Abstract
Inborn errors of metabolism in children can be challenging to interpret because of the similarity of their appearances on imaging. There are important clues to the diagnosis based on clinical history, head circumference (e.g., macrocephaly), geographic distribution of lesions (e.g., subcortical vs deep white matter or frontal vs parietooccipital), and other imaging features (e.g., contrast enhancement, calcification, cysts, and cortical dysplasia). In this article, we present an algorithm-based approach to diagnosing pediatric metabolic disease with a discussion of key imaging features.
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