MRI analysis is a research area that focuses on developing and implementing algorithms and methods to analyze magnetic resonance imaging (MRI) data. MRI is a non-invasive imaging technique that uses strong magnetic fields and radio waves to create detailed images of the internal structures of the human body. In MRI analysis, researchers aim to extract useful information from the complex and multi-dimensional MRI data, such as identifying specific tissues or structures, detecting abnormalities or diseases, tracking changes over time, and quantifying various parameters (e.g., volume, density, connectivity). Some common tasks in MRI analysis include image pre-processing (e.g., noise reduction, motion correction), segmentation (i.e., dividing the image into different regions or structures), registration (i.e., aligning images from different time points or modalities), and statistical analysis (e.g., comparing groups or correlating with clinical data). MRI analysis plays a crucial role in various fields, such as neuroscience (e.g., studying brain structure and function), oncology (e.g., tumor detection and monitoring), cardiology (e.g., assessing heart function), and musculoskeletal imaging (e.g., evaluating joint injuries). It provides valuable insights that can improve diagnosis, treatment planning, and understanding of disease mechanisms.