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Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis

Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis Abstract. We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and Λ -tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis

Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis


We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and -tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JMI.3.1.011008] Keywords: image reconstruction; objective assessment; image quality; Hotelling observer; digital breast tomosynthesis. Paper 15161SSRR received Jul. 30, 2015; accepted for publication Nov. 16, 2015; published online Dec. 22, 2015. Introduction In recent years, digital breast tomosynthesis (DBT) has continued to gain attention as a promising modality for breast imaging. By combining projection images acquired at varying angles, the effects of tissue superposition can be ameliorated, leading to improved visualization of masses and a decrease in false positives for mass detection.1­4 Since the angular range used in tomosynthesis is small, DBT acquisitions contain insufficient data to allow conventional tomographic image reconstruction methods to yield fully three-dimensional (3-D) images. Instead, a range of variants of analytic reconstruction methods from x-ray CT, such as filtered back-projection (FBP), has been developed for DBT in order to obtain quasi-3-D...
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Publisher
SPIE
Copyright
© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Subject
Special Section on Medical Image Perception and Observer Performance; Paper
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.JMI.3.1.011008
pmid
26702408
Publisher site
See Article on Publisher Site

Abstract

Abstract. We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and Λ -tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest.

Journal

Journal of Medical ImagingSPIE

Published: Jan 1, 2016

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