Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01

用于数字乳腺断层合成的高级迭代图像重建 - 重新提交 01

基本信息

批准号：
10224861
负责人：
XIAOCHUAN PAN
金额：
$ 51.79万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-14 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10224861
关键词：
Address Advanced Development Algorithmic Software Algorithms Back Breast Breast Cancer Detection Breast Microcalcification Callback Classification Clinic Clinical Complex Computer software Data Databases Detection Development Devices Digital Breast Tomosynthesis Dimensions Ensure Fiber Image Industrialization Industry Industry Collaboration Investigation Lesion Literature Malignant Neoplasms Mammographic screening Manuals Methodology Modality Patients Performance Play Research Research Personnel Resolution Role Scanning Screening for cancer Signal Transduction Skin Spiculate System Techniques Texture Thick Time Tissues Translating Translations Vendor Visual Work X-Ray Computed Tomography X-Ray Tomography base breast imaging calcification case-by-case basis clinical application clinical practice contrast imaging density design digital image reconstruction imaging properties imaging scientist improved innovation malignant breast neoplasm novel prototype public health relevance radiologist screening simulation success tomography tumor

项目摘要

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.

项目描述数字乳腺断层合成 (DBT) 在乳腺 X 光检查中的应用迅速增长筛选。虽然有证据表明迭代图像重建 (IIR) 算法可能会改进 DBT 图像质量体现在可视化乳房中的肿瘤毛刺和微钙化，没有任何由于DBT硬件的调整，先进IIR的发展与它的发展还有很大差距翻译到诊所。该项目以我们之前在 DBT 的 IIR 开发方面取得的成功为基础，重点关注通过开发新颖的 IIR 算法并将其集成到 DBT 系统中来填补这一空白以自动化方式选择参数，从而实现 IIR 改善 DBT 图像的潜力质量。该项目拥有数百个正常/异常 DBT 病例的数据库以及临床 DBT 系统，以及我们内部成像物理学家和放射科医生的协助。该计划的具体目标研究方向： 1：研究新颖的先进IIR算法； 2A：设计特定于 DBT 的图像质量指标体积表征； 2B：根据基于仿真的 IQ 指标确定 IIR 算法参数； 3：定量评估自动化高级 IIR 在 DBT 成像上的性能。的好处目标 1-2 产生的自动化 IIR 算法将由专家观察员在目标 3 中进行定量评估反对与 DBT 相关的成像任务的临床处理。拟建项目具有较高的临床和技术意义，因为使用 DBT 进行乳房 X 光检查正在成为美国的标准，并且因为所提出的研究使高级 IIR 的转化能够影响 DBT 临床应用。我们将直接在工业领先的扫描仪上开发自动化 IIR 算法，我们的诊所采用了 Hologic Selenia Dimensions，因此在该项目中取得的改进可能会产生以下效果：在提高灵敏度和降低回访率方面对乳房 X 光筛查产生直接影响。为该项目组建的团队包括领先的成像科学家、物理学家和乳腺成像专家放射科医生以及工业顾问。