An Algorithmic Approach to MR Imaging of Hypomyelinating Leukodystrophies.

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Tác giả: Chandrasekharan Kesavadas, Smily Sharma, Soumya Sundaram, Bejoy Thomas

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: United States : Journal of magnetic resonance imaging : JMRI , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 688024

 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.
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