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MRI in central nervous system infections: A simplified patterned approach
Author(s) -
Krithika Rangarajan,
Chandan Jyoti Das,
Atin Kumar,
Arun Kumar Gupta
Publication year - 2014
Publication title -
world journal of radiology
Language(s) - Uncategorized
Resource type - Journals
ISSN - 1949-8470
DOI - 10.4329/wjr.v6.i9.716
Subject(s) - medicine , magnetic resonance imaging , medical physics , diffusion imaging , in vivo magnetic resonance spectroscopy , computer science , neuroscience , radiology , artificial intelligence , diffusion mri , psychology
Recognition and characterization of central nervous system infections poses a formidable challenge to the neuro-radiologist. Imaging plays a vital role, the lesions typically being relatively inaccessible to tisue sampling. The results of an accurate diagnosis are endlessly rewarding, given the availability of excellent pharmacological regimen. The availability of numerous magnetic resonance (MR) sequences which provide functional and molecular information is a powerful tool in the hands of the radiologist. However, the plethora of sequences and the possibilities on each sequence is also intimidating, and often confusing as well as time consuming. While a large number of reviews have already described in detail the possible imaging findings in each infection, we intend to classify infections based on their imaging characteristics. In this review we describe an algorithm for first classifying the imaging findings into patterns based on basic MR sequences (T1, T2 and enhancement pattern with Gadolinium), and then sub-classify them based on more advanced molecular and functional sequences (Diffusion, Perfusion, Susceptibility imaging, MR Spectroscopy). This patterned approach is intended as a guide to radiologists in-training and in-practice for quickly narrowing their list of differentials when faced with a clinical challenge. The entire content of the article has also been summarised in the form of flow-charts for the purpose of quick reference.

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