Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01

项目说明 数字化乳腺断层扫描技术(DBT)在乳腺X线摄影中的应用日益广泛 放映。虽然有证据表明迭代图像重建(IIR)算法可以改进 DBT图像质量：显示乳房内的肿瘤毛刺和微钙化 调整到DBT硬件，先进的IIR与其发展仍有很大差距 翻译到诊所。这个项目是在我们之前为DBT开发IIR的成功的基础上进行的，重点是 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 自动选择参数，从而实现IIR在改善DBT图像方面的潜力 质量。该项目已经提供了数百例临床DBT的正常/异常DBT病例的数据库 系统，并得到我们内部成像物理学家和放射科医生的帮助。《公约》的具体目标 研究内容包括：1：研究新的先进IIR算法；2：设计针对DBT的图像质量指标 体积表征；2B：根据基于模拟的IQ度量确定IIR算法参数；3： 定量评估自动高级IIR在DBT成像上的性能。这项计划的好处 由目标1-2生成的自动IIR算法将由专家观察员在目标3中进行定量评估 针对与DBT相关的成像任务的临床处理。拟议中的项目具有很高的 临床和技术意义，因为使用DBT进行乳房X光检查正成为 标准，因为建议的研究使高级IIR能够转换为IMPACT DBT 临床应用程序。我们将在工业领先的扫描仪上直接开发自动化的IIR算法 在我们的临床中使用的Hology Selenia Dimensions，因此在这个项目中获得的改进可能会有一个 对乳房X光检查的直接影响是提高敏感度和降低回调率。 为这个项目组建的团队包括顶尖的成像科学家、物理学家和乳房成像 放射科医生和工业顾问。

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.