Basic principles of magnetic resonance imaging for beginner oral and maxillofacial radiologists
Autor: | Tomoko Shiraishi, Daisuke Inadomi, Kenji Yuasa, Kunihiro Miwa, Shoko Yoshida, Mamoru Sato, Toyohiro Kagawa, Marie Hashimoto, Takashi Tsuzuki |
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Rok vydání: | 2017 |
Předmět: |
medicine.medical_specialty
medicine.diagnostic_test business.industry Contrast effect media_common.quotation_subject Gadolinium chemistry.chemical_element Magnetic resonance imaging 030206 dentistry Real-time MRI Signal 030218 nuclear medicine & medical imaging Intensity (physics) 03 medical and health sciences 0302 clinical medicine chemistry Contrast (vision) Medicine Radiology Nuclear Medicine and imaging Dentistry (miscellaneous) Radiology Medical diagnosis business media_common Biomedical engineering |
Zdroj: | Oral Radiology. 33:92-100 |
ISSN: | 1613-9674 0911-6028 |
DOI: | 10.1007/s11282-017-0274-z |
Popis: | The basic principles and diagnostic methods of magnetic resonance imaging (MRI) for beginning surgeons are described in this review. MRI is an important technique that is essential for diagnoses in the maxillofacial area. It is a scanning method that obtains tomographic images of the human body using a magnetic field. In contrast to computed tomography, it does not utilize X-rays and, therefore, represents a noninvasive test that lacks radiation exposure. It is particularly effective for soft-tissue diagnoses. MRI involves imaging protons in vivo. Protons emit a signal when a radio frequency pulse is applied in a magnetic field; the MRI device then forms an image from these signals. The basic images produced are T1- and T2-weighted images; comparison of these images is the first step of MRI-based diagnosis. Short-T1 inversion recovery images, which eliminate the signal from fat, are also useful for diagnosis. Gadolinium is used as a contrast agent for MRI. Taking sequential images at fixed intervals while injecting the contrast agent and then graphing the contrast effect along the time axis produces a time–signal intensity curve, which is useful for identifying features such as malignant neoplasms based on the graph pattern. |
Databáze: | OpenAIRE |
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