Improving susceptibility mapping using a threshold-based K-space/image domain iterative reconstruction approach.

To improve susceptibility quantification, a threshold-based k-space/image domain iterative approach that uses geometric information from the susceptibility map itself as a constraint to overcome the ill-posed nature of the inverse filter is introduced. Simulations were used to study the accuracy of the method and its robustness in the presence of noise. In vivo data were processed and analyzed using this method. Both simulations and in vivo results show that most streaking artifacts inside the susceptibility map caused by the ill-defined inverse filter were suppressed by the iterative approach. In simulated data, the bias toward lower mean susceptibility values inside vessels has been shown to decrease from around 10% to 2% when choosing an appropriate threshold value for the proposed iterative method. Typically, three iterations are sufficient for this approach to converge and this process takes less than 30 s to process a 512×512×256 dataset. This iterative method improves quantification of susceptibility inside vessels and reduces streaking artifacts throughout the brain for data collected from a single-orientation acquisition. This approach has been applied to vessels alone as well as to vessels and other structures with lower susceptibility to generate whole brain susceptibility maps with significantly reduced streaking artifacts.

Susceptibility mapping as a means to visualize veins and quantify oxygen saturation.

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.

Susceptibility-weighted imaging: technical aspects and clinical applications, part 2.

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: technical aspects and clinical applications, part 1.

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.

Imaging cerebral amyloid angiopathy with susceptibility-weighted imaging.

Gradient-echo (GE) imaging is recognized as a means to detect hemorrhagic changes in cerebral amyloid angiopathy (CAA). However, almost 25% of patients with CAA do not show microhemorrhages on T2* GE imaging. We applied a new imaging method, susceptibility weighted imaging (SWI), to evaluate the presence of microhemorrhages. In a suspected case of CAA, where cognitive effects are also present, we show that SWI is much more sensitive in detecting microhemorrhages than conventional methods.