Task-based Optimization of Acquisition Parameters in Digital Breast Tomosynthesis

项目摘要 在这个研究项目中，我们提出了基于任务的数字乳房断层合成的优化。 (DBT)关于使用采集定制图像重建算法的采集方案。DBT 正迅速在临床上与传统的二维乳房X光检查一起用于乳腺癌筛查。 乳房X光摄影固有地受限于乳房的2D图像中的3D结构的重叠， 允许表明乳腺癌的形态被致密的结构隐藏或由 重叠组织的叠加。DBT通过通过有限的- 角度，断层扫描方法。虽然DBT的潜在优势已在 在文献中，关于捕获方案的通道优化仍然是一个活跃的领域 研究。在过去的一段时间里，已经在DBT中进行了许多基于任务的优化研究 十年，但这些通常将他们的注意力限制在单一的重建算法上。这个 拟议的工作通过对采集方案进行基于任务的比较来解决这一问题，同时 使用基于任务的客观度量设计的针对采集量身定做的重建算法。 对于这一点，将使用传统的和利用稀疏性的高级重建算法 目的。通过对重建算法的剪裁，这项工作也有望提供一种 在所考虑的采集方案中，图像质量的客观改善。《公约》的具体目标 本文的主要工作有：(1)研究了传统的变采样率DBT重建算法 研究了基于稀疏性优化的DBT图像重建方法，(3)研究了基于稀疏性的DBT图像重建方法 并为算法优化设计了特定于DBT的图像质量度量；(4)执行基于任务的 使用人类观察者研究评估针对获取量身定制的算法。 第一个目标将解决当前图像重建方法的量身定做 实践兴趣，以便建立比较更先进算法的基线。这个 第二个目标将集中在高级、稀疏开发、优化的开发和研究上。 基于DBT的图像重建算法。这一目标将涉及开发高效的 这些重建技术的算法和研究。第三个目标，以任务为基础 将开发基于DBT临床相关任务的图像质量指标，以调整 以前开发的基于优化的重建算法，收益收购量身定做，高级 重建算法。最后，在目标4中，为收购量身定做了基于任务的比较 重建算法将在人类观察者研究中执行。

Project Summary In this research project, we propose to perform task-based optimization of digital breast tomosynthesis (DBT) with respect to acquisition scheme using acquisition tailored image reconstruction algorithms. DBT is quickly being adopted in clinics alongside conventional 2D mammography for breast cancer screening. Mammography is inherently limited by the overlapping of 3D structures in a 2D image of the breast, allowing morphology indicative of breast cancer to be hidden by dense structures or mimicked by the superposition of overlapping tissues. DBT addresses this issue by providing 3D information via a limited- angle, tomographic approach. While the potential advantages of DBT have been widely demonstrated in the literature, optimization of the modality with respect to acquisition scheme remains an active area of research. A number of task-based optimization studies in DBT have been performed over the past decade, but these have typically confined their attention to a single reconstruction algorithm. The proposed work addresses this issue by performing a task-based comparison of acquisition schemes while employing acquisition-tailored reconstruction algorithms designed using task-based, objective metrics. Both traditional and advanced sparsity-exploiting reconstruction algorithms will be employed for this purpose. Through tailoring of the reconstruction algorithms, the work can also be expected to provide an objective improvement in image quality in the acquisition schemes considered. The specific aims of the proposed project are: (1) investigate traditional DBT reconstruction algorithms with varying acquisition scheme, (2) investigate sparsity-exploiting optimization-based DBT image reconstruction, (3) investigate and design DBT-specific image quality metrics for algorithm optimization, and (4) perform task-based assessment of acquisition-tailored algorithms with human observer studies. The first aim will address the tailoring of current image reconstruction methods to acquisition schemes of practical interest in order to establish a baseline for which to compare more advanced algorithms. The second aim will focus on the development and investigation of advanced, sparsity-exploiting, optimization- based DBT image reconstruction algorithms. This aim will involve both the development of efficient algorithms for, and the investigation of, these reconstruction techniques. In the third aim, task-based image quality metrics based on clinically relevant tasks in DBT will be developed for tuning of the previously developed optimization-based reconstruction algorithms, yielding acquisition tailored, advanced reconstruction algorithms. Lastly, in aim 4, task-based comparison of the acquisition tailored reconstruction algorithms will be performed with human observer studies.