3D Brain

3D brain imaging research focuses on developing advanced computational methods to analyze and interpret three-dimensional brain scans, primarily MRI and fMRI data, for improved diagnosis and understanding of neurological conditions. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), vision transformers (ViTs), variational autoencoders (VAEs), and diffusion models, to address challenges such as data scarcity, noise reduction, and accurate segmentation of brain structures. These techniques are applied to tasks like brain tumor segmentation, Alzheimer's disease diagnosis, and the synthesis of high-resolution brain images from lower-resolution data, ultimately aiming to enhance the accuracy and efficiency of clinical practice and neuroscience research. The resulting advancements hold significant potential for improving patient care and accelerating scientific discovery in neurology and related fields.