Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine
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Functional MRI (fMRI) by means of spin-echo (SE) techniques provides an interesting alternative to gradient-echo methods because the contrast is based primarily on dynamic averaging associated with the blood oxygenation level-dependent (BOLD) effect. In this article the contributions from different brain compartments to BOLD signal changes in SE echo planar imaging (EPI) are investigated. To gain a better understanding of the underlying mechanisms that cause the fMRI contrast, two experiments are presented: First, the intravascular contribution is decomposed into two fractions with different regimes of flow by means of diffusion-weighting gradient schemes which are either flow-compensated, or will maximally dephase moving spins. ⋯ The results indicate two qualitatively different components of flowing blood which contribute to the BOLD contrast and a nearly equal share in functional signal from the intra- and extravascular compartments at TE approximately 80 ms and 3 T. Combining these results, there is evidence that at least one-half of the functional signal originates from the parenchyma in SE fMRI at 3 T. The authors suggest the use of flow-compensated diffusion weighting for SE fMRI to improve the sensitivity to the parenchyma.
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Contrast-enhanced (CE) MR angiography of the right coronary artery (RCA) was performed using 2D thick-slice projection imaging with a small (8 mL) intravenous injection of contrast agent in six volunteers. With a tight contrast bolus injection, the RCA was enhanced for a few seconds after the contrast bolus was washed out of the right ventricle. ⋯ A mean vessel length of 7.1 +/- 0.9 cm was depicted with a signal-to-noise ratio of 11.8 +/- 0.7 and contrast-to-noise ratio of 6.1 +/- 0.6. Thick-slice 2D projection CE SSFP is a promising method to depict the RCA.
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Anisotropic diffusion filtering is widely used for MR image enhancement. However, the anisotropic filter is nonoptimal for MR images with spatially varying noise levels, such as images reconstructed from sensitivity-encoded data and intensity inhomogeneity-corrected images. In this work, a new method for filtering MR images with spatially varying noise levels is presented. ⋯ The noise-adaptive method was demonstrated to outperform the standard anisotropic diffusion filter in both image error reduction and image signal-to-noise ratio (SNR) improvement. The method was also applied to inhomogeneity-corrected and sensitivity encoding (SENSE) images. The new filter was shown to improve segmentation of MR brain images with spatially varying noise levels.