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Radiographic Tomosynthesis: Reconstruction Algorithms

J Dobbins

J Dobbins1*, (1) Duke University Medical Center, Durham, NC

TU-A-217A-2 Tuesday 8:00:00 AM - 9:55:00 AM Room: 217A

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 algorithms

Research sponsored in part by NIH and GE Healthcare.

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