GPU-based iterative cone-beam CT reconstruction using tight frame regularization
GPU-based iterative cone-beam CT reconstruction using tight frame regularization
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DOI：
10.1088/0031-9155/56/13/004
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发表时间：
2011-07-07
影响因子：
3.5
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Jia, Xun;Dong, Bin;Jiang, Steve B.
在大多数图像引导的放射治疗过程中，来自连续锥束计算机断层扫描(CBCT)的X射线成像剂量引起了临床的关注。本文的目标是开发一种基于图形处理单元(GPU)的快速算法，从欠采样和有噪声的投影数据中重建高质量的CBCT图像，从而降低成像剂量。为此，我们提出了一种基于迭代紧框架(TF)的CBCT重建算法。在迭代过程中附加了真实CBCT图像在TF基下具有稀疏表示的条件，作为解的正则化。为了加快计算速度，采用了多重网格方法。我们的GPU实现具有很高的计算效率，可以在5分钟内重建出分辨率为512×512×70的CBCT图像。我们已经在一个数字NCAT模型和一个物理Catphan模型上测试了我们的算法。实验结果表明，该算法能够在欠采样和低mAs的情况下重建CBCT。我们还从调制传递函数和不同扫描条件下的对比度噪声比两个方面对重建的CBCT图像质量进行了定量分析。实验结果表明，该算法具有较高的CBCT图像质量。此外，我们的算法也已经在一个真实的临床环境中得到了验证，使用了一个头颈部患者的病例。在不同的情况下，从重建图像质量和计算效率方面对改进的TF算法和当前最先进的TV算法进行了比较。
The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises a clinical concern in most image-guided radiation therapy procedures. It is the goal of this paper to develop a fast graphic processing unit (GPU)-based algorithm to reconstruct high-quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight-frame (TF)-based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512 x 512 x 70 can be reconstructed in similar to 5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstruct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of the modulation-transfer function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.