International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
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ARTIFICIAL INTELLIGENCE IN IMAGE PROCESSING FOR MEDICAL PHYSICS J. P. Pramod1, Sumaiyya Fatima2 & Baddula Gayathri Yadav3 1Asst Professor, Dept of Physics
Stanley College of Engineering and Technology for Women 2&3B.Tech Student, Dept of Computer Science and Engineering
Stanley College of Engineering and Technology for Women ---------------------------------------------------------------------------***---------------------------------------------------------------------Abstract: Artificial Intelligence (AI) is advancing the field of medical physics by delivering solutions that can be employed to maximize the quality of imaging in diagnostic imaging like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) with the weight on serving to doctors enhance diagnostic capabilities. Historically, medical imaging technologies have transformed and shaped how healthcare professionals do diagnostics and treat patients. From X-ray technology in the past times to cutting-edge imaging technologies like MRI and CT, there remain innovations giving birth to new possibilities and challenges. Presently, artificial intelligence is ushering the next wave with sophisticated solutions to enhance images and diagnostic tools, redefining the boundaries of medical physics. This demonstrates how compared to conventional downstream methods in MRI and CT imaging, AI-based image processing techniques have changed the game by fixing low resolution, noise, and artifacts. Through a deeper analysis of specific AI characterizations such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), and a discussion of clinical applications, this research highlights these technologies aligning to meet an increasing demand for precision and accuracy in medical diagnostics.
Keywords: Artificial Intelligence (AI), Medical Physics, Diagnostic Imaging, Magnetic Resonance Imaging (MRI), Convolutional Neural Networks (CNNs).
Introduction: Medical imaging has revolutionized healthcare, providing medical professionals with incredible insights into the workings of the human body. These technologies were not available a few decades ago. Some of the major medical physics tools include Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), and they each have their benefits. For example, in the case of MRI, uses very strong magnetic fields and radio waves to image soft tissues of the human body at incredibly high spatial resolutions. As a result, MRI has been successfully applied to neurological, musculoskeletal, and cardiovascular imaging. MRI does this because of the alignment of hydrogen protons through strong magnetic fields. These protons get knocked out of this temporary comfortable state, and as they go back to their happy state they emit radio signals, which are collected and reversed into the image. The main obstacle for MRI is sensitivity to patient motion. This problem is due to the long scan time. Patients have to be completely motionless for 30-60 minutes. If the patient moves even 1mm during the scan, the scan will be blurry and the image will not be diagnostic. CT imaging provides crosssectional images of the body through the use of X-rays. It has an incredible bone contrast, so with CT scanning tumors can be detected inside liver tissue. CT is also used in neurological imaging in the context of stroke. However, a CT scan is limited by its use of ionizing radiation. Any amount of ionizing radiation comes with a cancer risk if you need to be repeated. Low-dose CT imaging has attempted to alleviate some of these issues by decreasing radiation exposure; however, this energy decrease increases noise and lowers image resolution, which makes it more difficult for small abnormalities to be detected. Moreover, both modalities have noise and low resolution, which results in the loss of key information. Artificial intelligence, with all its potential, might become a solution to some of these problems, especially in image enhancement and noise reduction. In the past decade, AI has become an intermingling force transformational in medical physics, totally changing the way imaging data is processed and analyzed. Highly constructed models like convolutional neural networks (CNNs) and generative adversarial networks (GANs) have long been invented with the aim of quality enhancement, motion artifact correction, and noise reduction. For example, CNNs can be designed to identify and delete noise from MRI scans, producing clearer, more accurate imaging. Contrast and artifact resolution were optimized using AI techniques in CT imaging, which enabled improved detection of pathologies and improved diagnostic outcomes.
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