MCA dot sign (brain)
Citation, DOI & article data
The middle cerebral artery (MCA) dot sign, also known as the Sylvian fissure sign, is seen on non-contrast brain CT and represents the cross-sectional M2 equivalent of the hyperdense MCA sign. Rather than imaging a length of middle cerebral artery (typically the M1 segment), the dot sign represents a thromboembolism within a segmental branch of the MCA located within the Sylvian fissure (M2 segment). The sign appears when the high-attenuation MCA is viewed in axial section, since the occluded vessel courses in a plane perpendicular to the transverse plane of imaging.
The MCA dot sign is an early marker of thromboembolic occlusion of the distal MCA branches seen in the Sylvian fissure (M2 segment). The principally affected area of the brain is the insula.
Sensitivity and specificity
The sensitivity of this CT sign is approximately 35%, while its specificity may be as high as 100% 3.
Treatment and prognosis
The MCA dot sign is prognostic of poor outcome in patients with ischemic stroke, but with a better outcome than the hyperdense MCA sign 2.
As such, single studies have assessed the possibility of automated detection 4 and its value in prediction of secondary symptomatic intracranial hemorrhage 5.
Punctate vascular calcification along the M2 segment of the MCA within the Sylvian fissure represents a potential mimic of this sign.
- 1. Shetty SK. The MCA Dot Sign. Radiology. 2006;241 (1): 315-8. doi:10.1148/radiol.2411040573 - Pubmed citation
- 2. Barber PA, Demchuk AM, Hudon ME et-al. Hyperdense sylvian fissure MCA "dot" sign: A CT marker of acute ischemia. Stroke. 2001;32 (1): 84-8. Pubmed citation
- 3. Leary MC, Kidwell CS, Villablanca JP et-al. Validation of computed tomographic middle cerebral artery "dot"sign: an angiographic correlation study. Stroke. 2003;34 (11): 2636-40. doi:10.1161/01.STR.0000092123.00938.83 - Pubmed citation
- 4. Takahashi N, Lee Y, Tsai DY et-al. An automated detection method for the MCA dot sign of acute stroke in unenhanced CT. Radiol Phys Technol. 2014;7 (1): 79-88. doi:10.1007/s12194-013-0234-1 - Pubmed citation
- 5. Bentley P, Ganesalingam J, Carlton Jones AL et-al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin. 2014;4: 635-40. doi:10.1016/j.nicl.2014.02.003 - Free text at pubmed - Pubmed citation