Compressive Phase Contrast Tomography

TL;DR

This paper introduces a method using basis pursuit solvers and GPU-accelerated algorithms to enhance phase contrast tomography, reducing data requirements and artifacts for soft matter imaging.

Contribution

It presents a novel application of sparse reconstruction techniques and GPU computing to improve phase contrast tomography quality and efficiency.

Findings

Enhanced image quality with fewer views and lower radiation dose

Effective removal of ring artifacts in phase contrast images

Significant acceleration of Fourier transform computations using GPU

Abstract

When x-rays penetrate soft matter, their phase changes more rapidly than their amplitude. In- terference effects visible with high brightness sources creates higher contrast, edge enhanced images. When the object is piecewise smooth (made of big blocks of a few components), such higher con- trast datasets have a sparse solution. We apply basis pursuit solvers to improve SNR, remove ring artifacts, reduce the number of views and radiation dose from phase contrast datasets collected at the Hard X-Ray Micro Tomography Beamline at the Advanced Light Source. We report a GPU code for the most computationally intensive task, the gridding and inverse gridding algorithm (non uniform sampled Fourier transform).