In respiratory self-navigation (SN)^1,2, static structures, such as the arms or chest wall, may complicate motion detection due to the superposition of tissue signals. Even if motion detection is successful, the subsequent rigid motion correction may introduce streaking artefacts when applied to static structures. Suppressing signal from those tissues may therefore improve image quality. In this study, we test the hypothesis that SN coronary MRA will benefit from the introduction of an outer volume suppressing "2D-T₂-Prep"³, and present both phantom and in vivo results demonstrating this.

The first RF pulse of an adiabatic T₂-Prep^4,5 was replaced with a jinc pulse and spiral gradients (Fig. 1). This selectively excites a cylindrical volume^6-8 (Fig. 4). Meanwhile, the final RF pulse remains non-selective. It thus restores the cylinder of T₂-prepared magnetization, while rotating outer magnetization into the transverse plane, where it is then spoiled. This "2D-T₂-Prep" and its conventional non-selective counterpart were used as magnetization preparation modules prior to a prototype free-breathing 3D-radial SN sequence⁹, specifically adapted to SN via the collection of a superior-inferior (SI) readout at the start of each data interleave.

Respiratory displacement was corrected by introducing a phase shift directly into k-space for each radial projection, prior to reconstruction. The shift was determined by tracking the blood pool with either all coils or a user-selected optimal subset ("best" coils), first using an automated blood pool segmentation² and then using an iterative approach¹⁰. All images were collected on a 1.5 T clinical scanner (MAGNETOM Aera, Siemens Healthcare), with a bSSFP readout, 18 channel chest coil and 12 channel spine coil, (1.15 mm)³ isotropic voxels, FoV (220 mm)³, matrix size 192³, TE T₂-Prep = 40 ms, RF excitation angle 110°, 16 readouts/heartbeat, and TE/TR/T_acq=1.82/3.63/58 ms.

The performance of both the conventional and the 2D-T2-Prep were first compared by imaging a custom, home-built moving cardiac phantom (Fig. 2), containing a mock blood pool, myocardium, coronary artery, and static chest wall. The motion detection efficacy was measured via analysis of the mean gradient of the blood pool boundary in the SI projections after correction (Fig. 3). The phantom had a simulated ECG signal of 60 BPM and a "respiratory" displacement of +/- 2 cm, with a frequency of 17 rotations/minute, and was imaged 5 times for each T2-Prep technique. Next, MRA of the right coronary artery (RCA) was performed in 10 healthy volunteers. For both phantoms and volunteers, SNR was measured in the blood pool and myocardium. In volunteers, CNR was also measured between blood, myocardium, and lungs. In the mock and right coronary, vessel sharpness (VS) was determined with Soapbubble¹¹. A paired two-tailed student’s t-test was used to compare results from the conventional T₂-Prep+SN and the 2D-T₂-Prep+SN, with p<0.05 considered statistically significant.

In the moving phantom, all motion detection approaches (automated & iterative) and coil combinations were successful if the 2D-T₂-Prep was used. However, for the conventional T₂-Prep, motion correction failed unless both a selected coil subset (the "best" coils) and iterative motion detection were used. However, the 2D-T₂-Prep still outperformed the conventional T₂-Prep in the best coils + iterative case, increasing blood SNR by 53% (58.5 vs. 23.6), myocardial SNR by 47% (27.7 vs 12.6), and VS by 7.5% (58.0 vs. 62.3). Likewise, the mean SI projection gradient increased by 10.8% (all p<0.05).

In volunteers, the 2D-T₂-Prep maintained high signal in the region targeted by the 2D-selective pulse, but exterior signal, in the chest and lungs, was clearly attenuated (Fig. 4). High T₂ contrast could also be observed between the blood pool and myocardium, and both the left and right coronary arterial system could be visualized and analyzed. Consistent with these observations, a high blood-myocardium CNR was measured for both approaches, though the CNR of the 2D-T₂-Prep was significantly higher (Fig. 5). Similar results were found for blood-lung CNR and myocardium-lung CNR. As compared to the conventional T₂-Prep, the 2D-T₂-Prep also significantly improved SNR of the blood pool and myocardium. These improvements were true regardless of the motion detection approach used, and regardless of the coil subsets selected (Fig. 5). Additionally, when analyzing the RCA (automated segmentation, best coils, as shown in Fig. 4), VS increased by 34% (29.30 vs. 39.26) when using the 2D-T₂-Prep (p<0.05).

As compared to a conventional T₂-Prep, the 2D-T₂-Prep significantly improved SNR and CNR between all measured tissues, for both automated and iterative motion detection approaches, and regardless of coil selection, and improved VS when performing self-navigated coronary MRA. We hypothesize that these improvements may be due to the suppression of extraneous signal, such as from the chest wall, which would otherwise contribute to streaking artefacts and/or motion artefacts secondary to cardiac displacement correction. When these artefacts are suppressed, the apparent background noise and streaking are reduced, thereby improving both SNR and CNR. Although background signal suppression was effective in this study, it remained imperfect outside of the region selected by the 2D-T2-Prep. Based on previous investigations³, we hypothesize that this may be related to B­₁ inhomogeneity in the non-selective T₂-Prep restoration pulse (i.e. at the end of the T₂-Prep) and due to T₁ signal recovery after spoiling. In both cases, however, further background suppression may lead to even greater SNR and CNR improvements. The finding that the 2D-T₂-Prep also improved vessel sharpness may suggest that motion correction was more effective when using the 2D-T₂-Prep, a result that was supported by the increased sharpness of the blood pool tracking gradient. The overall improvements in image quality suggest that a 2D-T₂-Prep should be considered for use in self-navigation, regardless of the motion tracking approach used.