This repository encompasses the results of practical activities related to Magnetic Resonance Imaging (MRI) processing and an introduction to quantitative MRI (qMRI). It includes examples of DICOM image processing, manipulation of k-space, experimentation with quantitative MRI data, and analysis of noisy data. These materials aim to provide a comprehensive understanding of MRI image processing techniques and the fundamentals of qMRI.

1. Introduction - An overview of MRI principles and the scope of projects within this repository.
2. Playing with the k-Space
   • DICOM Processing - Techniques for handling DICOM images, including loading, visualization, and basic processing.
   • Basic k-Space Processing - Introduction to the concept of k-space and its significance in MRI image reconstruction.
   • Manipulating k-Space Shape - Advanced techniques for altering k-space to affect image quality and resolution.
   • Panic at the MRI Machine - Handling common issues and errors in MRI data acquisition and processing.
3. First Step in the Quantitative MRI World
   • Let's Start with Shepp-Logan Phantom - Utilizing the Shepp-Logan phantom for simulation and understanding basic MRI properties.
   • qMRI Approach - Introduction to quantitative MRI techniques and their applications in medical diagnosis and research.
   • Work with Noisy Data - Strategies for processing and analyzing MRI data in the presence of noise.

• To introduce the basic and advanced concepts of MRI and qMRI.
• To provide hands-on examples and tutorials on MRI image processing.
• To explore the quantitative aspects of MRI and their implications in medical research.

This repository is structured to facilitate learning through practical examples and exercises. Each section not only includes detailed explanations and introductions to the topics but also features a comprehensive Jupyter Notebook MRI_trabajo.ipynb with solved exercises. For an in-depth understanding of the results and methodologies employed, refer to the report MRI_report Sevillano.pdf, both of which are authored by me, ensuring authenticity and personal insight into the field of MRI image processing and qMRI.

Included in this repository are:

• Code examples in Python for processing and analyzing MRI data.
• The MRI_trabajo.ipynb notebook containing all exercises and solutions.
• A detailed report MRI_report Sevillano.pdf elaborating on the exercises' outcomes and techniques used.
• Datasets and images to practice the demonstrated techniques.

All files necessary for the exercises, including datasets and images, are available within this repository, aiming to provide a comprehensive view on handling and understanding MRI data. The code and report are personally authored, reflecting my approach to exploring the capabilities and applications of MRI and qMRI in medical research.