On the impact of input feature selection in deep scatter estimation for positron emission tomography

Yannick Berker; Marc Kachelrieß

doi:10.1117/12.2534281

28 May 2019 On the impact of input feature selection in deep scatter estimation for positron emission tomography

Yannick Berker, Marc Kachelrieß

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110720S (2019) https://doi.org/10.1117/12.2534281
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Deep scatter estimation (DSE) for X-ray computed tomography or positron emission tomography (PET) uses convolutional neural networks (CNNs) to estimate scatter distributions. We investigate the impact of physically motivated transformations and combinations of emission and attenuation input features on PET-DSE performance. Therefore, we decompose the analytical expression of a convolutional scatter model into different feature sets as a function of measured prompts and attenuation correction factors, and propose to use individual attenuation sinograms of central slabs and peripheral regions. Data from 20 patients ( 71 bed positions, 17 892 direct views) were collected and used to train CNNs to estimate the single scatter simulation (SSS) from various feature sets. Adding redundant attenuation features improved the convergence of validation metrics. Slab-wise attenuation sinograms improved training mean absolute errors by 10% and early-epoch validation metrics, yet without improvement in later epochs. In conclusion, physically motivated transformation of input features can help improve training and estimation performance in PET-DSE.

Citation Download Citation

Yannick Berker and Marc Kachelrieß "On the impact of input feature selection in deep scatter estimation for positron emission tomography", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110720S (28 May 2019); https://doi.org/10.1117/12.2534281

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KEYWORDS

Positron emission tomography

Convolution

Feature selection

Photons

Convolutional neural networks

Monte Carlo methods

Computer simulations

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