Task-based Optimization of Acquisition Parameters in Digital Breast Tomosynthesis

项目摘要 在本研究项目中，我们提出了基于任务的数字乳腺断层合成优化 (DBT)关于使用采集定制的图像重建算法的采集方案。DBT 与传统的2D乳房X线摄影一起迅速被临床采用，用于乳腺癌筛查。 乳房X线照相术固有地受到乳房的2D图像中的3D结构的重叠的限制， 从而使得指示乳腺癌的形态被致密结构隐藏或被致密结构模仿。 重叠组织的叠加。DBT通过提供3D信息解决了这个问题，通过有限的- 角度，断层摄影方法。虽然DBT的潜在优势已被广泛证明， 根据文献，关于采集方案的模态优化仍然是一个活跃的领域， research.在过去，已经进行了许多基于任务的DBT优化研究 十年来，但是这些通常将他们的注意力限制在单个重建算法上。的 建议的工作通过对采购方案进行基于任务的比较来解决这一问题， 采用使用基于任务的客观度量设计的采集定制重建算法。 传统的和先进的稀疏利用重建算法将采用这一点 目的.通过调整重建算法，这项工作也可以预期提供一个 所考虑的采集方案中图像质量的客观改善。的具体目标 本文的主要工作是：（1）研究了传统的DBT重建算法 方案，（2）研究基于稀疏利用优化的DBT图像重建，（3）研究 并设计DBT专用的图像质量指标用于算法优化，以及（4）执行基于任务的 通过人类观察者研究评估采集量身定制的算法。 第一个目标将解决目前的图像重建方法的采集方案的剪裁， 为了建立一个比较更先进算法的基线，我们需要考虑实际的兴趣。的 第二个目标将侧重于开发和研究先进的，稀疏开发，优化- 基于DBT的图像重建算法这一目标将涉及发展高效的 这些重建技术的算法和研究。第三个目标，基于任务的 将开发基于DBT中临床相关任务的图像质量指标， 先前开发的基于优化的重建算法，产生量身定制的采集， 重建算法最后，在aim 4中，基于任务的比较量身定制的获取 将使用人类观察者研究来执行重建算法。

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.