Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01

项目描述 数字乳腺断层摄影（DBT）在乳腺癌摄影中的应用迅速增长 筛选虽然有证据表明迭代图像重建（IIR）算法可以改善 DBT图像质量，在乳房中可视化肿瘤毛刺和微钙化方面， 由于对DBT硬件的调整，先进IIR的发展与其 翻译到诊所这个项目，建立在我们以前成功的IIR开发DBT，重点 通过开发新的IIR算法并将其集成到DBT系统中来填补这一空白， 以自动方式选择参数，从而实现IIR用于改善DBT图像的潜力 质量.该项目有一个数据库，包含数百例正常/异常DBT病例， 系统，以及我们内部成像物理学家和放射科医生的协助。的具体目标 研究内容包括：1：研究新颖的高级IIR算法; 2 A：设计DBT专用的图像质量指标 体积表征; 2B：根据基于模拟的IQ指标确定IIR算法参数; 3： 定量评价自动高级IIR在DBT成像上的性能。的利益 在目标3中，专家观察员将对目标1-2中的自动IIR算法进行定量评估 与DBT相关的成像任务的临床处理。该项目具有高 临床和技术意义，因为使用DBT进行乳房X线摄影筛查正在成为 标准在美国，因为研究提出使先进的IIR的翻译影响DBT 临床应用。我们将直接在行业领先的扫描仪上开发自动IIR算法， Hologic Selenia尺寸，在我们的诊所，因此在这个项目中获得的改进可能有一个 在提高灵敏度和降低回诊率方面，对乳房X线检查产生直接影响。 这个项目的团队包括领先的成像科学家、物理学家和乳房成像专家。 放射科医生，沿着还有工业顾问。

Project Description Digital breast tomosynthsis (DBT) has been growing rapidly in its application to mammographic cancer screening. While evidence exists suggesting that iterative image reconstruction (IIR) algorithms may improve DBT image quality in terms of visualizing tumor spiculations and microcalcifications in the breast without any adjustment to the DBT hardware, there remains a large gap between development of advanced IIR and its translation to the clinic. This project, building upon our previous success on IIR development for DBT, focuses on filling in this gap through development and integration of novel IIR algorithms into DBT systems with the parameter selection in an automated fashion, thus realizing the potential of IIR for improving DBT-image quality. The project has available a database of hundreds of normal/abnormal DBT cases with clinical DBT systems, and the assistance of our in-house imaging physicists and radiologists. The specific aims of the research are: 1: Investigate novel advanced IIR algorithms; 2A: Design image quality metrics specific to DBT volume characterization; 2B: Determine of IIR algorithms parameters from simulation-based IQ metrics; 3: Quantitatively evaluate the performance of automated advanced IIR on DBT imaging. The benefit of the resulting automated IIR algorithms from Aims 1-2 will be evaluated quantitatively in Aim 3 by expert observers against the clinical processing with respect to imaging tasks relevant for DBT. The proposed project has high clinical and technical significance, because the use of DBT for mammography screening is becoming the standard in the US and because the research proposed enables the translation of advanced IIR to impact DBT clinic applications. We will directly develop the automated IIR algorithms on the industrial leading scanner, the Hologic Selenia Dimensions, employed in our clinic, and thus improvements gained in this project may have an immediate impact for mammographic screening in terms of increasing sensitivity and reducing call-back rates. The team assembled for this project includes leading imaging scientists, physicists, and breast-imaging radiologists, along with industrial consultants.