Introduction/Purpose:

MRI lung imaging has emerged as a promising alternative to computed tomography due to its capabilities in the visualization and assessment of functional and morphological information, without the use of ionizing radiation^1,2. However, short T2* relaxation times and low proton densities in the lung lead to low SNR, with scan durations being limited by maximum breath-hold durations. Dedicated sequences such as ultrashort echo time (UTE³) and zero echo time (ZTE⁴) better exploit the available signal and have furthermore been optimized for lung imaging^5-9. While breath-hold imaging is possible for healthy and cooperative subjects^10,11,12, patients with lung diseases often exhibit limited breath-hold capabilities, leading to the need of free-breathing acquisitions in combination with motion compensation strategies. Traditional self-gating approaches rely on the identification of a characteristic frequency and thus perform best for uniform motion, but often irregular breathing patterns due to anxiety, shortness of breath or coughing are observed. Therefore, we adapted the nonuniform self-gating (nuSG) framework^13,14,15 for lung imaging and evaluated the method in six healthy volunteers, comparing the results to established k-space¹⁶ and image-based^9,17 gating techniques.

Methods:

All imaging experiments were performed on a clinical 1.5 T whole-body MRI system (Achieva 1.5 T, Philips Healthcare, Best, The Netherlands) using a dedicated 32-channel cardiac coil. A 2D UTE sequence (parameters are given in Table 1) was combined with a tiny golden angle (tyGA) acquisition scheme^18,19 to minimize motion sensitivity, eddy current related artefacts and to provide a better angular coverage for sliding-window and self-gating reconstructions. At two coronal slice locations two breath-hold acquisitions (inspiration and expiration) and two free-breathing acquisitions with uniform and irregular respiratory patterns were performed. The free-breathing acquisitions were reconstructed using nuSG^13,14,15, image-based self-gating⁹ and a k-space center based self-gating¹⁶ (Figure 1). To obtain quantitative information on lung density, proton fraction values (PF) in inspiration and expiration were pixelwise calculated according to $$PF = \frac{SI_{lung}}{SI_{muscle}}\textrm{exp}\left(\frac{TE}{T^*_2}\right),$$ with SI being the signal intensity of the lung parenchyma and intercostal muscle, TE the echo time and $$$T^*_2$$$ = 2.11 ms²⁰. After nonrigid image registration of exhaled and inhaled motion states using the Medical Image Registration Toolbox for Matlab (MIRT)²¹, fractional ventilation maps (FV) were calculated according to$$FV = \frac{SI_{EX}-SI_{IN}}{SI_{EX}}$$ To evaluate the residual motion blur after gating, image sharpness was analysed based on a standard metric (e.g. ^{13, 22}) that identifies the position of 25% and 75% of the maximum signal intensity on an interpolated intensity profile placed over the lung liver interface. For image sharpness analyses, the lung-liver interface is divided into three areas that are automatically detected by slope and variance analyses. The areas are described by linear equations and the x-distance of the intersections is chosen as a measure for sharpness.

Results:

Figure 2 shows reconstructed coronal slices of all three gating approaches. For inspiration, image sharpness of the nuSG approach is clearly superior to img-based and k-space-based self-gating. Further nuSG allows the reconstruction of the breathing motion over the entire acquisition period (Figure 3). All quantitative measurements are summarized in Figure 4. Both sharpness measures show the superiority of nuSG and img-based self-gating. Sharpness for breath-hold images is higher in expiration compared to inspiration. nuSG sharpness is on a similar level across both motion states and both respiratory patterns, with proton fractions being overestimated. Differences between inspiration and expiration are noticeable for breath-holds, image-based gating and to a lesser extent in nuSG and k-space based SG. Concerning fractional ventilation, nuSG comes closest to the BH reference, with a slight underestimation.

Discussion:

The presented results are in accordance with previous studies (9, 17) and indicate that k-space based self-gating is clearly outperformed by image-based and non-uniform self-gating. Especially for irregular respiration with varying ex- and inspiratory positions, the lung liver interface appears blurred in k-space based-SG. The reduced inspiratory image sharpness for the breathhold approach is most likely caused by respiratory drifts. For uniform breathing patterns the image-based approach performed excellent but partly fails for irregular breathing. Non-uniform self-gating works equally well for data acquired during uniform and irregular breathing, thus exhibiting better sharpness measures for inspiration even compared to the BH reference. The higher proton fraction values in the nuSG reconstructions could be due to the reconstruction with less data. The resulting lower SNR increases the influence of noise and could lead to an increased lung signal in magnitude images. However, there seems to be no negative effect on FV. As the difference between inspiration and expiration is typically higher in BH imaging compared to free breathing, it is obvious that the calculated FV is higher compared to gated imaging. While nuSG and image-based SG yield similar FV, ksp-based SG yields reduced values, most likely due to residual image blur.

Conclusion:

The nuSG approach in combination with 2D-tyUTE achieves functional values in line with the image-based SG approach as well as with the reference BH technique, while offering the advantage of intrinsically reconstructing a movie of the underlying time course of the motion, yielding a possible time-resolved evaluation of lung parenchyma changes even for highly non-uniform motion.

Acknowledgements

The authors thank the Ulm University Centre for Translational Imaging MoMAN for its support.