Deng Zixin, Pang Jianing, Lao Yi, Bi Xiaoming, Wang Guan, Chen Yuhua, Fenchel Matthias, Tuli Richard, Li Debiao, Yang Wensha, Fan Zhaoyang
1 Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars Sinai Medical Center , Los Angeles, CA , USA.
2 MR R&D, Siemens Healthineers , Chicago, IL , USA.
Br J Radiol. 2019 Mar;92(1095):20180424. doi: 10.1259/bjr.20180424. Epub 2019 Jan 3.
: Nine patients (seven pancreas, one liver, and one lung) were recruited. 4D-MRI was performed using two prototype k-space sorted techniques, stack-of-stars (SOS) and koosh-ball (KB) acquisitions. Post-processing using MoCoAve was implemented for both methods. Image quality score, apparent SNR (aSNR), sharpness, motion trajectory and standard deviation (σ_GTV) of the gross tumor volumes were compared between original and MoCoAve image sets.
: All subjects successfully underwent 4D-MRI scans and MoCoAve was performed on all data sets. Significantly higher image quality scores (2.64 ± 0.39 vs 1.18 ± 0.34, p = 0.001) and aSNR (37.6 ± 15.3 vs 18.1 ± 5.7, p = 0.001) was observed in the MoCoAve images when compared to the original images. High correlation in tumor motion trajectories in the superoinferior direction (SI: 0.91 ± 0.08) and weaker in the anteroposterior (AP: 0.51 ± 0.44) and mediolateral (ML: 0.37 ± 0.23) directions, similar image sharpness (0.367 ± 0.068 vs 0.369 ± 0.072, p = 0.805), and minimal average absolute difference (0.47 ± 0.34 mm) of the motion trajectory profiles was found between the two image sets. The σ_GTV in pancreas patients was significantly (p = 0.039) lower in MoCoAve images (1.48 ± 1.35 cm) than in the original images (2.17 ± 1.31 cm).
: MoCoAve using interphase motion correction and averaging has shown promise as a post-processing method for improving k-space sorted (SOS and KB) 4D-MRI image quality in thoracic and abdominal cancer patients.
: The proposed method is an image based post-processing method that could be applied to many k-space sorted 4D-MRI methods for improved image quality and signal-to-noise ratio while preserving image sharpness and respiratory motion fidelity. It is a useful technique for the radiotherapy planning community who are interested in using 4D-MRI but aren't satisfied with their current MR image quality.
招募了9名患者(7名胰腺患者、1名肝脏患者和1名肺部患者)。使用两种原型k空间排序技术,即星状堆叠(SOS)和库什球(KB)采集法进行4D-MRI检查。两种方法均采用MoCoAve进行后处理。比较原始图像集和MoCoAve图像集之间的图像质量评分、表观信噪比(aSNR)、清晰度、运动轨迹以及大体肿瘤体积的标准差(σ_GTV)。
所有受试者均成功完成4D-MRI扫描,并且对所有数据集都进行了MoCoAve处理。与原始图像相比,MoCoAve图像的图像质量评分(2.64±0.39对1.18±0.34,p = 0.001)和aSNR(37.6±15.3对18.1±5.7,p = 0.001)显著更高。在上下方向(SI:0.91±0.08)肿瘤运动轨迹的相关性较高,而在前后方向(AP:0.51±0.44)和左右方向(ML:0.37±0.23)较弱,两个图像集之间的图像清晰度相似(0.367±0.068对0.369±0.072,p = 0.805),并且运动轨迹轮廓的平均绝对差异最小(0.47±0.34毫米)。胰腺患者的MoCoAve图像中的σ_GTV(1.48±1.35厘米)显著低于原始图像(2.17±1.31厘米)(p = 0.039)。
使用相间运动校正和平均的MoCoAve已显示出有望作为一种后处理方法,用于改善胸部和腹部癌症患者的k空间排序(SOS和KB)4D-MRI图像质量。
所提出的方法是一种基于图像的后处理方法,可应用于许多k空间排序的4D-MRI方法,以提高图像质量和信噪比,同时保持图像清晰度和呼吸运动保真度。对于有兴趣使用4D-MRI但对当前MR图像质量不满意的放射治疗计划领域而言,这是一项有用的技术。