INTRODUCTION

Ultrashort Echo Time (UTE) imaging has attracted attention as a promising radiation-free alternative to computed tomography (CT) for the lung imaging. However, the primary challenge for the lung MRI is motion artifacts caused by respiratory motion. In order to reduce motion artifacts, there is a way to hold the breath for a while, but the free-breathing fashion is preferable to obtain 3D lung images such as CT due to long scan time and better patient comfort. Retrospective self-gating (SG) methods for free breathing imaging have been studied using DC signals¹or low-resolution image series². However, in these methods, bandpass filtering, and low spatial resolution and semi-automatic problems have not yet been solved. In this study, we propose a new 1D projection profile with high temporal/spatial resolution and investigate the feasibility and superiority in a healthy volunteer.

METHODS

Figure 1 shows the 3D radial UTE pulse sequence with the spoiling and half-echo readout gradient without TR increase. The UTE consist of a series of non-selective hard RF pulse followed by center-out and -in radial oversampling with ramp-sampled trapezoid readouts to acquire dual echo images. The readout gradients are followed by the bifunctional gradient which is to acquire the half-echo signal along the slice selection direction to monitor respiratory motion and to spoiling the residual transverse magnetization without TR increase. The half-echo (HE) signals are Fourier-transformed to 1D projection profiles in the superior-inferior direction. The diaphragm moving amount (HE-SG) was estimated from the 1D projection profiles by performing a cross-correlation between the first profile and the others. The principal component analysis (PCA) was performed to get the coil combined HE-SG signal. This method was implemented on a 3.0T clinical MRI scanner (MAGNETOM Prisma fit, Siemens Healthcare, Erlangen, Germany) equipped with a 30-element body coil and 24-element spine coil. Acquisition parameters were as follows: field of view (FOV) = 32 × 32 × 32 cm³, flip angle = 4°, 1.25 mm isotropic resolution, sampling bandwidth = 1220 Hz/pixel, readout duration = 0.41 ms, repetition time (TR) = 2.3 ms, TE1 = 0.06 ms, TE2 = 1.23 ms, TE3 (Half-Echo) = 1.26 ms, base resolution = 256, number of radial spokes = 165000 and total scan time = 6.3 min.

RESULTS

Three types of SG signal were compared in Figure 2: Half-echo-based projection (HE-SG), DC-based (DC-SG) and image-based (IM-SG) SG signal. The IM-SG signal was retrieved from 3D low-resolution image series with a sliding window of 300 radial spokes, a temporal resolution of 660 ms (Fig.2a). To estimate respiratory motions, the central three slices of the thorax were averaged and a line for the identification of the navigator position drawn manually over the lung–liver interface. (Fig.2b). The HE-SG has a similar respiratory pattern compare to the IM-SG, but DC-SG differs from the others on histograms and SG patterns (Fig. 2d). The motion suppressed lung images were reconstructed using accepted data as the same respiratory phase of each SG signal (gray zone in Fig.2d) is shown in Fig.3. The liver–lung interface is severely blurred in ungated reconstruction image than SG applied images. Among them, the image of the HE-SG most clearly shows the vascular structures of lung lower lobe and lung-liver interface. Dynamic respiratory phase images from the inspiration to expiration phase were shown in Fig.4.

DISCUSSION & CONCLUSION

In this study, we proposed a new self-gating signal to obtain a high-resolution 3D lung UTE image with suppressed respiratory motion artifacts.This HE-SG signal can track the position of the diaphragm with high spatial and temporal resolution acquired for every TR without increasing the scan time. Also, this HE-SG can automatically estimate the respiratory motion of the subject so that it can be reconstructed seamlessly for high-quality lung images or time-resolved lung images.

Acknowledgements

This work was supported by the National Research Foundation of Korea (RF) grant funded by the Korea government (MISP) (No. 2016R1C1B1013837)