论文标题
迭代运动补偿金重建超短te(imoco ute),用于高分辨率的自由呼吸肺MRI
Iterative Motion Compensation reconstruction ultra-short TE(iMoCo UTE) for high resolution free breathing pulmonary MRI
论文作者
论文摘要
目的,为了开发高扫描效率,通过将运动补偿重建与UTE采集(称为Imoco UTE)相结合,以校正运动的肺动脉MRI进行校正成像策略。方法,一种优化的金角排序径向UTE序列用于连续获取数据5分钟。所有读数都基于自动信号将所有读数分组为不同的呼吸运动状态,然后通过XD金角径向稀疏平行重建(XD Grasp)重建运动解决数据。从运动解析图像中选择了一个状态作为参考,并通过非刚性登记得出了从其他状态到参考的运动场。最后,通过使用迭代运动补偿重建和总体广义变化稀疏约束来重建所有运动的数据和运动场。结果,在志愿者和非囊性小儿患者(4-6 y/o)研究中评估了imoco UTE策略。与使用其他运动校正策略相比,用imoco Ute重建的图像提供了更清晰的解剖肺结构,并且具有更高的明显SNR和CNR,例如软门,运动解决的重建和非刚性运动补偿(MOCO)。在一项婴儿研究中,imoco ute还显示出令人鼓舞的结果。结论,提出的iMoco Ute结合了自动化,运动建模和压缩感测重建,以提高扫描效率,SNR并减少肺MRI中的呼吸运动。该提出的策略显示了自由呼吸肺MRI扫描的改善,尤其是在非常具有挑战性的应用情况下,例如儿科MRI研究。
Purpose, To develop a high scanning efficiency, motion corrected imaging strategy for free-breathing pulmonary MRI by combining a motion compensation reconstruction with a UTE acquisition, called iMoCo UTE. Methods, An optimized golden angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, then motion resolved data was reconstructed by XD Golden angle RAdial Sparse Parallel reconstruction (XD GRASP). One state from the motion resolved images was selected as a reference, and motion fields from the other states to the reference were derived via non-rigid registration. Finally, all motion resolved data and motion fields were reconstructed by using an iterative motion compensation reconstruction with a total generalized variation sparse constraint. Results, The iMoCo UTE strategy was evaluated in volunteers and non-sedated pediatric patient(4-6 y/o) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures, and higher apparent SNR and CNR, compared to using other motion correction strategies, such as soft-gating, motion resolved reconstruction, and non-rigid motion compensation(MoCo). iMoCo UTE also showed promising results in an infant study. Conclusions, The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency, SNR, and reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free breathing lung MRI scans, especially in very challenging application situations, such as pediatric MRI studies.