Model-based Reconstruction of Objects with Inexactly Known Components.
Model-based Reconstruction of Objects with Inexactly Known Components.
复制标题
具有不确切已知组件的基于模型的对象重建。
DOI：
10.1117/12.911202
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Siewerdsen,JH
中科院分区：
文献类型：
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作者：
Stayman,JW;Otake,Y;Schafer,S;Khanna,AJ;Prince,JL;Siewerdsen,JH
由于断层扫描重建是病态的，因此结合有关成像体积的附加知识的算法通常可以提高图像质量。当测量结果有噪声或数据缺失时尤其如此。本文提出了一个通用框架，将已知位于视场中的特定组成对象的衰减贡献纳入重建的一部分。诸如手术装置和工具之类的组件可以明确地建模为衰减体积的一部分，但关于它们的位置姿势和可能的变形并不准确地知道。所提出的重建框架称为已知分量重建（KCR），基于这种新颖的对象参数化、基于似然的目标函数以及配准和图像参数之间的交替优化，以共同估计潜在的衰减和未知配准。引入了可变形 KCR (dKCR) 方法，该方法采用基于控制点的扭曲算子来适应组件模型和物理组件之间的形状不匹配，从而允许更通用的不精确已知组件类别。 KCR 和 dKCR 方法适用于成像体积中存在脊柱固定硬件的低剂量锥形束 CT 数据。由于金属部件后面的投影数据中存在光子饥饿效应，此类数据尤其具有挑战性。将所提出的算法与传统的滤波反投影和惩罚似然重建进行比较，发现可以显着提高图像质量。传统方法会出现明显的伪影，使金属附近的破裂或断裂检测变得复杂，而 KCR 框架往往能够提供良好的解剖结构可视化，直至手术设备的边界。
Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional knowledge about the imaging volume generally have improved image quality. This is particularly true when measurements are noisy or have missing data. This paper presents a general framework for inclusion of the attenuation contributions of specific component objects known to be in the field-of-view as part of the reconstruction. Components such as surgical devices and tools may be modeled explicitly as being part of the attenuating volume but are inexactly known with respect to their locations poses, and possible deformations. The proposed reconstruction framework, referred to as Known-Component Reconstruction (KCR), is based on this novel parameterization of the object, a likelihood-based objective function, and alternating optimizations between registration and image parameters to jointly estimate the both the underlying attenuation and unknown registrations. A deformable KCR (dKCR) approach is introduced that adopts a control pointbased warping operator to accommodate shape mismatches between the component model and the physical component, thereby allowing for a more general class of inexactly known components. The KCR and dKCR approaches are applied to low-dose cone-beam CT data with spine fixation hardware present in the imaging volume. Such data is particularly challenging due to photon starvation effects in projection data behind the metallic components. The proposed algorithms are compared with traditional filtered-backprojection and penalized-likelihood reconstructions and found to provide substantially improved image quality. Whereas traditional approaches exhibit significant artifacts that complicate detection of breaches or fractures near metal, the KCR framework tends to provide good visualization of anatomy right up to the boundary of surgical devices.