echo EPI at 3T: Comparison with Free-breathing Turbo-field-echo Coronary MRA on Healthy Volunteers. Magn Reson Med Sci. 2018; 17: 161-167. (4) Teresa Correia et al. Technical note: Accelerated nonrigid motion-compensated isotropic 3D coronary MR angiography. Med Phys. 2018 Jan; 45(1): 214-222. (5) Markus Henningsson et al. Prospaective Respiratory Motion Correction for Coronary MR Angiography Using a 2D Image Navigator. Magn Reson Med. 2013; 69: 486-494. (6) Markus Henningsson et al. Whole-Heart Coronary MR Anigiography Using Image-Based Navigation for the Detection of Coronary Anomalies in Adult Patients With Congenital Heart Disease. Journal of Magenetic Resonance Imaging. 2016; 43(4): 947-955. (7) Markus Henningsson et al. Whole-Heart Coronary MR Angiography With 2D Self-Navigated Image Reconstruction. Magn Reson Med. 2012; 67: 437-445. (8) Markus Henningsson et al. Coronary MR Angiography Using Image-Based Respiratory Motion Compensation With Inline Correction and Fixed Gating Efficiency. Magn Reson Med. 2018; 79: 416-422. (9) Zwanenburg et al. MR Angiography of the cerebral perforating arteries with magnetization prepared anatomical reference at 7T: comparison with time-of-flight. J Magn Reson Imaging. 2008; 28: 1519–1526.