Hahn Andreas, Knaup Michael, Brehm Marcus, Sauppe Sebastian, Kachelrieß Marc
Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Department of Physics and Astronomy, Ruprecht-Karls-University, Im Neuenheimer Feld 226, Heidelberg, Germany.
Med Phys. 2018 Jun 25. doi: 10.1002/mp.13060.
In image-guided radiation therapy, fiducial markers or clips are often used to determine the position of the tumor. These markers lead to streak artifacts in cone-beam CT (CBCT) scans. Standard inpainting-based metal artifact reduction (MAR) methods fail to remove these artifacts in cases of large motion. We propose two methods to effectively reduce artifacts caused by moving metal inserts.
The first method (MMAR) utilizes a coarse metal segmentation in the image domain and a refined segmentation in the rawdata domain. After an initial reconstruction, metal is segmented and forward projected giving a coarse metal mask in the rawdata domain. Inside the coarse mask, metal is segmented by utilizing a 2D Sobel filter. Metal is removed by linear interpolation in the refined metal mask. The second method (MoCoMAR) utilizes a motion compensation (MoCo) algorithm [Med Phys. 2013;40:101913] that provides us with a motion-free volume (3D) or with a time series of motion-free volumes (4D). We then apply the normalized metal artifact reduction (NMAR) [Med Phys. 2010;37:5482-5493] to these MoCo volumes. Both methods were applied to three CBCT data sets of patients with metal inserts in the thorax or abdomen region and a 4D thorax simulation. The results were compared to volumes corrected by a standard MAR1 [Radiology. 1987;164:576-577].
MMAR and MoCoMAR were able to remove all artifacts caused by moving metal inserts for the patients and the simulation. Both new methods outperformed the standard MAR1, which was only able to remove artifacts caused by metal inserts with little or no motion.
In this work, two new methods to remove artifacts caused by moving metal inserts are introduced. Both methods showed good results for a simulation and three patients. While the first method (MMAR) works without any prior knowledge, the second method (MoCoMAR) requires a respiratory signal for the MoCo step and is computationally more demanding and gives no benefit over MMAR, unless MoCo images are desired.
在图像引导放射治疗中,基准标记物或夹子常被用于确定肿瘤的位置。这些标记物会在锥形束CT(CBCT)扫描中产生条纹伪影。基于标准图像修复的金属伪影减少(MAR)方法在存在大幅度运动的情况下无法去除这些伪影。我们提出了两种方法来有效减少由移动金属植入物引起的伪影。
第一种方法(MMAR)在图像域利用粗略的金属分割,并在原始数据域进行精细分割。在初始重建后,对金属进行分割并向前投影,在原始数据域得到一个粗略的金属掩码。在粗略掩码内部,利用二维索贝尔滤波器对金属进行分割。通过在精细金属掩码中进行线性插值来去除金属。第二种方法(MoCoMAR)利用一种运动补偿(MoCo)算法[《医学物理》。2013年;40:101913],该算法为我们提供一个无运动的容积(3D)或一系列无运动的容积时间序列(4D)。然后我们将归一化金属伪影减少(NMAR)[《医学物理》。2010年;37:5482 - 5493]应用于这些MoCo容积。两种方法都应用于胸部或腹部区域有金属植入物的三名患者的CBCT数据集以及一次4D胸部模拟。将结果与通过标准MAR1[《放射学》。198