Hansen David C, Sørensen Thomas Sangild
Department of Oncology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark.
Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200 Aarhus N, Denmark.
Phys Imaging Radiat Oncol. 2018 Mar 8;5:69-75. doi: 10.1016/j.phro.2018.02.004. eCollection 2018 Jan.
BACKGROUND & PURPOSE: Four dimensional Cone beam CT (CBCT) has many potential benefits for radiotherapy but suffers from poor image quality, long acquisition times, and/or long reconstruction times. In this work we present a fast iterative reconstruction algorithm for 4D reconstruction of fast acquisition cone beam CT, as well as a new method for temporal regularization and compare to state of the art methods for 4D CBCT.
MATERIALS & METHODS: Regularization parameters for the iterative algorithms were found automatically via computer optimization on 60 s acquisitions using the XCAT phantom. Nineteen lung cancer patients were scanned with 60 s arcs using the onboard image on a Varian trilogy linear accelerator. Images were reconstructed using an accelerated ordered subset algorithm. A frequency based temporal regularization algorithm was developed and compared to the McKinnon-Bates algorithm, 4D total variation and prior images compressed sensing (PICCS).
All reconstructions were completed in 60 s or less. The proposed method provided a structural similarity of 0.915, compared with 0.786 for the classic McKinnon-bates method. For the patient study, it provided fewer image artefacts than PICCS, and better spatial resolution than 4D TV.
Four dimensional iterative CBCT reconstruction was done in less than 60 s, demonstrating the clinical feasibility. The frequency based method outperformed 4D total variation and PICCS on the simulated data, and for patients allowed for tumor location based on 60 s acquisitions, even for slowly breathing patients. It should thus be suitable for routine clinical use.
四维锥形束CT（CBCT）在放射治疗方面有诸多潜在优势，但存在图像质量差、采集时间长和/或重建时间长的问题。在本研究中，我们提出了一种用于快速采集锥形束CT的4D重建的快速迭代重建算法，以及一种新的时间正则化方法，并与4D CBCT的现有先进方法进行比较。
通过使用XCAT体模在60秒采集数据上进行计算机优化，自动找到迭代算法的正则化参数。使用Varian Trilogy直线加速器上的机载图像对19名肺癌患者进行了60秒弧形扫描。使用加速有序子集算法重建图像。开发了一种基于频率的时间正则化算法，并与麦金农 - 贝茨算法、4D总变差和先验图像压缩感知（PICCS）进行比较。
所有重建均在60秒或更短时间内完成。所提出的方法的结构相似性为0.915，而经典的麦金农 - 贝茨方法为0.786。对于患者研究，它比PICCS产生的图像伪影更少，并且比4D TV具有更好的空间分辨率。
在不到60秒的时间内完成了四维迭代CBCT重建，证明了其临床可行性。基于频率的方法在模拟数据上优于4D总变差和PICCS，对于患者，即使是呼吸缓慢的患者，也能基于60秒的采集进行肿瘤定位。因此，它应该适用于常规临床应用。
