Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning

TL;DR

This paper introduces a deep learning approach using U-Net to improve limited angle transmission X-ray microscopy reconstructions, significantly reducing artifacts and enhancing image quality in synthetic and real biological data.

Contribution

First application of deep learning for limited angle TXM reconstruction, training on synthetic data to effectively reduce artifacts in real-world biological imaging.

Findings

U-Net reduces RMSE from 2.55e-3 to 1.21e-3 in synthetic data

SSIM improves from 0.625 to 0.920 with U-Net reconstruction

Further denoising enhances image quality and 3D visualization

Abstract

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts due to missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in $100^\circ$ limited angle tomography. For synthetic test data, the U-Net significantly reduces root-mean-square error (RMSE) from $2.55 \times 10^{-3}$ {\mu}m$^{-1}$ in the FBP reconstruction to $1.21 \times 10^{-3}$ {\mu}m$^{-1}$ in the U-Net reconstruction, and also improves structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least square denoising of measured projections, the RMSE and SSIM are further improved to $1.16 \times 10^{-3}$ {\mu}m$^{-1}$ and 0.932, respectively. For real test data, the proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nano-scale imaging in biology, nanoscience and materials science.