Magnetic resonance imaging
-
In this study, we investigated the use of a single-shot fast spin-echo-based sequence to perform diffusion tensor imaging (DTI) with improved anatomic fidelity through the entire brain and the cervical spine. Traditionally, diffusion tensor images have been acquired by single-shot echo-planar imaging (EPI) methods in which large distortions result from magnetic susceptibility effects, especially near air-tissue interfaces. These distortions can be problematic, especially in anterior and inferior portions of the brain, and they also can severely limit applications in the spine. ⋯ The mean diffusion measurements obtained with the SSFSE acquisition were not statistically different (p > 0.05) from EPI-based acquisitions. Compared to routine T(2)-weighted MR images, the DTI-EPI sequence showed up to 20% in elongation of the brain in the anterior-posterior direction on a sagittal image due to magnetic susceptibility distortions, whereas in the DTI-SSFSE, the image distortions were negligible. The diffusion tensor SSFSE method was also able to assess diffusion abnormalities in a brain stem hemorrhage, unaffected by the spatial distortions that limited conventional EPI acquisition.
-
Several studies have indicated that deconvolution based on singular value decomposition (SVD) is a robust concept for retrieval of cerebral blood flow in dynamic susceptibility contrast (DSC) MRI. However, the behavior of the technique under typical experimental conditions has not been completely investigated. In the present study, cerebral perfusion was simulated using different temporal resolutions, different signal-to-noise ratios (S/Ns), different shapes of the arterial input function (AIF), different signal drops, and different cut-off levels in the SVD deconvolution. ⋯ No systematic change of the average rCBV was observed with increasing noise or with increasing image time interval. At S/N = 40, a low cut-off value resulted in an rCBF that was closer to the true value. Furthermore, at low S/N it was difficult to differentiate ischemic tissue from WM.
-
By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. ⋯ An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.