Thoracic radiologists, engineers working on reconstruction and motion correction

Structural pulmonary imaging with MRI has potential to reduce radiation dose from repeated CT scans, particularly in pediatric diseases requiring longitudinal follow-up, such as cystic fibrosis. However, structural lung MRI has seen limited clinical success, due to the combined factors of short T2*, low proton density, and respiratory and cardiac motion. Recently, 3D UTE sequences have been proposed with optimized SNR efficiency from the data acquisition¹ and respiratory gating based on a respiratory belt. However, respiratory belt signals are often not robust or may include significant signal drift. This leads to suboptimal respiratory gating and produces motion blur in the resulting images. Moreover, traditional respiratory gating often results in low imaging efficiency.

The purpose of this study is to optimize the reconstruction of 3D UTE MR pulmonary imaging using 1) a self-gating technique to measure respiratory motion from MRI data itself (DC based self-gating) ^2,3, 2) a soft-gating technique to penalize the motion state inconsistency ⁴, and 3) a motion-resolved technique to provide images of all respiratory motion states,⁵ and combine them with L1-ESPIRiT, ⁶ an autocalibrating parallel imaging and compressed sensing method.

We evaluated the proposed methods across different applications: detecting pulmonary nodules, cystic fibrosis and demonstrated the initial feasibility of the proposed method.

Optimized 3D UTE SPGR sequence with slab excitation and a bit-reversed pseudo-random view ordering from Johnson et al. ¹ was implemented on a 3T (GE Healthcare) scanner at two sites. Free-breathing scans on a healthy volunteer as well as on 6 patients were performed across sites, with prescriptions similar to the following: field of view (FOV) = 32×32×30 cm³, TE/TR = 70 μs / 2.932 ms, flip angle = 4º, 1.25 mm isotropic resolution and total acquisition time is 5 min 14 s.

We extracted the respiratory motion from the acquired data by applying a low-pass filter on k-space center signal over time ($$$\|k_0\|$$$). For the soft-gating technique, we applied exponential decay weighting,⁴ to penalize motion state inconsistency, resulting in an effective undersampling ratio of 2.5:

$$w = \left\{\begin{array}{ll} e^{-\alpha(d - Thresh)} & \textrm{otherwise} \\ 1 & d \le Thresh \end{array} \right. \\ \text{ d is the respiratory motion estimation, $\alpha$ is scaling factor}\\ \text{ w is the soft-gating weights. } $$

For the motion-resolved reconstruction,⁵ we divided the data equally into 5 motion states and enforced temporal smoothness between motion states by adding total variation norm along respiration dimension on the objective function. Additionally, we incorporated both methods with L1-ESPIRiT, a parallel imaging and compressed sensing method to better exploit the inherent incoherency from the bit-reversed UTE acquisition. Reconstruction and motion correction models are shown in Figure 1. Due to current memory limits on our workstations, the motion-resolved images were reconstructed only on the volunteer with reduced isotropic resolution of 1.6 mm. We compared the soft-gating and motion-resolved L1-ESPIRiT ⁶ reconstruction with the traditional reconstructions, gated/non-gated gridding with density compensation, by evaluating qualitative SNR and image sharpness. All the reconstructions were carried out using the open source Berkeley Advanced Reconstruction Toolbox (BART: https://mikgroup.github.io/bart/). ⁷Figure 2 shows the comparison of traditional reconstructions (left and middle) with our soft-gating reconstruction (right). Red arrows point out the comparison where vessels and fine structures were blurred out by the respiratory motion when the traditional reconstruction was used, while the soft-gating technique was able to resolve the fine structures and diaphragm. Yellow arrows show the streaking artifacts due to gating induced undersampling when parallel imaging and compressed sensing were not applied. Figure 3 shows the motion-resolved images of all the motion states (from left to right) from the same dataset, and they also show greatly improved sharpness over the traditional reconstructions. Soft-gating with L1-ESPIRiT has qualitatively improved SNR than each motion state of motion-resolved result, while motion-resolved results provide all the motion states for comprehensive evaluations. Figure 4 shows the comparison of traditional reconstructions with the soft-gating reconstruction on a cystic fibrosis patient. Figure 5 shows clinical examples of a 4 mm pulmonary nodule (as green circle indicates) and cystic fibrosis (as the red circle indicates).In our study, DC based self-gating was reliable in extracting motion signal from MR data. Both soft-gating and motion resolved techniques addressed the motion issues for pulmonary imaging effectively. Given the optimized 3D­ ­UTE sequence combined with the optimized reconstruction, MRI could become a clinical standard for pulmonary imaging. In the spirit of reproducible research, we will provide the corresponding reconstruction code online in BART. ⁷