EVALUATION OF LOW-DOSE TO HIGH-DOSE CT IMAGES USING AI AND DEEP LEARNING
DOI:
https://doi.org/10.29121/granthaalayah.v13.i5.2025.6203Keywords:
Radiation Dose, Image Quality, Automatic Exposure Control, Contrast Agents, Dose Modulation, Phantom StudiesAbstract [English]
In clinical practice, Computed Tomography (CT) is indispensable for medical imaging. CT can deliver patient depicts in various dimensions. Low-dose CT occasionally produces impression with lesser resolution than standard CT, in spite of the reality that it may reduce the radiation hazards associated with CT scanning. In CT scanning, reducing the X-ray exposure the dosage can contribute to a significant deterioration in the clarity of the image, increasing the possibility of misinterpretation and missing diagnosis. The area of CT has repeatedly encountered substantial mathematical challenges, including reducing the radiation dose and developing images of outstanding quality to meet therapeutic diagnostic requirements. This paper discusses major objectives is to validate a reinforcement CT image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising conventional CT image reconstruction approach with a benchmark outline, along with each one's benefits and drawbacks. A comprehensive description using artificial intelligence and machine learning applied to the Low Dose CT imaging process has been put forward. Furthermore, an experimental analysis utilizing the comparative result with an existing protocol has been assessed and evaluated based on the performance metrics using suitable simulators.
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