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Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

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PIPN: Physics-inspired phase retrieval network for propagation-based X-ray phase-contrast imaging.

Ziyao Wang, Xianqin Du, Yimin Li

|January 15, 2026

Summary

This summary is machine-generated.

This study introduces a novel physics-inspired phase retrieval network (PIPN) for X-ray imaging. PIPN enables high-quality soft tissue imaging without extensive training data or multiple approximations.

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Area of Science:

Medical Imaging
Computational Physics
Biomedical Engineering

Background:

Propagation-based X-ray phase-contrast imaging (PB-XPCI) offers high-resolution soft tissue visualization.
Phase retrieval from single-distance intensity measurements is an underdetermined inverse problem, often requiring approximations.
Existing methods like deep learning (DL) demand extensive training data or complex computations.

Purpose of the Study:

To develop a data-free, physics-based phase retrieval method for PB-XPCI.
To introduce an accelerated physics-inspired phase retrieval network (PIPN).
To overcome limitations of conventional and DL-based phase retrieval techniques.

Main Methods:

A novel physics-inspired phase retrieval network (PIPN) was developed.
The PIPN utilizes a single approximation condition and a physics imaging model.
An acceleration strategy was implemented for the PIPN.

Main Results:

PIPN achieved phase retrieval without requiring training data.
The accelerated PIPN rapidly reconstructed high-quality phase projections.
The method demonstrated stability across various propagation distances.

Conclusions:

The proposed PIPN offers an efficient and data-free solution for X-ray phase retrieval.
The acceleration strategy enhances reconstruction speed and maintains image quality.
PIPN advances PB-XPCI for soft tissue imaging applications.