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An iterative method for the reconstruction of the coronary arteries from rotational X-ray angiography

机译:X射线血管造影重建冠状动脉的一种迭代方法

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摘要

Three-dimensional (3D) reconstruction of the coronary arteries offers great advantages in the diagnosis and treatment of cardiovascular diseases, compared to two-dimensional X-ray angiograms. Besides improved roadmapping, quantitative analysis of vessel lesions is possible. To perform 3D reconstruction, rotational projection data of the selectively contrast agent enhanced coronary arteries are acquired with simultaneous ECG recording. For the reconstruction of one cardiac phase, the corresponding projections are selected from the rotational sequence by nearest-neighbor ECG gating. This typically provides only 5-10 projections per cardiac phase. The severe angular undersampling leads to an ill-posed reconstruction problem. In this contribution, an iterative reconstruction method is presented which employs regularizations especially suited for the given reconstruction problem. The coronary arteries cover only a small fraction of the reconstruction volume. Therefore, we formulate the reconstruction problem as a minimization of the Li-norm of the reconstructed image, which results in a spatially sparse object. Two additional regularization terms are introduced: a 3D vesselness prior, which is reconstructed from vesselness-filtered projection data, and a Gibbs smoothing prior. The regularizations favor the reconstruction of the desired object, while taking care not to over-constrain the reconstruction by too detailed a-priori assumptions. Simulated projection data of a coronary artery software phantom are used to evaluate the performance of the method. Human data of clinical cases are presented to show the method's potential for clinical application.
机译:与二维X射线血管造影相比,冠状动脉的三维(3D)重建在心血管疾病的诊断和治疗方面具有巨大优势。除了改进路线图,还可以对血管病变进行定量分析。为了执行3D重建,通过同时进行ECG记录来获取选择性造影剂增强的冠状动脉的旋转投影数据。为了重建一个心脏相位,通过最近邻ECG门控从旋转序列中选择相应的投影。通常每个心动相仅提供5-10个投影。严重的角度欠采样会导致不适定的重建问题。在此贡献中,提出了一种迭代重建方法,该方法采用特别适合给定重建问题的正则化。冠状动脉仅覆盖重建体积的一小部分。因此,我们将重建问题公式化为重建图像的Li范数的最小值,从而导致空间稀疏的对象。引入了两个附加的正则化术语:3D先验先验(从先验先验过滤过的投影数据重建而来)和Gibbs平滑先验。正则化有利于期望对象的重建,同时注意不要通过过于详细的先验假设过度约束重建。冠状动脉软件模型的模拟投影数据用于评估该方法的性能。提供了临床病例的人类数据,以显示该方法在临床上的潜力。

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