Raffaele Giuseppe Agostino

arXiv2025

Microfluidic gratings for X-ray Phase Contrast Imaging

Fabrication of X-ray gratings has surged in the last two decades thanks to their vast employment in X-ray Phase Contrast Imaging, an imaging technique able to boost X-ray sensitivity to detect otherwise invisible details. These high aspect ratio devices are usually fabricated by complex, costly, multi-step processes that limit their size and volume scaling. These steps commonly involve UV or X-ray lithography, semiconductor selective etching and high-Z metal plating, usually Au, which require expensive tools and materials. Here we present a proof-of-concept fabrication via soft lithography and Hg infusion of microfluidic X-ray absorption gratings and their performance in biomedical imaging. Such fabrication technique requires fewer, less expensive, and more scalable processes using alternative and more sustainable materials, while showing comparable visibility with their conventional Au-based, solid equivalent. Our results constitute a promising shift in X-ray optics fabrication that could significantly lower barriers to commercialization and accelerate the practical deployment of X-ray Phase Contrast Imaging.

physics.optics↗

arXiv2024

μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation

X-ray computed microtomography (μ-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples. These images enable clinicians to examine internal anatomy and gain insights into the disease or anatomical morphology. However, extracting relevant information from 3D images requires semantic segmentation of the regions of interest, which is usually done manually and results time-consuming and tedious. In this work, we propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus. The framework employs an optimized 2D CNN architecture that can infer a 3D segmentation of the sample, avoiding the high computational cost of a 3D CNN architecture. We tackle the challenges of handling large and high-resoluted image data (over a thousand pixels in each dimension) and a small training database (only three samples) by proposing a standard protocol for data normalization and processing. Moreover, we investigate how the noise, contrast, and spatial resolution of the sample and the training of the architecture are affected by the reconstruction technique, which depends on the number of input images. Experiments show that our framework significantly reduces the time required to segment new samples, allowing a faster microtomography analysis of the Carassius auratus heart shape. Furthermore, our framework can work with any bio-image (biological and medical) from μ-CT with high-resolution and small dataset size

eess.IV↗