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Development of an Iterative Reconstruction Method for Low Dose CBCT in Proton Therapy Patient Positioning

机译:低剂量CBCT在质子治疗患者定位中的迭代重建方法的开发

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Cone Beam Computed Tomography (CBCT) is used to determine patient position before each irradiation, which results in increased radiation exposure for the patient. To reduce the patient exposure, reduction of either X-ray imaging time or X-ray tube current is necessary. However, this leads to increased noise and reduced contrast in images. To meet this requirement, we have developed an image reconstruction method employing an iterative algorithm that is robust to noise and that can achieve high image contrast. In the iterative image reconstruction process, a system matrix corresponding to the measured geometry is required. In gantry-mounted CBCT, the X-ray tube and the flat panel detector (FPD) deviate some from their ideal position, resulting in a different geometry for each measurement angle. However, full calculation of system matrices requires a large data capacity or significant computation time. To overcome these issues, our method calculates the system matrix for an ideal geometry in advance and performs positional deviation correction on the projection data, allowing us to obtain a reconstructed image with positional deviations taken into account. We applied both the Feldkamp method and the proposed method to digital phantom data with positional deviations and Poisson noise, and by comparing the reconstructed images, we were able to confirm improved noise reduction and image contrast. In addition, by calculating the ratio of the ROI value in the high absorption region with different size and that of BG in the module for evaluating resolution on each iterative image, the number of updates required to restorate the fine distribution was evaluated. As a result, it was found that about 200 updates is required to recover the distribution with 3lp/cm.
机译:锥束计算机断层扫描(CBCT)用于确定每次照射前的患者位置,这会增加患者的放射线暴露量。为了减少患者的暴露,必须减少X射线成像时间或X射线管电流。但是,这导致噪声增加和图像对比度降低。为了满足这一要求,我们开发了一种采用迭代算法的图像重建方法,该算法对噪声具有鲁棒性并且可以实现高图像对比度。在迭代图像重建过程中,需要与测量的几何形状相对应的系统矩阵。在龙门式CBCT中,X射线管和平板探测器(FPD)偏离了它们的理想位置,从而导致每个测量角度的几何形状都不相同。但是,系统矩阵的完整计算需要大数据容量或大量计算时间。为了克服这些问题,我们的方法预先计算了理想几何形状的系统矩阵,并对投影数据执行位置偏差校正,从而使我们能够获得考虑了位置偏差的重建图像。我们将Feldkamp方法和提出的方法同时应用于具有位置偏差和Poisson噪声的数字体模数据,并且通过比较重建的图像,我们能够确定改进的降噪和图像对比度。另外,通过计算具有不同大小的高吸收区域中的ROI值与用于评估每个迭代图像分辨率的模块中的BG的比率,评估了恢复精细分布所需的更新次数。结果,发现需要大约200次更新才能恢复3lp / cm的分布。

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