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

Fabrício A. Brito; Lucas R. Borges; Igor Guerrero; Predrag R. Bakic; Andrew D. A. Maidment; Marcelo A. C. Vieira

doi:10.1117/12.2293659

9 March 2018 Application of neural networks to model the signal-dependent noise of a digital breast tomosynthesis unit

Fabrício A. Brito, Lucas R. Borges, Igor Guerrero, Predrag R. Bakic, Andrew D. A. Maidment, Marcelo A. C. Vieira

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105730J (2018) https://doi.org/10.1117/12.2293659
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Fabrício A. Brito, Lucas R. Borges, Igor Guerrero, Predrag R. Bakic, Andrew D. A. Maidment, and Marcelo A. C. Vieira "Application of neural networks to model the signal-dependent noise of a digital breast tomosynthesis unit", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730J (9 March 2018); https://doi.org/10.1117/12.2293659

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