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Proceedings Paper

Application of neural networks to model the signal-dependent noise of a digital breast tomosynthesis unit
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Paper Abstract

This work presents a practical method for estimating the spatially-varying gain of the signal-dependent portion of the noise from a digital breast tomosynthesis (DBT) system. A number of image processing algorithms require previous knowledge of the noise properties of a DBT unit. However, this information is not easily available and thus must be estimated. The estimation of such parameters requires a large number of calibration images, as it often changes with acquisition angle, spatial position and radiographic factors. This could represent a barrier in the algorithm’s deployment, mainly for clinical applications. Thus, we modeled the gain of the Poisson noise of a commercially available DBT unit as a function of the radiographic factors, acquisition angle, and pixel position. First, we measured the noise parameters of a clinical DBT unit by acquiring 36 sets of calibration images (raw projections) using uniform phantoms of different thicknesses, within a range of radiographic factors commonly used in clinical practice. With this information, we trained a multilayer perceptron artificial neural network (MLP-ANN) to predict the gain of the Poisson noise automatically as a function of the acquisition setup. Furthermore, we varied the number of calibration images in the learning step of the MLP-ANN to determine the minimum number of images necessary to obtain an accurate model. Results show that the MLP-ANN was able to yield the desired parameters with average error of less than 2%, using a learning dataset limited to only seven sets of calibration images. The accuracy of the model, along with its computational efficiency, makes this method an attractive tool for clinical image-based applications.

Paper Details

Date Published: 9 March 2018
PDF: 11 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730J (9 March 2018); doi: 10.1117/12.2293659
Show Author Affiliations
Fabrício A. Brito, Univ. de São Paulo (Brazil)
Lucas R. Borges, Univ. de São Paulo (Brazil)
Igor Guerrero, Univ. de São Paulo (Brazil)
Predrag R. Bakic, The Univ. of Pennsylvania (United States)
Andrew D. A. Maidment, The Univ. of Pennsylvania (United States)
Marcelo A. C. Vieira, Univ. de São Paulo (Brazil)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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