Department of Radiology, University of Chicago, 5841 S. Maryland Avenue, Chicago, Illinois 60637, USA.
Med Phys. 2011 Jul;38 Suppl 1(Suppl 1):S117. doi: 10.1118/1.3560887.
The authors developed an iterative image-reconstruction algorithm for application to low-intensity computed tomography projection data, which is based on constrained, total-variation (TV) minimization. The algorithm design focuses on recovering structure on length scales comparable to a detector bin width.
Recovering the resolution on the scale of a detector bin requires that pixel size be much smaller than the bin width. The resulting image array contains many more pixels than data, and this undersampling is overcome with a combination of Fourier upsampling of each projection and the use of constrained, TV minimization, as suggested by compressive sensing. The presented pseudocode for solving constrained, TV minimization is designed to yield an accurate solution to this optimization problem within 100 iterations.
The proposed image-reconstruction algorithm is applied to a low-intensity scan of a rabbit with a thin wire to test the resolution. The proposed algorithm is compared to filtered backprojection (FBP).
The algorithm may have some advantage over FBP in that the resulting noise level is lowered at equivalent contrast levels of the wire.
作者开发了一种迭代图像重建算法，应用于低强度计算机断层扫描投影数据，该算法基于约束的全变差（TV）最小化。该算法设计的重点是恢复与探测器-bin 宽度相当的长度尺度上的结构。
恢复与探测器-bin 尺度相当的分辨率要求像素尺寸比 bin 宽度小得多。由于每个投影的傅里叶上采样和使用受约束的 TV 最小化，该图像阵列包含比数据多得多的像素，从而克服了这种欠采样。所提出的求解受约束 TV 最小化的伪代码旨在在 100 次迭代内为该优化问题提供准确的解决方案。
将提出的图像重建算法应用于对带有细金属丝的兔子进行低强度扫描以测试分辨率。将提出的算法与滤波反投影（FBP）进行了比较。
与 FBP 相比，该算法在金属丝的等效对比度水平下降低了噪声水平，可能具有一些优势。
