Deep Learning for Diffusion Tensor Imaging Estimation

Deep Learning for Diffusion Tensor Imaging Estimation: Unlocking Sparse Diffusion MRI Mental health and neurological disorders are on the rise globally, yet timely diagnosis remains a challenge due to high costs, long scan durations, and limited imaging accessibility. This book presents a breakthrough in neuroimaging by harnessing cutting-edge deep learning technologies-Transformers and Convolutional Neural Networks-to revolutionize how sparse diffusion MRI data is processed and analyzed. Dr. Abhishek Tiwari introduces SwinDTI, an innovative framework that dramatically improves the estimation of diffusion tensor imaging (DTI) parameters using minimal data, significantly reducing scan times while preserving clinical accuracy. This pioneering work empowers clinicians and researchers to extract meaningful insights from limited imaging resources, making it a powerful tool for early diagnosis of neurodegenerative diseases such as Alzheimer's and Frontotemporal Dementia. Drawing on extensive experimentation with benchmark datasets like HCP, ADNI, NIFD, and MICCAI Quad22, this book offers a roadmap for future-ready neuroimaging solutions. It bridges the gap between artificial intelligence and healthcare, providing scalable, explainable, and efficient tools for modern medical practice. Whether you're a researcher, clinician, or technologist, this book offers deep insights into the synergy between neuroscience and AI-unlocking new possibilities in brain connectivity mapping and precision diagnostics. ""A transformative step toward democratizing advanced neuroimaging using deep learning.""