BACKGROUND: There are many different white matter disorders, both inherited and acquired, and consequently the diagnostic process is difficult. Establishing a specific diagnosis is often delayed at great emotional and financial costs. The pattern of brain structures involved, as visualized by MRI, has proven to often have a high diagnostic specificity. METHODS: We developed a comprehensive practical algorithm that relies mainly on the characteristics of brain MRI. RESULTS: The initial decision point defines a hypomyelination pattern, in which the cerebral white matter is hyperintense (normal), isointense, or slightly hypointense relative to the cortex on T1-weighted images, vs other pathologies with more prominent hypointensity of the cerebral white matter on T1-weighted images. In all types of pathology, the affected white matter is hyperintense on T2-weighted images, but, as a rule, the T2 hyperintensity is less marked in hypomyelination than in other pathologies. Some hypomyelinating disorders are typically associated with peripheral nerve involvement, while others are not. Lesions in patients with pathologies other than hypomyelination can be either confluent or isolated and multifocal. Among the diseases with confluent lesions, the distribution of the abnormalities is of high diagnostic value. Additional MRI features, such as white matter rarefaction, the presence of cysts, contrast enhancement, and the presence of calcifications, further narrow the diagnostic possibilities. CONCLUSION: Application of a systematic decision tree in MRI of white matter disorders facilitates the diagnosis of specific etiologic entities.
BACKGROUND: There are many different white matter disorders, both inherited and acquired, and consequently the diagnostic process is difficult. Establishing a specific diagnosis is often delayed at great emotional and financial costs. The pattern of brain structures involved, as visualized by MRI, has proven to often have a high diagnostic specificity. METHODS: We developed a comprehensive practical algorithm that relies mainly on the characteristics of brain MRI. RESULTS: The initial decision point defines a hypomyelination pattern, in which the cerebral white matter is hyperintense (normal), isointense, or slightly hypointense relative to the cortex on T1-weighted images, vs other pathologies with more prominent hypointensity of the cerebral white matter on T1-weighted images. In all types of pathology, the affected white matter is hyperintense on T2-weighted images, but, as a rule, the T2 hyperintensity is less marked in hypomyelination than in other pathologies. Some hypomyelinating disorders are typically associated with peripheral nerve involvement, while others are not. Lesions in patients with pathologies other than hypomyelination can be either confluent or isolated and multifocal. Among the diseases with confluent lesions, the distribution of the abnormalities is of high diagnostic value. Additional MRI features, such as white matter rarefaction, the presence of cysts, contrast enhancement, and the presence of calcifications, further narrow the diagnostic possibilities. CONCLUSION: Application of a systematic decision tree in MRI of white matter disorders facilitates the diagnosis of specific etiologic entities.
Authors: Catherine Vaurs-Barriere; Kondi Wong; Thais D Weibel; Mones Abu-Asab; Michael D Weiss; Christine R Kaneski; Tong-Hui Mixon; Simona Bonavita; Isabelle Creveaux; John D Heiss; Maria Tsokos; Ehud Goldin; Richard H Quarles; Odile Boespflug-Tanguy; Raphael Schiffmann Journal: Ann Neurol Date: 2003-12 Impact factor: 10.422
Authors: M S van der Knaap; P G Barth; H Stroink; O van Nieuwenhuizen; W F Arts; F Hoogenraad; J Valk Journal: Ann Neurol Date: 1995-03 Impact factor: 10.422
Authors: R Schiffmann; J R Moller; B D Trapp; H H Shih; R G Farrer; D A Katz; J R Alger; C C Parker; P E Hauer; C R Kaneski Journal: Ann Neurol Date: 1994-03 Impact factor: 10.422
Authors: Marjo S van der Knaap; Gerre Vermeulen; Frederik Barkhof; Augustinus A M Hart; J Gerard Loeber; Jan F L Weel Journal: Radiology Date: 2004-02 Impact factor: 11.105
Authors: M T Dotti; A Federico; E Signorini; N Caputo; C Venturi; G Filosomi; G C Guazzi Journal: AJNR Am J Neuroradiol Date: 1994-10 Impact factor: 3.825
Authors: M S van der Knaap; J Valk; C J Bakker; M Schooneveld; J A Faber; J Willemse; R H Gooskens Journal: Dev Med Child Neurol Date: 1991-10 Impact factor: 5.449
Authors: Marjan E Steenweg; Adeline Vanderver; Berten Ceulemans; Prab Prabhakar; Luc Régal; Aviva Fattal-Valevski; Lawrence Richer; Barbara Goeggel Simonetti; Frederik Barkhof; Richard J T Rodenburg; Petra J W Pouwels; Marjo S van der Knaap Journal: Arch Neurol Date: 2012-06
Authors: Cameron J Brimley; Jonathan Lopez; Keith van Haren; Jacob Wilkes; Xiaoming Sheng; Clint Nelson; E Kent Korgenski; Rajendu Srivastava; Joshua L Bonkowsky Journal: Pediatr Neurol Date: 2013-09 Impact factor: 3.372
Authors: Violeta Chitu; Solen Gokhan; Maria Gulinello; Craig A Branch; Madhuvati Patil; Ranu Basu; Corrina Stoddart; Mark F Mehler; E Richard Stanley Journal: Neurobiol Dis Date: 2014-12-09 Impact factor: 5.996
Authors: Christina Sundal; Jay A Van Gerpen; Alexandra M Nicholson; Christian Wider; Elizabeth A Shuster; Jan Aasly; Salvatore Spina; Bernardino Ghetti; Sigrun Roeber; James Garbern; Anne Borjesson-Hanson; Alex Tselis; Russell H Swerdlow; Bradley B Miller; Shinsuke Fujioka; Michael G Heckman; Ryan J Uitti; Keith A Josephs; Matt Baker; Oluf Andersen; Rosa Rademakers; Dennis W Dickson; Daniel Broderick; Zbigniew K Wszolek Journal: Neurology Date: 2012-07-25 Impact factor: 9.910
Authors: Clint Nelson; Michael B Mundorff; E Kent Korgenski; Cameron J Brimley; Rajendu Srivastava; Joshua L Bonkowsky Journal: J Pediatr Date: 2012-10-13 Impact factor: 4.406