Presentation + Paper
10 March 2017 Real space channelization for generic DBT system image quality evaluation with channelized Hotelling observer
Author Affiliations +
Abstract
Digital breast tomosynthesis (DBT) is a relatively new 3D mammography technique that promises better detection of low contrast masses than conventional 2D mammography. The parameter space for DBT is large however and finding an optimal balance between dose and image quality remains challenging. Given the large number of conditions and images required in optimization studies, the use of human observers (HO) is time consuming and certainly not feasible for the tuning of all degrees of freedom. Our goal was to develop a model observer (MO) that could predict human detectability for clinically relevant details embedded within a newly developed structured phantom for DBT applications. DBT series were acquired on GE SenoClaire 3D, Giotto Class, Fujifilm AMULET Innovality and Philips MicroDose systems at different dose levels, Siemens Inspiration DBT acquisitions were reconstructed with different algorithms, while a larger set of DBT series was acquired on Hologic Dimensions system for first reproducibility testing. A channelized Hotelling observer (CHO) with Gabor channels was developed The parameters of the Gabor channels were tuned on all systems at standard scanning conditions and the candidate that produced the best fit for all systems was chosen. After tuning, the MO was applied to all systems and conditions. Linear regression lines between MO and HO scores were calculated, giving correlation coefficients between 0.87 and 0.99 for all tested conditions.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dimitar Petrov, Lesley Cockmartin, Nicholas Marshall, Liesbeth Vancoillie, Kenneth Young, and Hilde Bosmans "Real space channelization for generic DBT system image quality evaluation with channelized Hotelling observer", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360N (10 March 2017); https://doi.org/10.1117/12.2254385
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Digital breast tomosynthesis

Reconstruction algorithms

Image quality

3D acquisition

3D modeling

Mammography

Medical imaging

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