Image Processing Techniques for Diffusing Tensor Magnetic Resonance Imaging using Deep Learning
Pradeep Verma, Nitya Sri Nellore, Manoj Reddy Kichaiah Gari, Sairohith Thummarakoti, P. William, Lingarasu Kittusamy
Abstract
Evaluating water diffusion in brain tissues as well as white matter connections and structure are among the most significant applications of diffusion tensor imaging (DTI). Magnetic resonance imaging (MRI) uses this idea. DTI image processing systems struggle with accuracy and efficiency since they rely on human inputs and antiquated technology. A new deep learning method called convolutional neural networks (CNNs) has the potential to enhance and automate DTI data processing. CNNs make this feasible. This book on DTI includes a thorough analysis of deep learning applications. This covers image reconstruction, noise reduction, and scene segmentation. In terms of picture quality, diffusion measurement accuracy, and white matter tract identification, this study demonstrates how convolutional neural networks (CNNs) perform better than conventional techniques. We specifically look into how CNNs perform better than traditional techniques. Our research demonstrates that deep learning enhances DTI analysis for neurological diagnosis and research. This results in tools that are more precise and efficient.