Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction.
Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction.
复制标题
DOI：
10.1088/1361-6560/aa6a97
复制
发表时间：
2017-06-21
影响因子：
3.5
通讯作者：
Webster Stayman J
中科院分区：
文献类型：
--
作者：
Gang GJ;Siewerdsen JH;Webster Stayman J
在诊断性CT扫描仪上，常规采用管电流调制（TCM）来降低剂量。为了满足简单的基于噪声的图像质量要求，传统的TCM策略通常用于滤波反向投影（FBP）重建。这项工作研究了基于模型的迭代重建（MBIR）的TCM设计，以实现由基于任务的图像质量度量确定的最佳成像性能。此外，正则化是与TCM共同优化的MBIR的一个重要方面，它既包括控制整体平滑的正则化强度，也包括允许控制局部噪声的各向同性/各向异性和分辨率特性的方向权重。最初的调查集中在图像体积中单个位置的已知成像任务上。该框架采用傅里叶近似和解析近似来快速估计局部噪声功率谱（NPS）和调制传递函数(MTF) -每个都依赖于TCM和正则化。对于单位置优化，直接采用具体任务的局部可检测性指数（d '）作为目标函数。采用协方差矩阵自适应进化策略（CMA-ES）算法识别成像参数的最优组合。对传统方法和任务驱动方法的评估在腹部幻影中进行了肾脏中频识别任务。在传统策略中，使用最小方差准则的TCM模式最优FBP在应用于MBIR时的任务性能比未调制策略差。此外，任务驱动的中医MBIR设计被发现与传统的FBP设计具有相反的行为，腹部较弱的视图受到更大的影响，而较弱的侧面视图受到更小的影响。由于MBIR中的数据加权，这种TCM模式夸大了MTF和NPS的内在各向异性。定向惩罚设计也强化了这一趋势。任务驱动的方法优于传统的方法，通过TCM和正则化的联合优化，最大的d '提高了13%。这项工作表明，中医最优的MBIR与传统的FBP重建策略不同，最优的FBP策略在应用于MBIR时是次优的，甚至可能降低性能。任务驱动成像框架为优化MBIR的采集和重建提供了一种有前途的方法，可以提高成像性能和/或剂量利用率，超越传统的成像策略。
Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF) - each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d′) of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d′ of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.