Membrane stepping optimization in Modulation Based Imaging

Abstract

Modulation-based imaging (MoBI) is an X-ray phase-contrast technique that uses an intensity modulator (or membrane) in the beam. Although MoBI can be performed in a single shot, multiple exposures are typically needed to improve the quality of the result. The membrane is typically moved using a regular stepping pattern for convenience; however, the impact of the membrane movement scheme on image quality has not been fully investigated yet. In this work, we explore optimized movement strategies aiming at improving MoBI performance. An experimental study tested optimization schemes based on global and local standard deviation metrics, and compared them with regular and random stepping motions. The results demonstrated superior contrast-to-noise ratio and reduced angular sensitivity in the optimized approaches compared to conventional stepping. These results were consistent across different membrane types, with honeycomb membranes showing the highest compatibility with the optimization procedure. Noise power spectrum analysis further validated the advantages of the optimized motion strategies. Overall, the results demonstrate that an optimized membrane movement can significantly improve MoBI image quality without increasing experimental complexity.

Figures (6)

• Figure 1: Experimental setup sketch showing the relevant distances.
• Figure 2: Stepping optimization procedure. For each membrane type, 200 reference/sample image pairs were acquired. Subsets of size N were extracted using different stepping methods: subsets (1) are extracted randomly, subsets (2) are composed of consecutive images, subsets (3) were chosen by minimizing the standard deviation of their average image, and subsets (4) were selected by averaging the images, thus minimizing the standard deviation of their local standard deviation.
• Figure 3: a) Dark field image of the full sample obtained with a subset size of 5 and the honeycomb membrane. b) Absorption, dark field, horizontal refraction, and vertical refraction images retrieved using each optimization procedure: regular, random, global standard deviation, and local standard deviation. Only a cropped region of the image is shown for clarity. c) Quantitative evaluation: graphs displaying the quality metrics measured from the images as a function of the membrane stepping method.
• Figure 4: a) Dark field image of the full sample obtained with a subset size of 5 and the local standard deviation method. b) Absorption, dark field, horizontal refraction, and vertical refraction images retrieved using each membrane: archimede, honeycomb, sandpaper, and Vogel. Only a cropped region of the image is shown for clarity. c) Quantitative evaluation: graphs displaying the quality metrics measured from the images.
• Figure 5: Quality metrics measured from the absorption, refraction and dark-field images for each membrane type, plotted as a function of the number of membrane positions included in the subset.