Gang Grace J, Siewerdsen Jeffrey H, Stayman J Webster
G. J. Gang, J. W. Stayman, and J. H. Siewerdsen are with the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205 USA.
Conf Proc Int Conf Image Form Xray Comput Tomogr. 2016 Jul;2016:407-410.
A task-driven imaging framework for prospective fluence field modulation (FFM) is developed in this paper. The design approach uses a system model that includes a parameterized FFM acquisition and model-based iterative reconstruction (MBIR) for image formation. Using prior anatomical knowledge (e.g. from a low-dose 3D scout image), accurate predictions of spatial resolution and noise as a function of FFM are integrated into a task-based objective function. Specifically, detectability index (), a common metric for task-based image quality assessment, is computed for a specific formulation of the imaging task. To optimize imaging performance in across an image volume, a maximin objective function was adopted to maximize the minimum detectability index for many locations sampled throughout the volume. To reduce the dimensionality, FFM patterns were represented using wavelet bases, the coefficients of which were optimized using the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. The optimization was performed for a mid-frequency discrimination task involving a cluster of micro-calcifications in an abdomen phantom. The task-driven design yielded FFM patterns that were significantly different from traditional strategies proposed for FBP reconstruction. In addition to a higher minimum consistent with the objective function, the task-driven approach also improved to a greater extent over a larger area of the phantom. Results from this work suggests that FFM strategies suitable for FBP reconstruction need to be reevaluated in the context of MBIR and that a task-driven imaging framework provides a promising approach for such optimization.
本文开发了一种用于前瞻性注量场调制（FFM）的任务驱动成像框架。该设计方法使用一个系统模型，该模型包括用于图像形成的参数化FFM采集和基于模型的迭代重建（MBIR）。利用先验解剖学知识（例如来自低剂量3D侦察图像），将作为FFM函数的空间分辨率和噪声的准确预测集成到基于任务的目标函数中。具体而言，针对成像任务的特定公式计算可检测性指数（），这是基于任务的图像质量评估的常用指标。为了在整个图像体积上优化成像性能，采用了极大极小目标函数，以最大化在整个体积中采样的许多位置的最小可检测性指数。为了降低维度，使用小波基表示FFM模式，其系数使用协方差矩阵自适应进化策略（CMA-ES）算法进行优化。针对涉及腹部体模中一组微钙化的中频辨别任务进行了优化。任务驱动设计产生的FFM模式与为FBP重建提出的传统策略有显著不同。除了与目标函数一致的更高最小值外，任务驱动方法在体模的更大区域上也有更大程度的改善。这项工作的结果表明，适用于FBP重建的FFM策略需要在MBIR的背景下重新评估，并且任务驱动成像框架为这种优化提供了一种有前途的方法。
