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Self-calibration of cone-beam CT geometry using 3D-2D image registration

机译:使用3D-2D图像配准对锥束CT几何形状进行自校准

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Robotic C-arms are capable of complex orbits that can increase field of view, reduce artifacts, improve image quality, and/or reduce dose; however, it can be challenging to obtain accurate, reproducible geometric calibration required for image reconstruction for such complex orbits. This work presents a method for geometric calibration for an arbitrary source-detector orbit by registering 2D projection data to a previously acquired 3D image. It also yields a method by which calibration of simple circular orbits can be improved. The registration uses a normalized gradient information similarity metric and the covariance matrix adaptation-evolution strategy optimizer for robustness against local minima and changes in image content. The resulting transformation provides a `self-calibration' of system geometry. The algorithm was tested in phantom studies using both a cone-beam CT (CBCT) test-bench and a robotic C-arm (Artis Zeego, Siemens Healthcare) for circular and non-circular orbits. Self-calibration performance was evaluated in terms of the full-width at half-maximum (FWHM) of the point spread function in CBCT reconstructions, the reprojection error (RPE) of steel ball bearings placed on each phantom, and the overall quality and presence of artifacts in CBCT images. In all cases, self-calibration improved the FWHM-e. g. on the CBCT bench, FWHM = 0.86 mm for conventional calibration compared to 0.65 mm for self-calibration (p < 0.001). Similar improvements were measured in RPE-e. g. on the robotic C-arm, RPE = 0.73 mm for conventional calibration compared to 0.55 mm for self-calibration (p < 0.001). Visible improvement was evident in CBCT reconstructions using self- calibration, particularly about high-contrast, high-frequency objects (e. g. temporal bone air cells and a surgical needle). The results indicate that self- calibration can improve even upon systems with presumably accurate geometric calibration and is applicable to situations where conventional calibration is not feasible, such as complex non-circular CBCT orbits and systems with irreproducible source-detector trajectory.
机译:机器人C臂具有复杂的轨道,可以增加视野,减少伪影,提高图像质量和/或减少剂量;然而,对于这样复杂的轨道,获得图像重建所需的准确,可再现的几何校准可能具有挑战性。这项工作提出了一种通过将2D投影数据注册到先前获取的3D图像来对任意源探测器轨道进行几何校准的方法。它还产生了一种方法,通过该方法可以改善简单圆形轨道的校准。配准使用归一化的梯度信息相似性度量和协方差矩阵自适应进化策略优化器,以抵抗局部最小值和图像内容变化的鲁棒性。由此产生的转换提供了系统几何形状的“自校准”。使用锥形束CT(CBCT)测试台和机器人C型臂(Artis Zeego,西门子医疗)在圆形和非圆形轨道上对模型算法进行了幻像测试。根据CBCT重建中点扩散函数的半峰全宽(FWHM),每个模体上放置的钢球轴承的重投影误差(RPE)以及整体质量和外观来评估自校准性能CBCT图像中的伪影。在所有情况下,自校准都会改善FWHM-e。 G。在CBCT工作台上,常规校准的FWHM = 0.86 mm,而自校准的FWHM = 0.65 mm(p <0.001)。在RPE-e中测得了类似的改进。 G。在自动C臂上,常规校准的RPE = 0.73 mm,而自校准的RPE = 0.55 mm(p <0.001)。在使用自校准的CBCT重建中,明显的改善是明显的,特别是在高对比度,高频率的物体(例如颞骨气室和手术针)上。结果表明,即使在假定具有精确几何校准的系统上,自校准也可以改善,并且适用于常规校准不可行的情况,例如复杂的非圆形CBCT轨道和源-探测器轨迹不可再现的系统。

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