**8.4 Magnetic resonance imaging**

Magnetic resonance imaging (MRI) is a technique that uses high-intensity electromagnetic fields and radiofrequency waves to detect changes in the rotational axis of protons, mostly in water molecules. Water makes up almost all the tissues in the body and the difference in the percentage of water influences the axis changes. Deep learning applications in the field of MRI can be grouped into two broad categories. The first is related to the physical aspects and the generation of images on the device. In this category, you can find works that focus on image restoration, image reconstruction, and multimodal image registration [71]. The second category emphasizes applications for medical purposes, in which the determination of pathology or its progress is the main purpose [71–74]. Focusing on the second category, we find works on brain aging [75], brain vascular lesions [76], Alzheimer's disease [77], multiple sclerosis [78], glioma [79], and meningioma [80]. In the abdominal cavity, we find works of identification and segmentation of organs [81], polycystic kidneys [82], and renal transplantation [83]. Finally, isolating the spine as the focus of the study, we found works on labeling and separation of vertebrae [84], spinal stenosis grading [85], and identification and segmentation of spinal metastasis [86]. It is important to mention that organ segmentation is a very important focus in deep learning applications for MRI images. With the definition of organ contours in each plane (slice), the determination of the organ coordinates and the addition of consecutive areas, volumes can be calculated. The calculation of volumes is of crucial importance since they can be used to determine the dilation of organs (e.g., splenomegaly). The measurement of dilation is not only an important initial measurement. Thanks to the volumetric determination, it is possible to follow up on patients to observe the efficiency of treatments [81].
