SUMMARY: Susceptibility-weighted imaging (SWI) is a new neuroimaging technique, which uses tissue magnetic susceptibility differences to generate a unique contrast, different from that of spin density, T1, T2, and T2*. In this review (the first of 2 parts), we present the technical background for SWI. We discuss the concept of gradient-echo images and how we can measure local changes in susceptibility. Armed with this material, we introduce the steps required to transform the original magnitude and phase images into SWI data. The use of SWI filtered phase as a means to visualize and potentially quantify iron in the brain is presented. Advice for the correct interpretation of SWI data is discussed, and a set of recommended sequence parameters for different field strengths is given.

SUMMARY: Susceptibility-weighted imaging (SWI) has continued to develop into a powerful clinical tool to visualize venous structures and iron in the brain and to study diverse pathologic conditions. SWI offers a unique contrast, different from spin attenuation, T1, T2, and T2* (see Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1). In this clinical review (Part 2), we present a variety of neurovascular and neurodegenerative disease applications for SWI, covering trauma, stroke, cerebral amyloid angiopathy, venous anomalies, multiple sclerosis, and tumors. We conclude that SWI often offers complementary information valuable in the diagnosis and potential treatment of patients with neurologic disorders.

Susceptibility‐weighted imaging (SWI) consists of using both magnitude and phase images from a high‐resolution, three‐dimensional, fully velocity compensated gradient‐echo sequence. Postprocessing is applied to the magnitude image by means of a phase mask to increase the conspicuity of the veins and other sources of susceptibility effects. This article gives a background of the SWI technique and describes its role in clinical neuroimaging. SWI is currently being tested in a number of centers worldwide as an emerging technique to improve the diagnosis of neurological trauma, brain neoplasms, and neurovascular diseases because of its ability to reveal vascular abnormalities and microbleeds. J. Magn. Reson. Imaging 2005.

Purpose: To create an orientation‐independent, 3D reconstruction of the veins in the brain using susceptibility mapping.Materials and Methods: High‐resolution, high‐pass filtered phase images usually used for susceptibility weighted imaging (SWI) were used as a source for local magnetic field behavior. These images were subsequently postprocessed using an inverse procedure to generate susceptibility maps of the veins. Regularization and interpolation of the data in k‐space of the phase images were used to reduce reconstruction artifacts. To understand the effects of artifacts, and to fine‐tune the methodology, simulations of blood vessels were performed with and without noise.Results: With sufficient resolution, major veins in the brain could be visualized with this approach. The usual geometry‐dependent phase dipole effects are removed by this processing, leaving basically images of the veins. Different sized vessels show a different level of contrast depending on their partial volume effects. Vessels that are 8 mm or 16 mm in size show quantitative values expected for normal oxygen saturation levels. Smaller vessels show smaller values due to errors in the methodology and due to partial volume effects. Larger vessels show a bias toward a reduced susceptibility approaching 90% of the expected value. Limitations of the method and artifacts related to different sources of errors are demonstrated.Conclusion: Susceptibility maps can successfully create venograms of the brain with varying levels of contrast‐to‐noise depending on the size of the vessel. Partial volume effects render this approach more useful as an imaging tool or a visualization tool, although certain larger vessels have measured susceptibilities close to expected values associated with normal blood oxygen saturation levels. J. Magn. Reson. Imaging 2010;32:663–676.