Marriott Marquis Houston
Houston, Texas, United States
15 - 20 February 2020
Course (SC1292)
Technological Assessment of X-Ray Based Breast Imaging Systems Using Anthropomorphic Phantoms
Saturday 15 February 2020
8:30 AM - 12:30 PM

FormatCourse
Member Price $325.00
Non-Member Price $380.00
Student Member Price $182.00
register
Course
Details
  • Course Level:
  • Introductory
  • CEU:
  • 0.4
Course
Summary
Development of new breast X-ray imaging technologies or improvements to hardware or software of current systems usually require the accurate assessment of image quality. Image quality assessment methods are also required for quality control (QC) of clinical systems, for example as required by the U.S. Mammography Quality Standards Act (MQSA) program. The gold standard for assessment of image quality is human reader studies assessing diagnostic performance over a cohort of representative clinical images. These clinical trials are often difficult and expensive to perform, and therefore researchers have been studying alternative approaches that can assess diagnostic task performance without imaging patients. This short course will discuss methods for objectively assessing task performance of breast imaging systems without conducting a clinical trial. One approach that will be discussed is the in silico modeling of a clinical trial. This approach involves complete computer modeling of each step in the imaging chain including: 1) modeling of breast and relevant breast lesions, 2) modeling of the imaging system, and 3) modeling of the observer. Another more experimental approach that will also be discussed involves: 1) development of anthropomorphic physical phantoms with diagnostic features, 2) imaging of these phantoms on breast imaging commercial or prototype systems, and 3) assessment of task performance with either model or human observers. For maximum efficiency, the proposed in silico and experimental approaches require the development of computer or model observers that can emulate either ideal or human observer task performance. This short course will discuss the use of new machine learning algorithms that can be used to model observer performance in the assessment of breast imaging technology. This course will describe and make attendees aware of useful open-source software tools that can be downloaded.
Learning Outcomes
  • Summarize new methods for generating both digital and physical anthropomorphic breast phantoms that can be used for objective technology assessment.
  • Identify new open-source software that can be used to generate digital breast phantoms.
  • Summarize an approach for in silico Monte Carlo modeling of breast imaging systems.
  • Describe and learn about approaches to validate modeling tools.
  • Identify open-source Monte Carlo software available for in silico modeling of breast imaging systems.
  • Describe various approaches to modeling observers for breast phantom images. These include both conventional and machine-learning based model observers.
  • List advantages and disadvantages of various model observers.
  • Describe a new approach for objectively assessing breast imaging detectors.
  • Explain how to use machine learning model observers to assess task performance achieved with various breast imaging modalities.
  • Describe and learn about an in silico clinical trial conducted by the FDA to compare task performance with full-field digital mammography and digital breast tomosynthesis.
Intended
Audience
Scientists, engineers, technicians, or managers who wish to learn more about how to objectively assess breast imaging technology. Anyone who wants to learn more about; 1) optimizing new breast imaging systems, 2) optimizing new hardware or software based modifications to current breast imaging systems, 3) possible approaches of assessing system effectiveness for regulatory submissions, and 4) new methods that can be used for quality control of clinical breast imaging systems. Familiarity with x-ray interactions in tissue and digital imaging systems would be helpful.
About the
Instructors
Stephen J. Glick is a Research Biomedical Engineer in the Division of Imaging, Diagnostics, and Software Reliability at the U.S. Food and Drug Administration. He received the Ph.D. degree in Biomedical Engineering from Worcester Polytechnic Institute (WPI) in 1991. From 1991 to 2014, he held positions of Assistant Professor, Associate Professor and Professor in the Department of Radiology at University of Massachusetts Medical School. His primary research interests include the development, optimization and evaluation of new methods for x-ray imaging of breast cancer, and he has published over 70 peer-reviewed journal papers, 120 conference proceedings papers, and 10 book chapters. He is an AAPM Fellow and is currently on the Board of Associate Editors for the journals Medical Physics, and Journal of Medical Imaging.
Hilde Bosmans received the Ph.D. degree in 1992 with a thesis on MR Angiography. Her activities in breast cancer screening started with involvement in the early EU projects of the European Breast Cancer Network, where she took the lead in 1996 for the contributions on physico-technical QA. Today, she assures, with her team, medical physics expertise in the radiology department of the University Hospitals of Leuven, in 5 regional hospitals and for a network of 102 breast cancer screening sites. Dr. Bosmans has made scientific contributions in (model) observer work, development of phantoms, virtual clinical trials, dosimetry and measurement techniques in general. She has currently 11 PhD students and a track record of 235 peer reviewed publications and 73 SPIE papers. She is member of the Physico-Technical Steering Group of EUREF, of the Belgian IEC committee and of several task groups of EFOMP and AAPM dealing with Quality Assessment of radiological devices. She is coordinator of the EUTEMPE-RX courses for medical physics experts.
Andreu Badal is a Staff Fellow in the Division of Imaging, Diagnostics, and Software Reliability at the U.S. Food and Drug Administration. Dr. Badal earned a B.Sc. in Physics from the University of Barcelona, and a Ph.D. in Nuclear Engineering from the Universitat Politecnica de Catalunya in Barcelona (Spain), working in the research group developing the Monte Carlo code PENELOPE. He has specialized in the application of Monte Carlo radiation transport simulation methods in medical imaging, and is the main developer of MC-GPU, the first GPU-accelerated Monte Carlo code for the simulation of x-ray imaging devices.
Back to Top