BRIEF REPORT |
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Year : 2010 | Volume
: 58
| Issue : 1 | Page : 90--94 |
Ischemic hyperintensities on T1-weighted magnetic resonance imaging of patients with stroke: New insights from susceptibility weighted imaging
Neeraj N Baheti1, Ajith Cherian1, PR Wattamwar1, Chandrasekharan Kesavadas2, Bejoy Thomas2
1 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum - 695 011, India 2 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum - 695 011, India
Correspondence Address:
Chandrasekharan Kesavadas Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum - 695 011 India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0028-3886.60408
Hyperintensities on T1-weighted magnetic resonance imaging (MRI) in the setting of brain ischemia are usually considered hemorrhagic transformations. Such changes can also be seen due to "incomplete infarction" with selective neuronal loss. Arguments regarding the cause of these T1 hyperintensities have shuttled between gemistocytic astrocyte accumulation, tissue calcification and paramagnetic substance deposition. Susceptibility weighted imaging (SWI), a sensitive modality for detecting paramagnetic agents and blood products, has never been used to resolve this issue. The study was aimed to evaluate the SWI signal changes of T1 hyperintense lesion in stroke patients and understand its usefulness in differentiating a hemorrhagic infarct and an incomplete infarct. All the seven patients with infarct, having hyperintensities on T1 weighted MRI seen over the last one year were subjected to SWI. In none of the patients SWI failed to show any blooming. By doing SWI for T1-weighted hyperintensities, we can differentiate hemorrhagic infarct and a non-hemorrhagic "incomplete infarct". This differentiation will immensely help in planning management strategy and prognostication.
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