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

作者：Fei Tan, Xucheng Zhu, Marilynn Chan, Matthew A. Zapala, Shreyas Vasanawala, Frank Ong, Michael Lustig, Peder E. Z. Larson · 发表于：Magnetic Resonance in Medicine · 年份：2023 · DOI：10.1002/mrm.29703 · 被引用次数：17 · 研究领域：Atomic and Subatomic Physics Research、Advanced Radiotherapy Techniques、Medical Imaging Techniques and Applications

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 ventilatio...