Tomographic back-projection of either sparse or low-quality projection views, based on convolutional neural networks (CNN)

Payal JAIN

Diplomová práce

Abstract:

This thesis delves into the integration of classical and modern approaches in tomographic back-projection, with a particular emphasis on challenges presented by sparse or low-quality projection views. The research is dedicated to advancing reconstruction methods by combining Convolutional Neural Networks (CNNs) with classical algorithms, specifically the Feldkamp-Davis-Kress (FDK) and Simultaneous Iterative Reconstruction Technique (SIRT) algorithms implemented using the ASTRA Toolbox. Evaluation of the methodologies relies on key performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). The experimental results underscore the effectiveness of CNNs in overcoming challenges associated with sparse or low-quality projection views, providing valuable insights for the enhancement of tomographic reconstruction methodologies.

Abstract:

This thesis delves into the integration of classical and modern approaches in tomographic back-projection, with a particular emphasis on challenges presented by sparse or low-quality projection views. The research is dedicated to advancing reconstruction methods by combining Convolutional Neural Networks (CNNs) with classical algorithms, specifically the Feldkamp-Davis-Kress (FDK) and Simultaneous Iterative Reconstruction Technique (SIRT) algorithms implemented using the ASTRA Toolbox. Evaluation of the methodologies relies on key performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). The experimental results underscore the effectiveness of CNNs in overcoming challenges associated with sparse or low-quality projection views, providing valuable insights for the enhancement of tomographic reconstruction methodologies.

Keywords

Tomography, reconstruction, neural network, CNN, FDK, performance matrics, astra toolbox

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 9. 2. 2024

Obhajoba závěrečné práce

• Vedoucí: prof. Dr. Simon Zabler

JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH

Přírodovědecká fakulta

Magisterský studijní program / obor:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science