Problem: in recent years, new studies have been published on biological effects of strong static magnetic fields and on thermal effects of high-frequency electromagnetic fields as used in magnetic resonance imaging (MRI). Many of these studies have not yet been incorporated into current safety recommendations. Method: scientific publications from 2010 onwards on the biological effects of static and electromagnetic fields of MRI were searched and evaluated. Results: new studies confirm older work that has already described effects of static magnetic fields on sensory organs and the central nervous system accompanied by sensory perception. A new result is the direct effect of Lorentz forces on ionic currents in the semicircular canals of the vestibular organ. Recent studies on thermal effects of radiofrequency fields focused on the development of anatomically realistic body models and more accurate simulation of exposure scenarios. Recommendation for practice: strong static magnetic fields can cause unpleasant perceptions, especially dizziness. In addition, they can impair the performance of the medical personnel and thus potentially endanger patient safety. As a precaution, medical personnel should move slowly in the field gradient. High-frequency electromagnetic fields cause tissues and organs to heat up in patients. This must be taken into account in particular for patients with impaired thermoregulation as well as for pregnant women and newborns; exposure in these cases must be kept as low as possible.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Amyloidosis is a systemic disorder produced by the deposition of insoluble protein fibrils that fold and deposit in the myocardium. Patients with amyloidosis and cardiac involvement have higher mortality than patients without cardiac involvement. The two most prevalent forms of amyloidosis associated with cardiac involvement are AL amyloidosis, due to the deposition of immunoglobulin light chains, and ATTR amyloidosis, due to the deposition of the transthyretin (TTR) protein in mutated or senile form. This article aims to review the different cardiac imaging modalities (echocardiography, cardiac magnetic resonance imaging, nuclear medicine and tomography) that allow to determine the severity of cardiac involvement in patients with amyloidosis, the type of amyloidosis and its prognosis. Finally, we suggest a diagnostic algorithm to determine cardiac involvement in amyloidosis adapted to locally available diagnostic tools, with a practical and clinical approach.

Clinical/methodological problem: The identification of clinically significant prostate carcinomas while avoiding overdiagnosis of low-malignant tumors is a challenge in routine clinical practice. Standard radiologic procedures: Multiparametric magnetic resonance imaging (MRI) of the prostate acquired and interpreted according to PI-RADS (Prostate Imaging Reporting and Data System Guidelines) is accepted as a clinical standard among urologists and radiologists. Methodological innovations: The PI-RADS guidelines have been newly updated to version 2.1 and, in addition to more precise technical requirements, include individual changes in lesion assessment. Performance: The PI-RADS guidelines have become crucial in the standardization of multiparametric MRI of the prostate and provide templates for structured reporting, facilitating communication with the referring physician. Evaluation: The guidelines, now updated to version 2.1, represent a refinement of the widely used version 2.0. Many aspects of reporting have been clarified, but some previously known limitations remain and require further improvement of the guidelines in future versions.

The integration of medical images is the process of registering and fusing them to obtain a greater amount of diagnostic information. In this work an analysis is performed for the integration of images obtained through computed axial tomography and magnetic resonance imaging, for which a tool was developed in the Matlab program, where the registration is implemented through equivalent features; in addition, the pairs of images are compared by several fusion rules, with a view to identify the best algorithm in which the resulting fused image contains the most information from the original representations.

A systemic and synthetic review of the anatomy of the temporomandibular joint in magnetic resonance imaging was developed for its evaluation. The temporomandibular joint is an anatomical structure composed of bones, muscles, ligaments and an articular disc that allows important physiological movements, such as mandibular opening, closing, protrusion, retrusion and lateralization. Magnetic resonance imaging is an imaging technique that does not use ionizing radiation and is more specific for the evaluation and interpretation of soft tissues, due to its high resolution, so it has an important role in the diagnosis of various maxillofacial pathologies, which is why the dentist should have knowledge of the structures and functions of the temporomandibular joint through magnetic resonance imaging. The review demonstrates the importance of magnetic resonance imaging in the study of the anatomy of the temporomandibular joint, in addition to mentioning the advantages provided by this imaging technique such as its good detail of the soft tissues in its different sequences and the non-use of ionizing radiation to obtain its images.