基于超极化气体和 H 磁共振成像的肺部通气容积百分比半自动计算的空间模糊 C 均值阈值法。
Spatial fuzzy c-means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and H MRI.
机构信息
Academic Unit of Radiology, University of Sheffield, Sheffield, South Yorkshire, UK.
Insigneo Institute for in silico Medicine, Sheffield, South Yorkshire, UK.
出版信息
J Magn Reson Imaging. 2018 Mar;47(3):640-646. doi: 10.1002/jmri.25804. Epub 2017 Jul 6.
PURPOSE
To develop an image-processing pipeline for semiautomated (SA) and reproducible analysis of hyperpolarized gas lung ventilation and proton anatomical magnetic resonance imaging (MRI) scan pairs. To compare results from the software for total lung volume (TLV), ventilated volume (VV), and percentage lung ventilated volume (%VV) calculation to the current manual "basic" method and a K-means segmentation method.
MATERIALS AND METHODS
Six patients were imaged with hyperpolarized He and same-breath lung H MRI at 1.5T and six other patients were scanned with hyperpolarized Xe and separate-breath H MRI. One expert observer and two users with experience in lung image segmentation carried out the image analysis. Spearman (R), Intraclass (ICC) correlations, Bland-Altman limits of agreement (LOA), and Dice Similarity Coefficients (DSC) between output lung volumes were calculated.
RESULTS
When comparing values of %VV, agreement between observers improved using the SA method (mean; R = 0.984, ICC = 0.980, LOA = 7.5%) when compared to the basic method (mean; R = 0.863, ICC = 0.873, LOA = 14.2%) nonsignificantly (p = 0.25, p = 0.25, and p = 0.50 respectively). DSC of VV and TLV masks significantly improved (P < 0.01) using the SA method (mean; DSC = 0.973, DSC = 0.980) when compared to the basic method (mean; DSC = 0.947, DSC = 0.957). K-means systematically overestimated %VV when compared to both basic (mean overestimation = 5.0%) and SA methods (mean overestimation = 9.7%), and had poor agreement with the other methods (mean ICC; K-means vs. basic = 0.685, K-means vs. SA = 0.740).
CONCLUSION
A semiautomated image processing software was developed that improves interobserver agreement and correlation of lung ventilation volume percentage when compared to the currently used basic method and provides more consistent segmentations than the K-means method.
LEVEL OF EVIDENCE
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:640-646.
目的
开发一种用于半自动化（SA）和可重复分析超极化气体肺通气和质子解剖磁共振成像（MRI）扫描对的图像处理管道。比较软件对总肺容量（TLV）、通气量（VV）和通气百分比（%VV）计算的结果与当前手动“基本”方法和 K-均值分割方法的结果。
材料和方法
在 1.5T 上对 6 名患者进行超极化 3He 和同呼吸肺 1H MRI 成像，对另外 6 名患者进行超极化 129Xe 和单独呼吸 1H MRI 成像。一名专家观察者和两名具有肺图像分割经验的用户进行图像分析。计算输出肺容积之间的 Spearman（R）、Intraclass（ICC）相关性、Bland-Altman 协议限（LOA）和 Dice 相似性系数（DSC）。
结果
当比较 %VV 值时，与基本方法相比（平均值；R=0.863，ICC=0.873，LOA=14.2%），使用 SA 方法时观察者之间的一致性得到改善（平均值；R=0.984，ICC=0.980，LOA=7.5%），但差异无统计学意义（p=0.25，p=0.25，p=0.50 分别）。使用 SA 方法时，VV 和 TLV 掩模的 DSC 显著提高（P<0.01）（平均值；DSC=0.973，DSC=0.980）与基本方法相比（平均值；DSC=0.947，DSC=0.957）。与基本方法（平均高估=5.0%）和 SA 方法（平均高估=9.7%）相比，K-均值系统地高估了%VV，并且与其他方法的一致性较差（平均 ICC；K-均值与基本方法相比=0.685，K-均值与 SA 方法相比=0.740）。
结论
开发了一种半自动化图像处理软件，与当前使用的基本方法相比，该软件可提高肺通气量百分比的观察者间一致性和相关性，并提供比 K-均值方法更一致的分割。
证据水平
3 技术功效：第 2 阶段 J. Magn. Reson. Imaging 2018;47:640-646.
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