New MR sequences in daily practice: susceptibility weighted imaging. A pictorial essay

Background Susceptibility-weighted imaging (SWI) is a relatively new magnetic resonance (MR) technique that exploits the magnetic susceptibility differences of various tissues, such as blood, iron and calcification, as a new source of contrast enhancement. This pictorial review is aimed at illustrating and discussing its main clinical applications. Methods SWI is based on high-resolution, threedimensional (3D), fully velocity-compensated gradientecho sequences using both magnitude and phase images. A phase mask obtained from the MR phase images is multiplied with magnitude images in order to increase the visualisation of the smaller veins and other sources of susceptibility effects, which are displayed at best after postprocessing of the 3D dataset with the minimal intensity projection (minIP) algorithm. Results SWI is very useful in detecting cerebral microbleeds in ageing and occult low-flow vascular malformations, in characterising brain tumours and degenerative diseases of the brain, and in recognizing calcifications in various pathological conditions. The phase images are especially useful in differentiating between paramagnetic susceptibility effects of blood and diamagnetic effects of calcium. SWI can also be used to evaluate changes in iron content in different neurodegenerative disorders. Conclusion SWI is useful in differentiating and characterising diverse brain disorders

Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images

Background: Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction.Methodology/Principal Findings: Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (>= 2) lobar microbleeds.Conclusions/Significance: MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds