Korea Electrotechnology Research Institute, Seoul, Korea.
Biomed Eng Online. 2012 Aug 2;11:44. doi: 10.1186/1475-925X-11-44.
In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction.
We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts.
The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method.
The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.
在稀疏视角 CT 成像中,骨结构周围可能会出现强条纹伪影,这往往会降低图像的可读性。基于压缩感知(CS)或全变差(TV)最小化的图像重建方法已经在很大程度上减少了条纹伪影,但稀疏视角 CT 成像仍然存在残留的条纹伪影。我们在基于 CS 的图像重建中引入了一种新的减少骨诱导条纹伪影的方法。
我们首先从滤波反投影(FBP)方法重建的图像中识别高强度骨区域,并仅计算来自骨区域的正弦图。然后,在进行基于 CS 的图像重建之前,我们从测量的正弦图中减去表示骨区域的计算正弦图。从减去的正弦图重建的图像将代表软组织,其条纹伪影很少。为了恢复骨区域中的原始图像强度,我们将从 FBP 图像中识别出的骨区域图像添加到软组织图像中,形成组合图像。然后,我们使用组合图像作为迭代的初始条件,再次对测量的正弦图进行基于 CS 的图像重建。为了验证所提出方法的实验验证,我们使用微 CT 对对比体模和大鼠进行成像,并根据相对均方误差和条纹伪影引起的总变差这两个衡量标准来评估重建图像。
从视觉上检查,所提出的方法重建的图像比原始基于 CS 的方法重建的图像具有更小的条纹伪影。定量图像评估研究也表明,该方法优于传统的基于 CS 的方法。
所提出的方法可以有效地抑制稀疏视角 CT 成像中骨结构引起的条纹伪影。