[论文解读] GPU-based Fast Low-dose Cone Beam CT Reconstruction via Total Variation

Xun Jia, Yifei Lou+6 more|arXiv (Cornell University)|Jan 4, 2010

Medical Imaging Techniques and Applications参考文献 29被引用 4

一句话总结

本文提出了一种基于GPU加速的迭代重建算法，用于低剂量锥束CT（CBCT）扫描，采用总变差（TV）正则化以减少辐射暴露。通过利用适合GPU的前向-后向分裂算法并结合多网格加速技术，该方法可在每束射线仅0.1 mAs/投影的情况下，仅用40组投影即实现高质量CBCT图像重建，辐射剂量最高可降低36倍，重建时间约为130秒——比传统迭代方法快约100倍。

ABSTRACT

Cone-beam CT (CBCT) has been widely used in image guided radiation therapy (IGRT) to acquire updated volumetric anatomical information before treatment fractions for accurate patient alignment purpose. However, the excessive x-ray imaging dose from serial CBCT scans raises a clinical concern in most IGRT procedures. The excessive imaging dose can be effectively reduced by reducing the number of x-ray projections and/or lowering mAs levels in a CBCT scan. The goal of this work is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital NCAT phantom and a head-and-neck patient case. The performance under low mAs is also validated using a physical Catphan phantom and a head-and-neck Rando phantom. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for IGRT patient alignment purpose. Phantom experiments indicated that CBCT images can be successfully reconstructed with our algorithm under as low as 0.1 mAs/projection level. Comparing with currently widely used full-fan head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, it is estimated that an overall 36 times dose reduction has been achieved with our algorithm. Moreover, the reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ~100 times faster than similar iterative reconstruction approaches.

研究动机与目标

• 为解决图像引导放疗（IGRT）中重复CBCT扫描导致的辐射剂量过高的临床问题。
• 实现在低剂量下获取的欠采样和噪声较大的投影数据中高质量CBCT图像的重建。
• 开发一种快速、基于GPU加速的算法，在显著降低成像剂量的同时保持图像质量。

提出的方法

• 将CBCT图像重建建模为最小化包含数据保真项与总变差（TV）正则化项的能量泛函问题。
• 设计了一种针对GPU优化的前向-后向分裂算法，以高效求解TV正则化优化问题。
• 采用多网格技术加速收敛过程，并提升在GPU架构上的重建速度。
• 使用数字NCAT体模、患者解剖结构以及物理Catphan和Rando体模进行验证。
• 通过TV最小化实现迭代重建，以抑制低剂量数据中的噪声并保留边缘特征。
• 通过充分利用GPU的并行计算能力与内存合并访问机制，优化计算性能。

实验结果

研究问题

• RQ1 总变差正则化是否能有效从严重欠采样且噪声较大的低剂量投影数据中重建出高质量的CBCT图像？
• RQ2 在保持IGRT患者摆位诊断图像质量的前提下，可实现多大程度的剂量降低？
• RQ3 与传统迭代方法相比，GPU加速能否将重建时间缩短至临床可接受的水平？
• RQ4 实现诊断上有用的CBCT图像重建所需的最少投影数和mAs水平是多少？
• RQ5 多网格技术如何提升基于GPU的迭代重建中的重建速度与收敛性能？

主要发现

• 仅使用40组X射线投影，每束投影剂量为0.1 mAs/投影，即可获得满足IGRT患者摆位需求的CBCT图像质量。
• 与标准全扇形头颈部扫描协议（360组投影，0.4 mAs/投影）相比，该方法实现了约36倍的剂量降低。
• 在NVIDIA Tesla C1060 GPU上，重建时间约为130秒，相比传统迭代方法实现了约100倍的速度提升。
• 物理体模实验验证了在0.1 mAs/投影的超低剂量水平下仍能成功实现图像重建。
• GPU优化算法在有效抑制低剂量数据噪声的同时，保持了图像保真度与边缘清晰度。
• 多网格技术显著提升了收敛速度，且未影响图像质量。