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TU‐A‐217A‐02: Radiographic Tomosynthesis: Reconstruction Algorithms
Author(s) -
Dobbins J
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 - 10.1118/1.4735892
Subject(s) - tomosynthesis , iterative reconstruction , computer science , projection (relational algebra) , computer vision , artificial intelligence , algorithm , mammography , medicine , cancer , breast cancer
Digital 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. The simplest form of tomosynthesis reconstruction is the shift‐and‐add technique, whereby the projection images are shifted with respect to one another and then summed to render a particular section of the patient. While this technique is efficient, it also leaves blurry artifacts from structures that are outside of the plane of interest. High quality tomosynthesis reconstruction therefore requires some means of reducing these blurry artifacts. Three approaches have been widely reported for tomosynthesis reconstruction with blur removal: (1) filtered backprojection, (2) matrix inversion tomosynthesis, and (3) various iterative reconstruction schemes. Fundamentals of these three approaches will be discussed, along with the advantages and limitations of each. Learning Objectives: 1. To understand the basics of tomosynthesis reconstruction 2. To understand the mathematical background and implementation strategies for filtered backprojection, matrix inversion tomosynthesis, and iterative reconstruction schemes 3. To understand the relative advantages and disadvantages of the three reconstruction algorithmsResearch sponsored in part by NIH and GE Healthcare.