Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity
Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity
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DOI：
10.1109/access.2021.3049181
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Yongbo Wang;Gaofeng Chen;Tao Xi;Z. Bian;D. Zeng;H. Zaidi;Ji He;Jianhua Ma
中科院分区：
文献类型：
--
作者：
Yongbo Wang;Gaofeng Chen;Tao Xi;Z. Bian;D. Zeng;H. Zaidi;Ji He;Jianhua Ma
稀疏视图扫描在实现超低剂量计算机断层扫描（CT）检查方面具有巨大潜力。然而，重建图像中的噪声和伪影是很大的障碍，必须加以处理以保持诊断的准确性。现有的稀疏视图CT重建算法通常是针对圆形成像几何形状设计的，而临床上普遍采用螺旋成像几何形状。在本文中，我们表明稀疏视图螺旋 CT（SHCT）图像不仅包含噪声和伪影，而且还包含严重的解剖失真。这些问题降低了现有稀疏视图 CT 重建算法的适用性。为了解决这个问题，我们分析了 SHCT 图像中的三维（3D）解剖结构稀疏性。基于分析，我们提出了用于 SHCT 重建的张量分解和各向异性全变分正则化模型（TDATV）。具体来说，张量分解适用于非局部立方体组，以利用解剖结构冗余；各向异性总变分作用于整个体积以利用结构分段平滑。最后，提出了乘法器交替方向法来求解 TDATV 模型。据我们所知，本文提出了第一个研究稀疏视图螺旋 CT 重建的工作。 TDATV 模型通过数字体模、物理体模和临床患者研究进行了验证。结果表明，SHCT 可以作为利用所提出的 TDATV 模型将 HCT 辐射剂量降低至超低水平的潜在解决方案。
Sparse-view scanning has great potential for realizing ultra-low-dose computed tomography (CT) examination. However, noise and artifacts in reconstructed images are big obstacles, which must be handled to maintain the diagnosis accuracy. Existing sparse-view CT reconstruction algorithms were usually designed for circular imaging geometry, whereas the helical imaging geometry is commonly adopted in the clinic. In this paper, we show that the sparse-view helical CT (SHCT) images contain not only noise and artifacts but also severe anatomical distortions. These troubles reduce the applicability of existing sparse-view CT reconstruction algorithms. To deal with this problem, we analyzed the three-dimensional (3D) anatomical structure sparsity in SHCT images. Based on the analyses, we proposed a tensor decomposition and anisotropic total variation regularization model (TDATV) for SHCT reconstruction. Specifically, the tensor decomposition works on nonlocal cube groups to exploit the anatomical structure redundancy; the anisotropic total variation works on the whole volume to exploit the structural piecewise-smooth. Finally, an alternating direction method of multipliers is developed to solve the TDATV model. To our knowledge, the paper presents the first work investigating the reconstruction of sparse-view helical CT. The TDATV model was validated through digital phantom, physical phantom, and clinical patient studies. The results reveal that SHCT could serve as a potential solution for reducing HCT radiation dose to ultra-low level by using the proposed TDATV model.