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Split-step non-paraxial method

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# 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**](https://opg.optica.org/oe/fulltext.cfm?uri=oe-30-18-32808).

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

![](https://raw.githubusercontent.com/bu-cisl/SSNP-IDT/master/visualize/1.1.svg)

### SSNP model and reconstruction

![](https://raw.githubusercontent.com/bu-cisl/SSNP-IDT/master/visualize/1.2.svg)

## Results visualization
### Annular IDT result

![](https://raw.githubusercontent.com/bu-cisl/SSNP-IDT/master/visualize/3.svg)

![](https://raw.githubusercontent.com/bu-cisl/SSNP-IDT/master/visualize/4.svg)

### Multiplexed IDT result

![](https://raw.githubusercontent.com/bu-cisl/SSNP-IDT/master/visualize/5.svg)

## Installation

1. Prepare the environment for PyCUDA library (the [PyCUDA wiki page](https://wiki.tiker.net/PyCuda/Installation/) 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`](https://anaconda.org/nvidia/cuda) package:
   ```shell
   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
   ```shell
   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

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