Optimization of Breast Tomosynthesis Imaging Systems for Computer-Aided Detection

Abstract

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)2 The goal of this research is to develop methodology for optimizing acquisition parameters for digital tomosynthesis of the breast (DBT). Optimization is important to be able to study whether DBT can be used in place of screening mammography with better sensitivity and specificity. The research is composed of three parts: 1) to develop a computer model to simulate tomosynthesis, 2) to generate realistic breast and tumor models, and 3) to determine optimal acquisition parameters by using a genetic algorithm with CADe performance as an indicator of fitness. In this report, we summarize the research done for this project to date, focussing particularly on the development of an anthropomorphic software breast phantom and its validation. Lastly, we evaluate methods for measuring anatomic noise in projection imaging.Digital breast tomosynthesis, optimization, computer simulation, anatomic noise, anthropomorphic breast phantom 30

Optimization of computer aided detection systems: An evolutionary approach

“…Litjens et al (2017) have performed a comprehensive-review of applications of deep learning to medical image analysis; their findings confirm that most researchers employ an intuition-based random search to optimize hyper-parameters. Finally, a few authors have also sought to optimize acquisition or reconstruction parameters in order to optimize CAD performance (Lau, 2011;Lee et al, 2017). For instance, Lau (2011) developed realistic phantoms to optimize image acquisition parameters and therefore, indirectly, CAD results.In this work, we have assumed the imaging system and reconstruction algorithm to be fixed, which is the most realistic scenario for CAD development both in industry and academia wherever images are acquired by a commercial scanner in a clinical setting.…”

A scatter correction method for contrast-enhanced dual-energy digital breast tomosynthesis

“…Processing times of 14 min were reported. Investigators have computed scatter point-spread functions (spsf) (Yang et al , 2014; Lau, 2012; Diaz et al , 2014) to study the nature of scatter in DM and DBT. (Zhao et al , 2015) developed a spsf-based patient specific SC method for CBCT.…”