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使用 3D 黑血 MRI 和自动分割技术快速评估股动脉斑块负担。

Fast plaque burden assessment of the femoral artery using 3D black-blood MRI and automated segmentation.

机构信息

Department of Electronic Engineering, City University of Hong Kong, Hong Kong.

出版信息

Med Phys. 2011 Oct;38(10):5370-84. doi: 10.1118/1.3633899.

Abstract

PURPOSE

Vessel wall imaging techniques have been introduced to assess the burden of peripheral arterial disease (PAD) in terms of vessel wall thickness, area or volume. Recent advances in a 3D black-blood MRI sequence known as the 3D motion-sensitized driven equilibrium (MSDE) prepared rapid gradient echo sequence (3D MERGE) have allowed the acquisition of vessel wall images with up to 50 cm coverage, facilitating noninvasive and detailed assessment of PAD. This work introduces an algorithm that combines 2D slice-based segmentation and 3D user editing to allow for efficient plaque burden analysis of the femoral artery images acquired using 3D MERGE.

METHODS

The 2D slice-based segmentation approach is based on propagating segmentation results of contiguous 2D slices. The 3D image volume was then reformatted using the curved planar reformation (CPR) technique. User editing of the segmented contours was performed on the CPR views taken at different angles. The method was evaluated on six femoral artery images. Vessel wall thickness and area obtained before and after editing on the CPR views were assessed by comparison with manual segmentation. Difference between semiautomatically and manually segmented contours were compared with the difference of the corresponding measurements between two repeated manual segmentations.

RESULTS

The root-mean-square (RMS) errors of the mean wall thickness (t(mean)) and the wall area (WA) of the edited contours were 0.35 mm and 7.1 mm(2), respectively, which are close to the RMS difference between two repeated manual segmentations (RMSE: 0.33 mm in t(mean), 6.6 mm(2) in WA). The time required for the entire semiautomated segmentation process was only 1%-2% of the time required for manual segmentation.

CONCLUSIONS

The difference between the boundaries generated by the proposed algorithm and the manually segmented boundary is close to the difference between repeated manual segmentations. The proposed method provides accurate plaque burden measurements, while considerably reducing the analysis time compared to manual review.

摘要

目的

血管壁成像技术已被引入,以评估血管壁厚度、面积或体积来评估外周动脉疾病(PAD)的负担。一种称为三维运动敏感驱动平衡(3D MSDE)准备快速梯度回波序列(3D MERGE)的 3D 黑血 MRI 序列的最新进展允许采集多达 50cm 覆盖范围的血管壁图像,从而能够进行非侵入性和详细的 PAD 评估。本工作介绍了一种算法,该算法结合了基于 2D 切片的分割和 3D 用户编辑,以允许对使用 3D MERGE 采集的股动脉图像进行有效的斑块负担分析。

方法

基于连续 2D 切片的分割方法基于传播分割结果。然后使用曲面重建(CPR)技术对 3D 图像体积进行重新格式化。在不同角度拍摄的 CPR 视图上对分割轮廓进行用户编辑。该方法在六张股动脉图像上进行了评估。通过与手动分割进行比较,评估了在 CPR 视图上编辑前后获得的血管壁厚度和面积。半自动和手动分割轮廓之间的差异与两次重复手动分割之间相应测量值的差异进行了比较。

结果

编辑轮廓的平均壁厚度(t(mean))和壁面积(WA)的均方根误差(RMS)分别为 0.35mm 和 7.1mm²,接近两次重复手动分割之间的 RMS 差异(RMSE:t(mean)为 0.33mm,WA 为 6.6mm²)。整个半自动分割过程所需的时间仅为手动分割所需时间的 1%-2%。

结论

该算法生成的边界与手动分割边界之间的差异接近于重复手动分割之间的差异。与手动审阅相比,该方法提供了准确的斑块负担测量值,同时大大减少了分析时间。

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本文引用的文献

1
Carotid plaque assessment using fast 3D isotropic resolution black-blood MRI.
Magn Reson Med. 2011 Mar;65(3):627-37. doi: 10.1002/mrm.22642. Epub 2010 Oct 12.
3
Comparison of 3D-diffusion-prepared segmented steady-state free precession and 2D fast spin echo imaging of femoral artery atherosclerosis.
Int J Cardiovasc Imaging. 2010 Mar;26(3):309-21. doi: 10.1007/s10554-009-9544-0. Epub 2009 Nov 28.
4
Three-dimensional T2-weighted MRI of the human femoral arterial vessel wall at 3.0 Tesla.
Invest Radiol. 2009 Sep;44(9):619-26. doi: 10.1097/RLI.0b013e3181b4c218.
7
Magnetic resonance imaging of carotid atherosclerosis: plaque analysis.
Top Magn Reson Imaging. 2007 Oct;18(5):371-8. doi: 10.1097/rmr.0b013e3181598d9d.
10
Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour.
Phys Med Biol. 2004 Nov 7;49(21):4943-60. doi: 10.1088/0031-9155/49/21/007.

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