SSNP-IDT: Split-step non-paraxial model for intensity diffraction tomography

Python implementation of paper: High-fidelity intensity diffraction tomography with a non-paraxial multiple-scattering model.

This repository includes a highly flexible and easy-to-use Python package based on PyCUDA library, and code examples for simulation and reconstruction.

Citation

If you find this project useful in your research, please consider citing our paper:

J. Zhu, H. Wang, and L. Tian, "High-fidelity intensity diffraction tomography with a non-paraxial multiple-scattering model," Opt. Express 30, 32808-32821 (2022).

Introduction

We propose a novel intensity diffraction tomography (IDT) reconstruction algorithm based on the split-step non-paraxial (SSNP) model for recovering the 3D refractive index (RI) distribution of multiple-scattering biological samples. High-quality IDT reconstruction requires high-angle illumination to encode both low- and high- spatial frequency information of the 3D biological sample. Our SSNP model can more accurately compute multiple scattering from high-angle illumination compared to paraxial approximation-based multiple-scattering models. We apply this SSNP model to both sequential and multiplexed IDT techniques. We develop a unified reconstruction algorithm for both IDT modalities that is highly computationally efficient and is implemented by a modular automatic differentiation framework. We demonstrate the capability of our reconstruction algorithm on both weakly scattering buccal epithelial cells and strongly scattering live C. elegans worms and live C. elegans embryos.

Annular and multiplexed IDT setups

SSNP model and reconstruction

Results visualization

Annular IDT result

Multiplexed IDT result

Installation

1. Prepare the environment for PyCUDA library (the PyCUDA wiki page was not updated for a long time and hard to follow)
   1. Install a C/C++ compiler, and add it to your PATH (gcc for Linux and cl.exe for Windows)
   2. Install the CUDA library. If you prefer conda as virtual environment manager, you can install the nvidia::cuda package:

   conda install nvidia::cuda

   Note: scikit-cuda cannot find some CUDA versions automatically on Windows. It's possible to change the related code in scikit-cuda rather than reinstall an old version of CUDA, but do it at your own risk.
2. Install the ssnp package in this repository

   pip install "git+https://github.com/bu-cisl/SSNP-IDT#subdirectory=ssnp_pkg"

3. Download the examples and run with Python

License

Project is licensed under the terms of the GPL-v3 license. see the LICENSE file for details