Prior image constrained compressed sensing: Implementation and performance evaluation
Prior image constrained compressed sensing: Implementation and performance evaluation
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DOI：
10.1118/1.3666946
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发表时间：
2012-01-01
期刊：
影响因子：
3.8
通讯作者：
Chen, Guang-Hong
中科院分区：
文献类型：
--
作者：
Lauzier, Pascal Theriault;Tang, Jie;Chen, Guang-Hong
目的：先验图像约束压缩感知（PICCS）是一种图像重建框架，它将通常可用的先验图像纳入压缩感知目标函数。使用优化过程重建图像。在本文中，几个替代的无约束最小化方法被用来实现PICCS。其目的是研究和比较每个实现的性能，以及评估的PICCS目标函数的性能与图像quality.Methods：六种不同的最小化方法进行了研究，相对于收敛速度和重建精度。这些最小化方法包括最速下降（SD）方法和共轭梯度（CG）方法。这些算法需要执行线搜索。因此，对于每个最小化算法，两个线搜索算法进行评估：回溯（BT）线搜索和快速牛顿-拉夫森（NR）线搜索。用相对均方根误差来评价重建精度。该算法提供了最佳的收敛速度被用来研究PICCS相对于先前的图像参数α和数据一致性参数λ的性能。PICCS的重建精度，低对比度的空间分辨率和噪声特性进行了研究。一个数值的幻影进行了模拟和动物模型进行扫描，使用多排探测器计算机断层扫描（CT）扫描仪产生的投影datasets在本study.Results：λ在很宽的范围内，CG方法与弗莱彻-里夫斯公式和NR线搜索提供了最快的收敛速度为重建精度的一个平等的水平。使用这种最小化方法，PICCS的重建精度进行了研究相对于α和λ的变化。当视角的数量在107、80、64、40、20和16之间变化时，相对均方根误差达到最小值，α近似为0.5。对于最佳值附近的α值，重建图像的空间分辨率保持相对恒定，噪声纹理是非常相似的先验图像，这是使用滤波反投影（FBP）algorithm.Conclusions：关于最小化方法的性能，非线性CG方法与NR线搜索产生最佳的收敛速度。关于PICCS图像重建的性能，可以得出三个主要结论。(1)当先验图像参数的加权参数被选择为接近α = 0.5时，PICCS的性能是最佳的。(2)对于使用PICCS从欠采样数据集重建的图像中的静态对象测量的空间分辨率相对于接近其最佳值的α的完全采样重建没有退化。(3)PICCS重建的噪声纹理与使用传统的FBP方法重建的先验图像的噪声纹理相似。(C)2012年美国医学物理学家协会。[DOI：10.1118/1.3666946]
Purpose: Prior image constrained compressed sensing (PICCS) is an image reconstruction framework which incorporates an often available prior image into the compressed sensing objective function. The images are reconstructed using an optimization procedure. In this paper, several alternative unconstrained minimization methods are used to implement PICCS. The purpose is to study and compare the performance of each implementation, as well as to evaluate the performance of the PICCS objective function with respect to image quality.Methods: Six different minimization methods are investigated with respect to convergence speed and reconstruction accuracy. These minimization methods include the steepest descent (SD) method and the conjugate gradient (CG) method. These algorithms require a line search to be performed. Thus, for each minimization algorithm, two line searching algorithms are evaluated: a backtracking (BT) line search and a fast Newton-Raphson (NR) line search. The relative root mean square error is used to evaluate the reconstruction accuracy. The algorithm that offers the best convergence speed is used to study the performance of PICCS with respect to the prior image parameter alpha and the data consistency parameter lambda. PICCS is studied in terms of reconstruction accuracy, low-contrast spatial resolution, and noise characteristics. A numerical phantom was simulated and an animal model was scanned using a multirow detector computed tomography (CT) scanner to yield the projection datasets used in this study.Results: For lambda within a broad range, the CG method with Fletcher-Reeves formula and NR line search offers the fastest convergence for an equal level of reconstruction accuracy. Using this minimization method, the reconstruction accuracy of PICCS was studied with respect to variations in alpha and lambda. When the number of view angles is varied between 107, 80, 64, 40, 20, and 16, the relative root mean square error reaches a minimum value for alpha approximate to 0.5. For values of alpha near the optimal value, the spatial resolution of the reconstructed image remains relatively constant and the noise texture is very similar to that of the prior image, which was reconstructed using the filtered backprojection (FBP) algorithm.Conclusions: Regarding the performance of the minimization methods, the nonlinear CG method with NR line search yields the best convergence speed. Regarding the performance of the PICCS image reconstruction, three main conclusions can be reached. (1) The performance of PICCS is optimal when the weighting parameter of the prior image parameter is selected to be near alpha = 0.5. (2) The spatial resolution measured for static objects in images reconstructed using PICCS from undersampled datasets is not degraded with respect to the fully-sampled reconstruction for alpha near its optimal value. (3) The noise texture of PICCS reconstructions is similar to that of the prior image, which was reconstructed using the conventional FBP method. (C) 2012 American Association of Physicists in Medicine. [DOI: 10.1118/1.3666946]