使用双期低剂量 CT 图像测量心肌细胞外容积分数的框架。

A framework to measure myocardial extracellular volume fraction using dual-phase low dose CT images.

机构信息

Clinical Image Processing Service, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, Maryland 20892.

出版信息

Med Phys. 2013 Oct;40(10):103501. doi: 10.1118/1.4819936.

DOI:10.1118/1.4819936

PMID:24089934

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3785529/

Abstract

PURPOSE

Myocardial extracellular volume fraction (ECVF) is a surrogate imaging biomarker of diffuse myocardial fibrosis, a hallmark of pathologic ventricular remodeling. Low dose cardiac CT is emerging as a promising modality to detect diffuse interstitial myocardial fibrosis due to its fast acquisition and low radiation; however, the insufficient contrast in the low dose CT images poses great challenge to measure ECVF from the image.

METHODS

To deal with this difficulty, the authors present a complete ECVF measurement framework including a point-guided myocardial modeling, a deformable model-based myocardium segmentation, nonrigid registration of pre- and post-CT, and ECVF calculation.

RESULTS

The proposed method was evaluated on 20 patients by two observers. Compared to the manually delineated reference segmentations, the accuracy of our segmentation in terms of true positive volume fraction (TPVF), false positive volume fraction (FPVF), and average surface distance (ASD), were 92.18% ± 3.52%, 0.31% ± 0.10%, 0.69 ± 0.14 mm, respectively. The interobserver variability measured by concordance correlation coefficient regarding TPVF, FPVF, and ASD were 0.95, 0.90, 0.94, respectively, demonstrating excellent agreement. Bland-Altman method showed 95% limits of agreement between ECVF at CT and ECVF at MR.

CONCLUSIONS

The proposed framework demonstrates its efficiency, accuracy, and noninvasiveness in ECVF measurement and dramatically advances the ECVF at cardiac CT toward its clinical use.

摘要

目的

心肌细胞外容积分数（ECVF）是弥漫性心肌纤维化的替代成像生物标志物，也是病理性心室重构的标志。低剂量心脏 CT 由于其快速采集和低辐射而成为检测弥漫性间质心肌纤维化的一种很有前途的方法；然而，低剂量 CT 图像对比度不足，这对从图像中测量 ECVF 提出了很大的挑战。

方法

为了解决这个难题，作者提出了一个完整的 ECVF 测量框架，包括点引导的心肌建模、基于可变形模型的心肌分割、CT 前后的非刚性配准以及 ECVF 计算。

结果

该方法在 20 名患者中由两名观察者进行了评估。与手动勾画的参考分割相比，我们的分割在真阳性容积分数（TPVF）、假阳性容积分数（FPVF）和平均表面距离（ASD）方面的准确性分别为 92.18%±3.52%、0.31%±0.10%、0.69±0.14mm。观察者间一致性相关系数（CC）在 TPVF、FPVF 和 ASD 方面的可变性分别为 0.95、0.90、0.94，显示出极好的一致性。Bland-Altman 方法显示 CT 时 ECVF 和 MR 时 ECVF 之间的 95%一致性界限。