Detection of normal spinal veins by using susceptibility‐weighted imaging

Purpose:

To evaluate the visualization of the spinal veins using susceptibility‐weighted imaging (SWI).

Materials and Methods:

A 1.5‐T magnet equipped with a spine matrix coil was used. Axial SWI scans of 20 healthy volunteers were obtained with a three‐dimensional fast low‐angle shot (3D‐FLASH) sequence. Maximum intensity projection (MIP) of the phase images were reconstructed and five MIP images (at the levels of T11, T11/12, T12, T12/L1, and L1) were selected for the evaluation. The anterior median vein (AMV), posterior median vein (PMV), anterior radiculomedullary vein (ARV), posterior radiculomedullary vein (PRV), and sulcal vein (SV) were evaluated using a 4‐grade scale (0, none; 1, weak; 2, moderate; and 3, prominent).

Results:

The AMV was detected in all the subjects (100%). The detection rates of the other veins were lower: PMV, 65%; right ARV, 45%; left ARV, 15%; right PRV, 10%; left PRV, 30%; and SV, 0%. The average scores for AMV, PMV, right ARV, left ARV, right PRV, left PRV, and SV were 0.98, 0.24, 0.20, 0.08, 0.08, 0.14, and 0, respectively.

Conclusion:

SWI of the spine is feasible. The extrinsic spinal veins can be visualized by SWI without using contrast materials. J. Magn. Reson. Imaging 2010;31:32–38. © 2009 Wiley‐Liss, Inc.     

Removing background phase variations in susceptibility‐weighted imaging using a fast,forward‐field calculation

Purpose

To estimate magnetic field variations induced from air–tissue interface geometry and remove their effects from susceptibility‐weighted imaging (SWI) data.

Materials and Methods

A Fourier transform–based field estimation method is used to calculate the field deviation arising from air–tissue interface geometry. This is accomplished by manually drawing or automatically detecting the sinuses, the mastoid cavity, and the head geometry. The difference in susceptibility, Δχ, between brain tissue and air spaces is then calculated using a residual‐phase minimization approach. SWI data are corrected by subtracting the predicted phase from the original phase images. Resultant phase images are then used to perform the SWI postprocessing.

Results

Significant improvement in the postprocessed SWI data is demonstrated, most notably in the frontal and midbrain regions and to a lesser extent at the boundary of the brain. Specifically, there is much less dropout of signal after phase correction near air–tissue interfaces, making it possible to see vessels and structures that were often incorrectly removed by the conventional SWI postprocessing.

Conclusion

The Fourier transform–based field estimation method is a powerful 3D background phase removal method for improving SWI images, providing clearer images of the forebrain and the midbrain regions. J. Magn. Reson. Imaging 2009;29:937–948. © 2009 Wiley‐Liss, Inc.     

Positive contrast imaging of iron oxide nanoparticles with susceptibility‐weighted imaging

Superparamagnetic iron oxide particles can be utilized to label cells for immune cell and stem cell therapy. The labeled cells cause significant field distortions induced in their vicinity, which can be detected with magnetic resonance imaging (MRI). In conventional imaging, the signal voids arising from the field distortions lead to negative contrast, which is not desirable, as detection of the cells can be masked by native low signal tissue. In this work, a new method for visualizing magnetically labeled cells with positive contrast is proposed and described. The technique presented is based on the susceptibility‐weighted imaging (SWI) post‐processing algorithm. Phase images from gradient‐echo sequences are evaluated pixel by pixel, and a mask is created with values ranging from 0 to 1, depending on the phase value of the pixel. The magnitude image is then multiplied by the mask. With an appropriate mask function, positive contrast in the vicinity of the labeled cells is created. The feasibility of this technique is proved using an agar phantom containing superparamagnetic iron oxide particles–labeled cells and an ex vivo bovine liver. The results show high potential for detecting even small labeled cell concentrations in structurally inhomogeneous tissue types. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.     

Comparison of amyloid plaque contrast generated by T2‐weighted,T‐weighted,and susceptibility‐weighted imaging methods in transgenic mouse models of Alzheimer's disease

One of the hallmark pathologies of Alzheimer's disease (AD) is amyloid plaque deposition. Plaques appear hypointense on T₂‐weighted and T‐weighted MR images probably due to the presence of endogenous iron, but no quantitative comparison of various imaging techniques has been reported. We estimated the T₁, T₂, T, and proton density values of cortical plaques and normal cortical tissue and analyzed the plaque contrast generated by a collection of T₂‐weighted, T‐weighted, and susceptibility‐weighted imaging (SWI) methods in ex vivo transgenic mouse specimens. The proton density and T₁ values were similar for both cortical plaques and normal cortical tissue. The T₂ and T values were similar in cortical plaques, which indicates that the iron content of cortical plaques may not be as large as previously thought. Ex vivo plaque contrast was increased compared to a previously reported spin‐echo sequence by summing multiple echoes and by performing SWI; however, gradient echo and SWI were found to be impractical for in vivo imaging due to susceptibility interface–related signal loss in the cortex. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.     

Diminished visibility of cerebral venous vasculature in multiple sclerosis by susceptibility‐weighted imaging at 3.0 Tesla

Multiple sclerosis (MS) is a disease of the central nervous system characterized by widespread demyelination, axonal loss and gliosis, and neurodegeneration; susceptibility‐weighted imaging (SWI), through the use of phase information to enhance local susceptibility or T2* contrast, is a relatively new and simple MRI application that can directly image cerebral veins by exploiting venous blood oxygenation. Here, we use high‐field SWI at 3.0 Tesla to image 15 patients with clinically definite relapsing‐remitting MS and to assess cerebral venous oxygen level changes. We demonstrate significantly reduced visibility of periventricular white matter venous vasculature in patients as compared to control subjects, supporting the concept of a widespread hypometabolic MS disease process. SWI may afford a noninvasive and relatively simple method to assess venous oxygen saturation so as to closely monitor disease severity, progression, and response to therapy. J. Magn. Reson. Imaging 2009;29:1190–1194. © 2009 Wiley‐Liss, Inc.     

Human imaging at 9.4 T using T2*‐, phase‐, and susceptibility‐weighted contrast

The effect of susceptibility differences on an MR image is known to increase with field strength. Magnetic field inhomogeneities within the voxels influence the apparent transverse relaxation time T₂*, while effects due to different precession frequencies between voxels caused by local field variations are evident in the image phase, and susceptibility‐weighted imaging highlights the veins and deep brain structures. Here, these three contrast mechanisms are examined at a field strength of 9.4 T. The T₂* maps generated allow the identification of white matter structures not visible in conventional images. Phase images with in‐plane resolutions down to 130 μm were obtained, showing high gray/white matter contrast and allowing the identification of internal cortical structures. The susceptibility‐weighted images yield excellent visibility of small venous structures and attain an in‐plane resolution of 175 μm. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc.     

Detecting lesions in multiple sclerosis at 4.7 tesla using phase susceptibility‐weighting and T2‐weighting

Purpose

To demonstrate 4.7 Tesla (T) imaging methods for visualizing lesions in multiple sclerosis in the human brain using phase susceptibility‐weighting and T2 weighting.

Materials and Methods

Seven patients with relapsing‐remitting multiple sclerosis were imaged at 4.7T using three‐dimensional (3D) susceptibility‐weighted imaging (SWI) with 0.90 mm³ voxel volumes, and with 2D T2‐weighted fast spin echo (T2WFSE) with 0.34 mm³ voxels and 1.84 mm³ voxels. The visibility of MS lesions at 4.7T with phase SWI and T2WFSE was assessed by independent lesion counts made by an experienced neuroradiologist, and by quantitative measures.

Results

High resolution T2WFSE at 4.7T provided excellent depiction of hyperintense lesions. When combined with phase SWI, 124 total lesions were identified of which 18% were only visible on phase SWI and not on T2WFSE. The phase lesions had a mean phase shift relative to local background of ?11.15 ± 5.97 parts per billion.

Conclusion

Imaging at 4.7T can provide both high quality, high resolution T2WFSE and SWI for visualization of lesions in multiple sclerosis. Phase susceptibility‐weighting can identify additional lesions that are not visible with high resolution T2WFSE. J. Magn. Reson. Imaging 2009;30:737–742. © 2009 Wiley‐Liss, Inc.

     

Myocardial T mapping free of distortion using susceptibility‐weighted fast spin‐echo imaging: A feasibility study at 1.5 T and 3.0 T

This study demonstrates the feasibility of applying free‐breathing, cardiac‐gated, susceptibility‐weighted fast spin‐echo imaging together with black blood preparation and navigator‐gated respiratory motion compensation for anatomically accurate T mapping of the heart. First, T maps are presented for oil phantoms without and with respiratory motion emulation (T = (22.1 ± 1.7) ms at 1.5 T and T = (22.65 ± 0.89) ms at 3.0 T). T relaxometry of a ferrofluid revealed relaxivities of R = (477.9 ± 17) mM^?1s^?1 and R = (449.6 ± 13) mM^?1s^?1 for UFLARE and multiecho gradient‐echo imaging at 1.5 T. For inferoseptal myocardial regions mean T values of 29.9 ± 6.6 ms (1.5 T) and 22.3 ± 4.8 ms (3.0 T) were estimated. For posterior myocardial areas close to the vena cava T‐values of 24.0 ± 6.4 ms (1.5 T) and 15.4 ± 1.8 ms (3.0 T) were observed. The merits and limitations of the proposed approach are discussed and its implications for cardiac and vascular T‐mapping are considered. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.     

A population‐specific symmetric phase model to automatically analyze susceptibility‐weighted imaging (SWI) phase shifts and phase symmetry in the human brain

Purpose:

To create a population‐specific symmetric phase model and to evaluate the susceptibility‐weighted imaging (SWI) phase in terms of phase shift using different segmentation methods (manual and automatic) and phase shift symmetry, which is expected as a marker for lateralized Parkinson's disease (PD) symptoms.

Materials and Methods:

SWI and T₁‐weighted data from 25 PD patients and five healthy controls were acquired on a 3T MRI system. A population‐specific, symmetric phase model was developed. Regions of interest (ROIs) were defined manually on the phase model, manually on each individual data set, and automatically using model‐based segmentation (MBS). Manually‐ and MBS‐defined ROIs were compared using kappa values, and left‐right phase symmetry was evaluated using correlation analysis.

Results:

Independent of the analysis method, a phase increase from the anterior to the posterior putamen, and the average phase value relationship substantia nigra > globus pallidus > red nucleus was found. Phase symmetry analysis shows a difference between lateralized and symmetric PD.

Conclusion:

The symmetric phase model helps to analyze phase data with similar accuracy, but a greatly reduced tracing effort compared to individual tracing and also allows evaluating left‐right phase symmetries. J. Magn. Reson. Imaging 2010;31:215–220. © 2009 Wiley‐Liss, Inc.     

Robust tissue–air volume segmentation of MR images based on the statistics of phase and magnitude: Its applications in the display of susceptibility‐weighted imaging of the brain

Purpose

To develop a robust algorithm for tissue–air segmentation in magnetic resonance imaging (MRI) using the statistics of phase and magnitude of the images.

Materials and Methods

A multivariate measure based on the statistics of phase and magnitude was constructed for tissue–air volume segmentation. The standard deviation of first‐order phase difference and the standard deviation of magnitude were calculated in a 3 × 3 × 3 kernel in the image domain. To improve differentiation accuracy, the uniformity of phase distribution in the kernel was also calculated and linear background phase introduced by field inhomogeneity was corrected. The effectiveness of the proposed volume segmentation technique was compared to a conventional approach that uses the magnitude data alone.

Results

The proposed algorithm was shown to be more effective and robust in volume segmentation in both synthetic phantom and susceptibility‐weighted images of human brain. Using our proposed volume segmentation method, veins in the peripheral regions of the brain were well depicted in the minimum‐intensity projection of the susceptibility‐weighted images.

Conclusion

Using the additional statistics of phase, tissue–air volume segmentation can be substantially improved compared to that using the statistics of magnitude data alone. J. Magn. Reson. Imaging 2009;30:722–731. © 2009 Wiley‐Liss, Inc.