Abstract
Background: Sarcoidosis is an unknown etiology multisystem inflammatory disease in which noncaseating granulomas (a collections of inflammatory cells) form and grow in various organs, involving predominantly lungs, intrathoracic lymph node, skin and eyes. It most commonly affects patients between 20 and 40 years old of age but it could be observed at any age (female predominance; rare in Asians).
Discussion: The areas of the body usually affected by sarcoidosis are lungs, skin, or lymph nodes; pulmonary and mediastinal involvement is seen in over of 90% of patients. Less commonly eyes, liver, heart, and brain are involved. Any organ, however, can be affected.
Early diagnosis of sarcoidosis can be difficult due to few signs and symptoms in its early stages, and when disease does occur, it may mimic other pathologies, and is made up with chest X-ray, Computed Tomography (CT)-High Resolution CT (HRCT), gallium scans. Fluoro-Deoxy Glucose– Positron Emission Tomography (FDG-PET) is another useful tool to assess the extent of disease and has a potential to evaluate the clinical management of patients responding or not to the treatment.
Conclusion: In this review, we would summarize in brief the clinical indications of PDG-PET in sarcoidosis and report the imaging features of the main organs involved in this disease.
Keywords: FDG-PET, sarcoidosis, angiotensin converting enzyme, CT, inflammatory disease, lymph node.
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
41
4