X-ray Near-Field Holotomography Reconstruction Using Implicit Neural Representations

X-ray near-field holotomography provides non-destructive, in situ 3D visualization of specimen interiors at nanometer-scale resolution.Reconstruction traditionally involves two separate steps: first retrieving the projected phase for different rotation angles, then applying tomographic reconstruction to obtain a 3D volume from 2D projections.Both steps are ill-posed inverse problems and separating them leads to information loss, due to reconstruction errors.Recent advances in implicit neural representations (INRs) have demonstrated remarkable capabilities in scene rendering and tomographic reconstruction.In this work, we propose a unified INR-based framework that jointly solves the phase retrieval and tomographic reconstruction problems.This joint formulation enforces 3D consistency, resulting in significantly improved phase, absorption, and volumetric reconstructions.Moreover, INRs provide substantial data compression.This compression reduces storage requirements by $95\%$, which is particularly important with the advent of fourth-generation synchrotron sources and the corresponding growth in data volume.