CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD
CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD
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DOI：
10.3934/ipi.2019022
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发表时间：
2019-06-01
影响因子：
1.3
通讯作者：
Lai, Rongjie
中科院分区：
文献类型：
--
作者：
Cong, Wenxiang;Wang, Ge;Lai, Rongjie
自然图像的补丁流形具有低维结构，并容纳丰富的结构信息。受最近低维流形模型（LDMM）工作的启发，我们将LDMM应用于正则化X射线计算机断层扫描（CT）图像重建。该方法恢复了图像的详细结构信息，显著提高了CT图像的空间分辨率和对比度分辨率。数值模拟数据和临床实验数据被用来评估所提出的方法。通过与结合全变分正则化的同步代数重建技术（SART）的比较研究，验证了该方法的优越性。结果表明，基于LDMM的方法可以实现更精确的图像重建，具有高保真度和对比度分辨率。
The patch manifold of a natural image has a low dimensional structure and accommodates rich structural information. Inspired by the recent work of the low-dimensional manifold model (LDMM), we apply the LDMM for regularizing X-ray computed tomography (CT) image reconstruction. This proposed method recovers detailed structural information of images, significantly enhancing spatial and contrast resolution of CT images. Both numerically simulated data and clinically experimental data are used to evaluate the proposed method. The comparative studies are also performed over the simultaneous algebraic reconstruction technique (SART) incorporated the total variation (TV) regularization to demonstrate the merits of the proposed method. Results indicate that the LDMM-based method enables a more accurate image reconstruction with high fidelity and contrast resolution.