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一种基于相间运动校正和平均以提高四维磁共振成像图像质量的后处理方法:一项临床可行性研究。

A post-processing method based on interphase motion correction and averaging to improve image quality of 4D magnetic resonance imaging: a clinical feasibility study.

作者信息

Deng Zixin, Pang Jianing, Lao Yi, Bi Xiaoming, Wang Guan, Chen Yuhua, Fenchel Matthias, Tuli Richard, Li Debiao, Yang Wensha, Fan Zhaoyang

机构信息

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.

出版信息

Br J Radiol. 2019 Mar;92(1095):20180424. doi: 10.1259/bjr.20180424. Epub 2019 Jan 3.

Abstract

METHODS

: 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.

RESULTS

: 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).

CONCLUSION

: 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.

ADVANCES IN KNOWLEDGE

: 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.

摘要

方法

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)。

结果

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)。

结论

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。

知识进展

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。

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本文引用的文献

1
Improved vessel-tissue contrast and image quality in 3D radial sampling-based 4D-MRI.
J Appl Clin Med Phys. 2017 Nov;18(6):250-257. doi: 10.1002/acm2.12194. Epub 2017 Oct 4.
2
Respiratory motion-resolved, self-gated 4D-MRI using rotating cartesian k-space (ROCK).
Med Phys. 2017 Apr;44(4):1359-1368. doi: 10.1002/mp.12139. Epub 2017 Mar 11.
3
Self-navigated 4D cartesian imaging of periodic motion in the body trunk using partial k-space compressed sensing.
Magn Reson Med. 2017 Aug;78(2):632-644. doi: 10.1002/mrm.26406. Epub 2016 Sep 25.
5
4D respiratory motion-compensated image reconstruction of free-breathing radial MR data with very high undersampling.
Magn Reson Med. 2017 Mar;77(3):1170-1183. doi: 10.1002/mrm.26206. Epub 2016 Mar 16.
7
MRI noise estimation and denoising using non-local PCA.
Med Image Anal. 2015 May;22(1):35-47. doi: 10.1016/j.media.2015.01.004. Epub 2015 Feb 7.
8
Denoised and texture enhanced MVCT to improve soft tissue conspicuity.
Med Phys. 2014 Oct;41(10):101916. doi: 10.1118/1.4894714.
9
Self-gated radial MRI for respiratory motion compensation on hybrid PET/MR systems.
Med Image Comput Comput Assist Interv. 2013;16(Pt 3):17-24. doi: 10.1007/978-3-642-40760-4_3.

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