Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography.
Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography.
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DOI：
10.1118/1.3397463
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发表时间：
2010-05
期刊：
影响因子：
3.8
通讯作者：
Sungwon Yoon;A. Pineda;R. Fahrig
中科院分区：
文献类型：
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作者：
Sungwon Yoon;A. Pineda;R. Fahrig
目的提出一种同时分割和重建重建域的迭代层析重建算法，并应用于稀疏投影图像的层析重建。方法该算法采用两阶段水平集方法分割和迭代层析重建相结合的方法实现图像的分割和重建。同时分割和重建是通过水平集函数的演变和每个区域的强度值更新之间的交替。为了处理有限数量的投影，先验信息的重建是通过惩罚似然函数。具体地，假设每个区域内的平滑函数（分段平滑函数）和每个区域的有界函数强度值。这种先验信息被配制成一个二次目标函数与线性约束。水平集函数的演化是通过对水平集函数在负梯度方向上进行人工时间演化来实现的;亮度值的更新是通过梯度投影共轭梯度算法来实现的。结果提出的同时分割和重建的结果进行了比较，“传统的”迭代重建（没有分割），迭代重建后分割，和过滤反投影。当该算法应用于模拟投影的数值模型和真实的扇束投影的Catphan模型，这两个模型都不满足先验假设时，观察到归一化均方根误差的6%-13%的改善。结论：提出的同时分割和重建可以提高重建图像质量。该算法正确地将重建空间分割成区域，保留不同区域之间的尖锐边缘，并平滑每个区域内的噪声。所提出的算法框架具有适应不同的先验约束的灵活性，同时保持同步分割和重建所取得的好处。
PURPOSE An iterative tomographic reconstruction algorithm that simultaneously segments and reconstructs the reconstruction domain is proposed and applied to tomographic reconstructions from a sparse number of projection images. METHODS The proposed algorithm uses a two-phase level set method segmentation in conjunction with an iterative tomographic reconstruction to achieve simultaneous segmentation and reconstruction. The simultaneous segmentation and reconstruction is achieved by alternating between level set function evolutions and per-region intensity value updates. To deal with the limited number of projections, a priori information about the reconstruction is enforced via penalized likelihood function. Specifically, smooth function within each region (piecewise smooth function) and bounded function intensity values for each region are assumed. Such a priori information is formulated into a quadratic objective function with linear bound constraints. The level set function evolutions are achieved by artificially time evolving the level set function in the negative gradient direction; the intensity value updates are achieved by using the gradient projection conjugate gradient algorithm. RESULTS The proposed simultaneous segmentation and reconstruction results were compared to "conventional" iterative reconstruction (with no segmentation), iterative reconstruction followed by segmentation, and filtered backprojection. Improvements of 6%-13% in the normalized root mean square error were observed when the proposed algorithm was applied to simulated projections of a numerical phantom and to real fan-beam projections of the Catphan phantom, both of which did not satisfy the a priori assumptions. CONCLUSIONS The proposed simultaneous segmentation and reconstruction resulted in improved reconstruction image quality. The algorithm correctly segments the reconstruction space into regions, preserves sharp edges between different regions, and smoothes the noise within each region. The proposed algorithm framework has the flexibility to be adapted to different a priori constraints while maintaining the benefits achieved by the simultaneous segmentation and reconstruction.