Shi Caiyun, Liang Dong, Wang Haifeng, Zhu Yanjie
School of Biomedical Engineering, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong, China; Medical AI Research Centre, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong, China.
Magn Reson Imaging. 2024 Apr;107:80-87. doi: 10.1016/j.mri.2024.01.009. Epub 2024 Jan 17.
To improve the scan efficiency of thoracic aorta vessel wall imaging using a self-gating (SG)-based motion correction scheme.
A slab-selective variable-flip-angle 3D turbo spin-echo (SPACE) sequence was modified to acquire SG signals and imaging data. Cartesian sampling with a tiny golden-step spiral profile ordering was used to obtain the imaging data during the systolic period, and then the image data were subsequently corrected based on the SG signals and binned to different respiratory cycles. Finally, respiratory artifacts were estimated from image-based registration of 3D undersampled respiratory bins that were reconstructed with L1 iterative self-consistent parallel imaging reconstruction (SPIRiT). This method was evaluated in 11 healthy volunteers and compared against conventional diaphragmatic navigator-gated acquisition to assess the feasibility of the proposed framework.
Results showed that the proposed method achieved image quality comparable to that of conventional diaphragmatic navigator-gated acquisition with an average scan time of 4 min. The sharpness of the vessel wall and the definition of the liver boundary were in good agreement with the navigator-gated acquisition, which took approximately above 8.5 min depend on the respiratory rate. Further valuation of this technique in patients will be conducted to determine its clinical use.
使用基于自门控（SG）的运动校正方案提高胸主动脉血管壁成像的扫描效率。
对板层选择性可变翻转角三维快速自旋回波（SPACE）序列进行修改，以获取SG信号和成像数据。采用具有微小黄金步长螺旋轮廓排序的笛卡尔采样在收缩期获取成像数据，然后基于SG信号对图像数据进行校正，并分箱到不同的呼吸周期。最后，通过对用L1迭代自洽并行成像重建（SPIRiT）重建的三维欠采样呼吸分箱进行基于图像的配准来估计呼吸伪影。在11名健康志愿者中对该方法进行了评估，并与传统的膈肌导航门控采集进行比较，以评估所提出框架的可行性。
结果表明，所提出的方法实现了与传统膈肌导航门控采集相当的图像质量，平均扫描时间为4分钟。血管壁的清晰度和肝脏边界的清晰度与导航门控采集结果高度一致，后者根据呼吸频率大约需要8.5分钟以上。将进一步在患者中对该技术进行评估，以确定其临床用途。
