[论文解读] GPU-based Cone Beam CT Reconstruction via Total Variation Regularization

Xun Jia, Yifei Lou+5 more|arXiv (Cornell University)|Jan 4, 2010

Medical Imaging Techniques and Applications参考文献 7被引用 6

一句话总结

本文提出了一种基于GPU加速的总变差正则化锥束CT重建算法，可在欠采样和噪声较大的投影数据下实现高质量的CBCT成像，与传统迭代方法相比，重建时间快约100倍，剂量可降低至原来的1/36。

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。
• 实现在显著减少投影数据和低mAs水平下高质量CBCT重建，以最小化患者剂量。
• 开发一种适用于临床日常IGRT工作流程的快速、基于GPU加速的重建算法。
• 在数字体模、物理体模和患者数据上，于临床相关的低剂量条件下验证该方法。

提出的方法

• 采用GPU友好的前向-后向分裂算法求解CBCT重建的总变差正则化优化问题。
• 能量泛函结合了保证投影一致性的数据保真项和促进分段平滑重建的总变差正则化项。
• 采用多网格技术以加速收敛，并提升GPU架构下的重建效率。
• 在数字NCAT体模和头颈部患者病例上评估算法在低剂量条件下的图像质量。
• 利用Catphan和Rando物理体模在低mAs水平（0.1 mAs/投影）下验证性能。
• 在NVIDIA Tesla C1060 GPU上测量重建速度，实现实时或近实时图像重建。

实验结果

研究问题

• RQ1 是否仅需40个投影（而非标准的360个）即可重建出满足临床诊断要求的CBCT图像？
• RQ2 在保持临床可接受图像质量的前提下，每投影的mAs可降低至何种程度（例如0.1 mAs/投影）？
• RQ3 所提出的基于GPU加速的算法与传统迭代重建方法相比，速度如何？
• RQ4 总变差正则化是否能有效抑制低剂量、欠采样CBCT数据中的噪声并保持边缘清晰？
• RQ5 重建图像质量是否足以支持IGRT中准确的患者摆位？

主要发现

• 仅需40个X射线投影即可重建出满足IGRT中患者摆位要求的临床可接受质量的CBCT图像。
• 该算法可在极低剂量水平（0.1 mAs/投影）下成功重建出诊断级质量的CBCT图像。
• 与标准全扇形头颈部扫描协议（360个投影，0.4 mAs/投影）相比，所提方法估计可实现36倍的剂量降低。
• 在NVIDIA Tesla C1060 GPU上，重建时间约为130秒，相比类似迭代重建方法提速约100倍。
• 体模研究证实，该方法在低剂量条件下仍能保持图像保真度和空间准确性。
• 总变差正则化与GPU加速的结合，实现了鲁棒、快速且低剂量的CBCT重建，适用于临床部署。