An Algorithmic Approach to MR Imaging of Hypomyelinating Leukodystrophies.

Abstract

Hypomyelinating leukodystrophies (HLDs) are a heterogeneous group of white matter diseases characterized by permanent deficiency of myelin deposition in brain. MRI is instrumental in the diagnosis and recommending genetic analysis, and is especially useful as many patients have a considerable clinical overlap, with the primary presenting complains being global developmental delay with psychomotor regression. Hypomyelination is defined as deficient myelination on two successive MR scans, taken at least 6 months apart, one of which should have been obtained after 1 year of age. Due to subtle differences in MRI features, the need for a systematic imaging approach to diagnose and classify hypomyelinating disorders is reiterated. The presented article provides an explicit review of imaging features of a myriad of primary and secondary HLDs, using state of the art genetically proven MR cases. A systematic pattern-based approach using MR features and specific clinical clues is illustrated for a quick yet optimal diagnosis of common as well as rare hypomyelinating disorders. The major MR features helping to narrow the differential diagnosis include extent of involvement like diffuse or patchy hypomyelination with selective involvement or sparing of certain white matter structures like optic radiations, median lemniscus, posterior limb of internal capsule and periventricular white matter; cerebellar atrophy; brainstem, corpus callosal or basal ganglia involvement; T2 hypointense signal of the thalami; and presence of calcifications. The authors also discuss the genetic and pathophysiologic basis of HLDs and recent methods to quantify myelin in vivo using advanced neuroradiology tools. The proposed algorithmic approach provides an improved understanding of these rare yet important disorders, enhancing diagnostic precision and improving patient outcomes. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 5.