Reducing Poisson noise and baseline drift in X-ray spectral images with bootstrap Poisson regression and robust nonparametric regression

TL;DR

This paper introduces a novel algorithm combining bootstrap Poisson regression and robust nonparametric regression to enhance the signal-to-noise ratio in low-photon-count X-ray spectral images, improving quantitative analysis accuracy.

Contribution

The paper presents a new method that integrates confidence interval testing, bootstrap Poisson regression, and robust baseline correction for better denoising of spectral images.

Findings

Outperforms existing methods in SNR improvement

Accurately estimates trace element concentrations

Enhances quantitative analysis precision

Abstract

X-ray spectral imaging provides quantitative imaging of trace elements in biological sample with high sensitivity. We propose a novel algorithm to promote the signal-to-noise ratio (SNR) of X-ray spectral images that have low photon counts. Firstly, we estimate the image data area that belongs to the homogeneous parts through confidence interval testing. Then, we apply the Poisson regression through its maximum likelihood estimation on this area to estimate the true photon counts from the Poisson noise corrupted data. Unlike other denoising methods based on regression analysis, we use the bootstrap resampling method to ensure the accuracy of regression estimation. Finally, we use a robust local nonparametric regression method to estimate the baseline and subsequently subtract it from the X-ray spectral data to further improve the SNR of the data. Experiments on several real samples show that the proposed method performs better than some state-of-the-art approaches to ensure accuracy and precision for quantitative analysis of the different trace elements in standard reference biological sample.