Motion-compensated low-rank reconstruction for simultaneous structural and functional UTE lung MRI

Abstract

Purpose: Three-dimensional UTE MRI has shown the ability to provide simultaneous structural and functional lung imaging, but it is limited by respiratory motion and relatively low lung parenchyma SNR. The purpose of this paper is to improve this imaging by using a respiratory phase-resolved reconstruction approach, named motion-compensated low-rank reconstruction (MoCoLoR), which directly incorporates motion compensation into a low-rank constrained reconstruction model for highly efficient use of the acquired data.

Theory and methods: The MoCoLoR reconstruction is formulated as an optimization problem that includes a low-rank constraint using estimated motion fields to reduce the rank, optimizing over both the motion fields and reconstructed images. The proposed reconstruction along with XD and motion state-weighted motion-compensation (MostMoCo) methods were applied to 18 lung MRI scans of pediatric and young adult patients. The data sets were acquired under free-breathing and without sedation with 3D radial UTE sequences in approximately 5 min. After reconstruction, they went through ventilation analyses. Performance across reconstruction regularization and motion-state parameters were also investigated.

Results: The in vivo experiments results showed that MoCoLoR made efficient use of the data, provided higher apparent SNR compared with state-of-the-art XD reconstruction and MostMoCo reconstructions, and yielded high-quality respiratory phase-resolved images for ventilation mapping. The method was effective across the range of patients scanned.

Conclusion: The motion-compensated low-rank regularized reconstruction approach makes efficient use of acquired data and can improve simultaneous structural and functional lung imaging with 3D-UTE MRI. It enables the scanning of pediatric patients under free-breathing and without sedation.

Keywords: motion compensation; pulmonary MRI; ultrashort echo time (UTE); ventilation imaging.

Figure 1

Motion Compensated Low-Rank Constrained (MoCoLoR) reconstruction workflow for respiratory phase-resolved lung MRI. (a) First, a respiratory signal is required, which can be derived from the center of the k-space. (b) Based on this signal, raw data is grouped by respiratory states. (c) Respiratory phase-resolved image volumes are iteratively reconstructed by MoCoLoR, including image registration between respiratory states. (d) Singular vector decomposition (SVD) is used to enforce low rank. (e) A sample visualization of the spatial bases from SVD shows that adding in motion-compensation (MoCo) from the estimated motion fields compresses the information into fewer components, decreasing the rank. This framework can also be adapted to reconstruct time-resolved images.

Figure 2

Representative structural image and ventilation maps. The dataset represents a 14 y/o patient with severe combined immunodeficiency (SCID) who presented with dyspnea on exertion in different respiratory states. The circled regions show improved structures at end-expiration and intermediate respiratory states, and the arrows point to the sharpened diaphragm.

Figure 3

Box Plots of aSNR and Sharpness Measurements of All Datasets. Apparent SNR (aSNR) of the lung parenchyma and aorta indicates an aSNR boost with the MoCoLoR approach. The Trachea aSNR measures the noise level within the trachea. Maximum Derivative (MD) of the diaphragm quantifies the sharpness. Mean and standard deviations are summarized in Table 1.

Figure 4

Investigation of the Regularization Parameter for MoCoLoR. The figure depicts the structural and functional images of a 25 y/o patient with Leukemia. $\lambda L=0.05$ provides high parenchymal apparent SNR (SNR_p) and does not appear to over- or under-estimate the ventilation.

Figure 5

Number of Respiratory States of MoCoLoR. One dataset from a 19 y/o patient with chronic cough and pulmonary nodules was reconstructed into 2–50 respiratory states. Structural images for 2, 6, 10, 20, 32, and 50 states were shown. The end-inspiration respiratory state was selected for illustration to demostrate the resolved motion blurring. With the increased number of states, the diaphragm is sharper while the aliasing artifact is more appearent.

Figure 6

Apparent SNR, Maximum Derivative, and Ventilation measurements of a different number of states. Results from 2–50 respiratory states are included. (Top) Structural metrics compared results from all three reconstruction approaches. Note that 50-state MostMoCo did not converge in the 48-hour grid job time limit and the streaking artifacts led to an unusually high maximum derivative. (Bottom) Ventilation measurements visualize ventilation results from MoCoLoR reconsturction. The normalized phase is the respiratory state (e.g. 0…11) divided by the total number of states (e.g. 12) 0. Since respiration is cyclic, the normalized states 0 and 1 are the same and both represent the end-expiration state.

Figure 7

MoCoLoR ventilation mapping applied to Longitudinal Imaging. Repeated scans of a pediatric patient with childhood interstitial lung disease (ChILD) at ages 4 and 4.5 years old are shown. High-density suture material is visible in the left lower lobe at both time points (red arrow) because of previous wedge resection. These ventilation maps are normalized by the average ventilation, which corresponds to the total percentage volume change of the lung. Histograms of the regional ventilation and specific ventilation at both times are also shown.

Figure 8

MoCoLoR reconstructed images at end-expiration state for all eighteen datasets. Images were enlarged to show details.