THE MATERIAL DECOMPOSITION ANALYSIS BASED ON ML ALGORITHM USING PHOTON COUNTING CT

Abstract

This thesis presents a novel approach to enhancing material decomposition (MD) in medical imaging using the MARS CT scanner. Traditional MD techniques are plagued by issues like computational complexity, frequent re-calibration needs, and cross-talk between materials with similar densities, which complicate accurate classification and quantification. This study effectively addresses these challenges by developing and implementing the advanced ResUNet++ model. ResUNet++ leverages nested convolutional blocks, dense skip connections, and residual layers to maintain high image resolution and improve feature propagation and reuse, mitigating the vanishing gradient problem. Comprehensive testing on both standard and blind datasets demonstrated the model’s robustness, significantly reducing noise and cross-talk and leading to marked improvements in classification accuracy and quantification precision across elements like Gold (Au), Calcium (Ca), Gadolinium (Gd), Iodine (I), Lipid, and Water. Comparative analysis with the conventional MD method used in the MARS scanner highlights the superior performance of ResUNet++, eliminating the need for pre-calibration. The study concludes that ResUNet++ sets a new benchmark for MD accuracy, offering a robust solution for more reliable and efficient diagnostic procedures in medical imaging. Future work will focus on testing the model on biological data and training it to adapt to various protocols, ensuring broader applicability and enhanced performance in diverse medical imaging scenarios.