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

• 批准号: 9978584
• 负责人: XIAOCHUAN PAN
• 金额: $ 52.85万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-14 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9978584
• 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

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