Electrical Impedance Tomography Image Reconstruction

Duration: August 2023 – January 2024
Type: Independent Study

This project explored the application of variational autoencoders (VAEs) to tackle the ill-posed inverse problem in Electrical Impedance Tomography (EIT) image reconstruction. The primary goal was to achieve a 5% improvement in image quality compared to state-of-the-art models.

Key Achievements:

Implemented novel VAE architectures specifically designed for EIT reconstruction
Developed advanced regularization techniques to handle the ill-posed nature of the problem
Achieved improved reconstruction quality through learned latent representations
Compared performance against existing reconstruction methods

Technical Details:

EIT is a medical imaging technique that reconstructs the internal conductivity distribution of an object from electrical measurements taken at its boundary. The main challenge lies in the ill-posed nature of the inverse problem, where small measurement errors can lead to significant reconstruction artifacts.

Our approach leveraged the power of variational autoencoders to learn meaningful representations of conductivity distributions, enabling more robust and accurate reconstructions even in the presence of measurement noise.

Technologies Used:

Python
TensorFlow/PyTorch
MATLAB
Advanced optimization algorithms
Medical imaging processing tools img: /assets/img/12.jpg —

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