Machine Learning and Vision Transformer for CT Scanners' Calibration and Quality Assessment

Yıl 2024, Cilt: 8 Sayı: 2, 118 - 126, 22.12.2024

Khanh Man , Majeed Soufian , Amani Mansour Alsaeedi Jon Fulford Hairil Abdul Razak

Öz

In this study, we present the process and research for finding the best machine learning methodology and innovative
approach to evaluate the image quality in Computed Tomography (CT) scanners by predicting Signal-to-Noise Ratio (SNR) and
Contrast-to-Noise Ratio (CNR) from low-resolution CT images of a series of phantoms. Traditional methods of Image Quality
Assessment (IQA), reliant on subjective evaluation by radiologists, often suffer from variability and inefficiency. To address
these limitations, we explored both interpretable models like the Adaptive Neuro-Fuzzy Inference System (ANFIS) and other
advanced deep learning architectures. Initially, ANFIS combined with Gray Level Co-occurrence Matrix (GLCM) features
yielded suboptimal results, with an R-squared value of 0.634. Experimenting with various deep learning methodologies for
improving the performance, directed us to develop a hybrid model integrating DenseNet, Vision Transformers, and
reparameterization techniques, which showed that can achieve superior results with an R-squared value of 0.8892. This research
paper focuses on searching for the optimal machine learning model and lays the groundwork for an automated tool that can
optimize imaging protocols by providing a comprehensive quality assessment of CT images in CT calibration.

Anahtar Kelimeler

Machine learning, Deep learning, Vision Transformer, CT calibration

Kaynakça

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