Department of Physics, University of Alberta, Edmonton, Alberta, Canada.
Med Phys. 2012 Mar;39(3):1481-94. doi: 10.1118/1.3685578.
The first aim of this study is to investigate the feasibility of online autocontouring of tumor in low field MR images (0.2 and 0.5 T) by means of a phantom and simulation study for tumor-tracking in linac-MR systems. The second aim of this study is to develop an MR compatible, lung tumor motion phantom.
An autocontouring algorithm was developed to determine both the position and shape of a lung tumor from each intra fractional MR image. To initiate the algorithm, an expert user contours the tumor and its maximum anticipated range of motion (herein termed the Background) using pretreatment scan data. During treatment, the algorithm processes each intrafractional MR image and automatically contours the tumor. To evaluate this algorithm, the authors built a phantom that replicates the low field contrast parameters (proton density, T(1), T(2)) of lung tumors and healthy lung parenchyma. This phantom allows simulation of MR images with the expected lung tumor CNR at 0.2 and 0.5 T by using a single 3 T scanner. Dynamic bSSFP images (approximately 4 images per second) are acquired while the phantom undergoes a series of preprogrammed motions based on patient lung tumor motion data. These images are autocontoured off-line using our algorithm. The fidelity of autocontouring is assessed by comparing autocontoured tumor shape and its centroid position to the actual tumor shape and its position.
The algorithm successfully contoured the shape of a moving tumor model from dynamic MR images acquired every 275 ms. Dice's coefficients of > 0.96 and > 0.93 are achieved in 0.5 and 0.2 T equivalent images, respectively. Also, the algorithm tracked tumor position during dynamic studies, with root mean squared error (RMSE) values of < 0.55 and < 0.92 mm for 0.5 and 0.2 T equivalent images, respectively. Autocontouring speed is approximately 5 ms for each image.
Dice's coefficients of > 0.96 and > 0.93 are achieved between autocontoured and real tumor shapes, and the position of a tumor can be tracked with RMSE values of < 0.55 and < 0.92 mm in 0.5 and 0.2 T equivalent images, respectively. These results demonstrate the feasibility of lung tumor autocontouring in low field MR images, and, by extension, intrafractional lung tumor tracking with our laboratory's linac-MR system.
本研究的首要目的是通过体模和模拟研究，调查在低磁场磁共振图像（0.2 和 0.5T）中进行肿瘤自动勾画的可行性，以实现直线加速器-磁共振系统中的肿瘤跟踪。本研究的第二个目的是开发一种兼容磁共振的、用于模拟肺肿瘤运动的体模。
开发了一种自动勾画算法，用于从每个分次内磁共振图像中确定肺肿瘤的位置和形状。为了启动该算法，专家用户使用预处理扫描数据勾勒肿瘤及其最大预期运动范围（以下称为“背景”）。在治疗过程中，该算法处理每个分次内磁共振图像并自动勾勒肿瘤。为了评估该算法，作者构建了一个体模，该体模复制了肺肿瘤的低场对比度参数（质子密度、T1、T2）和健康的肺实质。通过使用单个 3T 扫描仪，该体模可以模拟预期的肺肿瘤在 0.2 和 0.5T 时的信噪比。在体模根据患者肺肿瘤运动数据进行一系列预设运动时，采集动态 bSSFP 图像（大约每秒 4 幅图像）。使用我们的算法离线自动勾画这些图像。通过将自动勾画的肿瘤形状及其质心位置与实际肿瘤形状及其位置进行比较，评估自动勾画的准确性。
该算法成功地从每 275ms 采集的动态磁共振图像中勾勒出移动肿瘤模型的形状。在 0.5 和 0.2T 等效图像中，Dice 系数分别>0.96 和>0.93。此外，该算法在动态研究中跟踪肿瘤位置，在 0.5 和 0.2T 等效图像中，均方根误差（RMSE）值分别<0.55 和<0.92mm。自动勾画速度大约为每幅图像 5ms。
在 0.5 和 0.2T 等效图像中，自动勾画的肿瘤形状与真实肿瘤形状之间的 Dice 系数分别>0.96 和>0.93，肿瘤位置可以通过 RMSE 值<0.55 和<0.92mm 进行跟踪。这些结果表明，在低磁场磁共振图像中进行肺肿瘤自动勾画是可行的，并且可以扩展到我们实验室的直线加速器-磁共振系统中的分次内肺肿瘤跟踪。
