BACKGROUND AND PURPOSE:Quantitative susceptibility mapping of the human brain has demonstrated strong potential in examining iron deposition, which may help in investigating possible brain pathology. This study assesses the reproducibility of quantitative susceptibility mapping across different imaging sites.

The steady-state free precession (SSFP) method has been shown to exhibit strong potential for distortion-free functional magnetic resonance imaging (fMRI). One major challenge of SSFP fMRI is that the frequency band corresponding to the highest functional sensitivity is extremely narrow, leading to substantial loss of functional contrast in the presence of magnetic field drifts. In this study we propose a frequency stabilization scheme whereby an RF pulse with small flip angle is applied before each image scan, and the initial phase of the free induction decay (FID) signals is extracted to reflect temporal field drifts. A simple infinite impulse response (IIR) filter is further employed to obtain a low-pass-filtered estimate of the central reference frequency for the upcoming scan. Experimental results suggest that the proposed scheme can stabilize the frequency settings in accordance with field drifts, with oscillation amplitudes of <0.5 Hz. Phantom studies showed that both slow drifts and fast fluctuations were prominently reduced, resulting in less than 5% signal variations. Visual fMRI at submillimeter in-plane resolution further demonstrated 15% activation signals that were nicely registered in the microvessels within the sulci. It is concluded that the IIR-filtered frequency stabilization is an effective technique for achieving reliable SSFP fMR images at high field strengths. Magn Reson Med 57:369 -379, 2007.

Dynamic contrast-enhanced (DCE) MRI has been used to quantitatively evaluate pulmonary perfusion based on the assumption of a gamma-variate function and an arterial input function (AIF) for deconvolution. However, these assumptions may be too simplistic and may not be valid in pathological conditions, especially in patients with complex inflow patterns (such as in congenital heart disease). Exploratory data analysis methods make minimal assumptions on the data and could overcome these pitfalls. In this work, two temporal clustering methodsKohonen clustering network (KCN) and Fuzzy C-Means (FCM)-were concatenated to identify pixel time-course patterns. The results from seven normal volunteers show that this technique is superior for discriminating vessels and compartments in the pulmonary circulation. Patient studies with five cases of acquired or congenital pulmonary perfusion disorders demonstrate that pathologies can be highlighted in a concise map that combines information of the mean transit time (MTT) and pulmonary blood volume (PBV). The method was found to provide greater insight into the perfusion dynamics that might be over- Proper regulation of pulmonary perfusion and ventilation is required for efficient gas exchange. Therefore, it is essential to accurately estimate pulmonary perfusion to assess the pathophysiology of the lung. In current clinical practice, pulmonary perfusion is evaluated with the use of nucleotide scintigraphic methods. However, these techniques are limited by poor spatial resolution and artifacts introduced by long imaging times (1). Magnetic resonance imaging (MRI) has been demonstrated to be a promising tool for evaluating brain perfusion with a spatial resolution of less than 1.5 mm and temporal resolution of about 1 s using Gd-DTPA as a contrast agent (2). When the same strategy is extended to image pulmonary perfusion, the poor magnetic field homogeneity caused by the complex air-tissue interfaces in the lung reduces the T* 2 of the lung tissue to only a few milliseconds. The resulting low signalto-noise ratio (SNR) limits the application of MRI in the lungs. Recent developments in short-TE imaging sequences have overcome the T* 2 decay and made it feasible to assess pulmonary perfusion by dynamic contrast-enhanced (DCE) MRI, as used in brain perfusion studies (1,3-7).Assuming that the contrast agent is a nondiffusible, intravascular tracer, quantitative indices, such as the relative pulmonary blood volume (PBV), relative mean transit time (MTT), and relative pulmonary blood flow (PBF), can be derived from the time courses of signal intensity (SI) in DCE-MRI (1,8,9). To eliminate the SI change from recirculation of the tracer, the time course is fitted to an assumed bolus-shaped function (typically a gamma-variate function) before the calculation is performed. Summary parameters, such as time-to-peak and gamma-fitting parameters, can be obtained as well (10). Furthermore, to achieve the absolute quantification of PBV, PBF, and MTT, a suitable arterial input function (AIF) is ...

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It is concluded that the bSSFP sequence is a plausible technique for fMRI investigations at submillimeter voxel widths without compromising fCNR. The reduction of partial volume averaging with nonactivated brain tissues to retain fCNR is uniquely suitable for high spatial resolution applications such as the resolving of columnar organization in the brain.

Purpose:To develop an adaptive subvoxel stepping scheme, as an adjunct to tensor deflection (TEND) tractography, that automatically adjusts the stepping size by considering the tensor linearity to properly trace fiber bundles in regions with different degrees of tensor anisotropy. Materials and Methods:A theoretical investigation of the TEND algorithm was performed to assess the degree of deflection of the propagation vector toward the major eigenvector. Mathematically generated phantoms (one with curved fibers and the other with crossing fibers) at wide ranges of signal-to-noise ratio (SNR), and human brain images obtained in vivo were used to test the performance of the adaptive stepping algorithm. Results:The degree of deflection was found to be inversely related to the stepping size. A small stepping size was advantageous for tracing single curved fiber bundles, whereas a large stepping size was beneficial for passing through fiber crossing regions. The performance of the adaptive stepping algorithm was superior to fixed stepping in both situations, leading to an approximately 0.17 voxel of deviation in curved fibers and a nearly 100% successful tracking rate in crossing fibers at typical SNR. Human brain images demonstrated similar results. Conclusion:The adaptive stepping algorithm is a helpful adjunct to TEND tractography.

Purpose: Imaging of midbrain nuclei using T2-or T2*-weighted MRI often entails long echo time, leading to long scan time. In this study, an inverse double-echo steady-state (iDESS) technique is proposed for efficiently depicting midbrain nuclei. Methods: Thirteen healthy subjects participated in this study. iDESS was performed along with two sets of T2*-weighted spoiled gradient-echo images (SPGR1, with scan time identical to iDESS and SPGR2, using clinical scanning parameters as a reference standard) for comparison. Generation of iDESS composite images combining two echo signals was optimized for maximal contrast-to-noise ratio (CNR) between the red nuclei and surrounding tissues. Signal-to-noise ratios (SNRs) were calculated from the occipital lobe. Comparison was also made using phase-enhanced images as in standard susceptibility-weighted imaging (SWI). Results: The iDESS images present significantly higher SNR efficiency (171.3) than SPGR1 (158.7, p = 0.013) and SPGR2 (95.5, p < 10 −8). iDESS CNR efficiency (19.2) is also significantly greater than SPGR1 (6.9, p < 10) and SPGR2 (14.3, p = 0.0016). Compared with DESS, iDESS provides further advantage on enhanced phase information and hence improved contrast on SWI-processed images. Conclusions: iDESS efficiently depicts midbrain nuclei with improved CNR efficiency, increased SNR efficiency, and reduced scan time and is less prone to susceptibility signal loss from air-tissue interfaces. C 2015 American Association of Physicists in Medicine.

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