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UNIVERSITY OF BUCHAREST
FACULTY OF PHYSICS Guest
2024-11-22 2:27 |
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Conference: Bucharest University Faculty of Physics 2024 Meeting
Section: Biophysics; Medical Physics
Title:
Advanced MRI Applications in Clinical Diagnosis and Monitoring of Neurological Disorders
Authors:
Nicoleta CAZACU, Marilena DOBRESCU
Affiliation:
Smeeni Chronic Disease Hospital, Buzău, Romania
E-mail
s.nicoleta59@yahoo.ro
Keywords:
Magnetic Resonance Imaging, Neurological Disorders, MRI pulse sequences, MRI applications
Abstract:
Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that plays a prime role in various disease aspects, including diagnosis, prognosis, and treatment response assessment. Recent advancements in MRI technology, especially in brain imaging acquisition and post-processing, have substantially contributed to our comprehension of disease-specific pathogenetic mechanisms. MRI serves as a crucial technique in diagnosing and planning the treatment of neurological disorders by offering detailed and precise images of the brain and spinal cord. In MRI different sequences are used, each tailored to different diagnostic needs: T1-weighted images for anatomy and tissue characterization, T2-weighted images for detecting various types of pathology, including edema, inflammation, cysts, and certain types of tumors, fluid-attenuated inversion recovery (FLAIR) for enhancing the visibility of abnormalities by suppressing cerebrospinal fluid signal, susceptibility (SWI) or gradient echo imaging (GRE) for detecting small amounts of hemorrhage, blood products or calcium, diffusion-weighted imaging for diagnosing acute strokes and post-contrast imaging, typically T1-weighted with fat suppression, to highlight the integrity of the blood-brain barrier.
Neurological disorders such as multiple sclerosis (MS), Alzheimer's disease, Parkinson's disease, stroke, epilepsy, and brain tumors are just a few examples of neurological disorders that could be diagnosed and monitored using MRI.
Numerous research projects are underway to progress the development of algorithms and image analysis methods for more precise disease progression detection and prognosis.
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