基于降低设备制造成本或辐射剂量等目的,计算机断层成像(Computer Tomography,CT)中的一个实际需求是以有限的探测器尺寸来获得更大的视野(Field of View,FOV),通过将探测器放置在横向偏移位置可以有效的扩大FOV.然而,常规的重建算法无法精确重建偏置投影数据,针对这一问题,本文提出了一种基于自适应加权增强总变差最小化的偏置重建模型及CP(Chambolle-Pock)求解算法.具体来说,构建自适应加权增强总变差范数作为正则项,其中自适应权重根据局部增强梯度自适应调整权值,进而设计了一种基于自适应加权增强总变差最小化的偏置重建模型(Weighted Adaptive-weight reinforced Total Variation,WAwrTV),并推导出 了 相应的 CP 算法.实验结果表明,所提算法能有效的重建偏置投影数据并提高重建精度,且具有良好的抗噪性能.
Image reconstruction based on adaptive weighting enhanced total variation for CT with a displaced detector array
In order to reduce manufacturing costs or radiation doses,one of the practical needs in com-puter tomography(CT)is to obtain a larger field of view(FOV)with a limited-size detector,which can be realized by placing the detector with a laterally offset.However,conventional reconstruction algorithms cannot accurately reconstruct images from these projection data with detector offset.To solve this problem,this paper proposes a reconstruction model based on adaptive weighting enhanced total variation minimiza-tion(WAwrTV)and its Chambolle-Pock(CP)solving algorithm.The model constructs an adaptive weighting enhanced gradient norm as a regularization term and includes a bias-weighted fidelity term.In experiments,Projections with detector offset were simulated using both the ThoraxRecon phantom and real CT images.Reconstruction images are qualitatively and quantitatively analyzed.Results demonstrate that the proposed algorithm effectively reconstructs projection data with detector offset,improves reconstruction accuracy,and exhibits good noise resistance.
万方数据
维普
