Premium
WE‐E‐217BCD‐01: Digital Breast Tomosynthesis: Basic Principles and the QMP's Role
Author(s) -
Dobbins J,
Chakrabarti K
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4736138
Subject(s) - tomosynthesis , medical physics , computer science , mammography , imaging phantom , breast imaging , iterative reconstruction , process (computing) , projection (relational algebra) , artificial intelligence , computer vision , radiology , medicine , breast cancer , algorithm , cancer , operating system
Digital breast tomosynthesis is a form of limited angle tomography, in which section (slice) images are produced from a series of discrete projection images acquired at different angles. Tomosynthesis can be useful in breast imaging by providing potentially better visibility of lesions over conventional mammography, especially in patients with dense breasts. This talk will cover the various physics aspects of DBT, including reconstruction algorithms, the importance of deblurring, and optimizing image acquisition parameters. Remaining important research questions in DBT will be presented and discussed. The presentation will also discuss MQSA Certificate extension process for currently approved digital breast tomosynthesis (DBT) systems. Training requirements, manufacturer required tests for Mammography Equipment Evaluation (MEE) as acceptance tests, and phantom imaging for the purpose of approval of certificate extension will be described. The talk will emphasize the specific tests where special attention must be given and will discuss how the techs should be advised to perform these tests. Learning Objectives: 1. To understand the fundamentals of tomosynthesis reconstruction, including deblurring, algorithm choice, and optimization 2. To understand FDA's certificate extension process for DBT 3. To understand the requirements for MEE 4. To understand the required AEC tracking data Research sponsored in part by NIH, Siemens, and GE Healthcare.