🔬 LiquidET_NatComm2024

Reconstruction Algorithm and Post-Processing for 3D Colloidal Assemblies

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About This Repository

This repository features the CS-DART (Compressed Sensing Discrete Algebraic Reconstruction Technique) algorithm and associated post-processing codes. These are central to the research presented in our submission to Nature Communications. The details are as follows:

@article{arenas2023liquid,
  title={Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies},
  author={Arenas Esteban, Daniel and Wang, Da and Kadu, Ajinkya and Olluyn, Noa and S{\'a}nchez Iglesias, Ana and Gomez Perez, Alejandro and Gonzalez Casablanca, Jesus and Nicolopoulos, Stavros and Liz-Marz{\'a}n, Luis M and Bals, Sara},
  journal={arXiv e-prints},
  pages={arXiv--2311},
  year={2023}
}

CS-DART combines compressed sensing with discrete tomographic principles to improve the 3D reconstruction of colloidal particles in liquid environment under HAADF-STEM imaging, despite the high electron dose sensitivity and significant missing wedge issues faced due to liquid-cell holder.

Highlights

Feature	Description
Advanced 3D Reconstruction	Enhances the resolution and detail of 3D structures in liquid conditions.
Complex Structures	Reveals intricate geometries such as tetrahedrals and polyhedra for particle counts N = 4, 5, 6.
Quantitative Analysis	Employs alpha shapes for detailed morphological and spatial metrics.
Experimental Validation	Proven against both synthetic and experimental datasets in demanding liquid environments.

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System Requirements

Software Dependencies

• MATLAB: Tested with MATLAB R2023a.
• Operating Systems: Windows 10.

MATLAB Toolboxes Required

• Image Processing Toolbox
• Statistics and Machine Learning Toolbox
• Computer Vision Toolbox (optional for some functions)

Hardware Requirements

Component	Specification
RAM	32 GB Minimum
CPU	Intel(R) Core(TM) i7-8700 @ 3.20GHz or equivalent
GPU	Nvidia RTX 2070, 8 GB

External Dependencies

• ASTRA-Toolbox v2.1.0: A framework for tomographic operators. More Info
• SPOT: Simplifies modeling of linear operators in tomography. More Info
• MinConf Optimization Package: Optimizes objectives including CS-DART. More Info

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Installation Guide

Getting Started

1. Clone the Repository

   git clone https://github.com/ajinkyakadu/LiquidET_NatComm2024.git
   cd LiquidET_NatComm2024

2. Set Up MATLAB Environment

   run('setup.m');

Installation Time

• Typical setup time is under 1 minute on a standard desktop.

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Quick Start Guide

1. Prepare the Environment

   cd examples

2. Run Reconstruction Script

   ex01_step01_N4Liquid

3. Execute Post-Processing

   ex01_step02_N4Liquid

Expected Outputs

• Reconstructed Volume: Saved as csdart_reconstructed_volume.rec in the data folder.
• Quantitative Descriptors: Stored as quant_descriptors_NP.mat in the data folder.

Expected Demo Time

• Approximately 30 minutes on a standard desktop.

Data Access

Access the dataset for different colloidal systems (N = 4, 5, 6) at Zenodo. Zenodo DOI: 10.5281/zenodo.11175299

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Usage

To adapt the software for your data:

1. Data Preparation Ensure your data is formatted correctly and placed in the data directory.

2. Script Adjustments

   • Modify ex01_step01_N4Liquid.m to reference your dataset and adjust parameters for CS-DART reconstruction scheme.
   • Adjust parameters like cropRadius and minArea in ex01_step02_N4Liquid.m to fit your data.

3. Execute the Analysis Follow the demo steps with your data specifics.

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Explore. Reconstruct. Analyze.