Surgical planning for deep brain stimulation implantation procedures requires T1-weighted imaging (T1WI) for stereotactic navigation. Because the subthalamic nucleus, the main target for deep brain stimulation, and other midbrain nuclei cannot be visualized on the stereotactic guidance T1WI, additional T2-weighted imaging (T2WI) is generally obtained and registered to the T1WI for surgical targeting. Surgical planning based on the registration of the 2 data sets is subject to error resulting from inconsistent geometric distortions and any subject movement between the 2 scans. In this article, we propose a new method to produce susceptibility-enhanced, contrast-optimized T1-weighted 3-dimensional spoiled gradient recalled acquisition in steady state images with enhanced contrast for midbrain nuclei within the volumetric T1WI data set itself, eliminating the need for additional T2WI. The scan parameters of 3-dimensional spoiled gradient recalled acquisition in steady state are chosen in a way that T1WI can be obtained from conventional magnitude reconstruction and images with improved contrast between midbrain nuclei and surrounding tissues can be produced from the same data by performing susceptibility-weighted imaging reconstruction on a chosen region of interest. In addition, our preliminary experience suggests that the resulting contrast between the midbrain nuclei is superior to the current state-of-the-art fast spin echo T2WI in depicting the subthalamic nucleus as distinct from the substantia nigra pars reticulata and clear depiction of the nucleus ventrointermedius externus of thalamus.
A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging.
Resting-state functional magnetic resonance imaging (fMRI) is a promising tool for neuroscience and clinical studies. However, there exist significant variations in strength and spatial extent of resting-state functional connectivity over repeated sessions in a single or multiple subjects with identical experimental conditions. Reproducibility studies have been conducted for resting-state fMRI where the reproducibility was usually evaluated in predefined regions-of-interest (ROIs). It was possible that reproducibility measures strongly depended on the ROI definition. In this work, this issue was investigated by comparing data-driven and predefined ROI-based quantification of reproducibility. In the data-driven analysis, the reproducibility was quantified using functionally connected voxels detected by a support vector machine (SVM)-based technique. In the predefined ROI-based analysis, all voxels in the predefined ROIs were included when estimating the reproducibility. Experimental results show that (1) a moderate to substantial within-subject reproducibility and a reasonable between-subject reproducibility can be obtained using functionally connected voxels identified by the SVM-based technique; (2) in the predefined ROI-based analysis, an increase in ROI size does not always result in higher reproducibility measures; (3) ROI pairs with high connectivity strength have a higher chance to exhibit high reproducibility; (4) ROI pairs with high reproducibility do not necessarily have high connectivity strength; (5) the reproducibility measured from the identified functionally connected voxels is generally higher than that measured from all voxels in predefined ROIs with typical sizes. The findings (2) and (5) suggest that conventional ROI-based analyses would underestimate the resting-state fMRI reproducibility.
Spiral imaging is vulnerable to spatial and temporal variations of the amplitude of the static magnetic field (B(0)) caused by susceptibility effects, eddy currents, chemical shifts, subject motion, physiological noise, and system instabilities, resulting in image blurring. Here, a novel off-resonance correction method is proposed to address these issues. A k-space energy spectrum analysis algorithm is first applied to inherently and dynamically generate a B(0) map from the k-space data at each time point, without requiring any additional data acquisition, pulse sequence modification, or phase unwrapping. A simulated phase evolution rewinding algorithm and an automatic residual deblurring algorithm are then used to correct for the blurring caused by both spatial and temporal B(0) variations, resulting in a high spatial and temporal fidelity. This method is validated against conventional B(0) mapping and deblurring methods, and its advantages for dynamic MRI applications are demonstrated in functional MRI studies.
Intrascan subject movement in clinical MR spectroscopic examinations may result in inconsistent water suppression that distorts the metabolite signals, frame-to-frame variations in spectral phase and frequency, and consequent reductions in the signal-to-noise ratio due to destructive averaging. Frame-to-frame phase/frequency corrections, although reported to be successful in achieving constructive averaging, rely on consistent water suppression, which may be difficult in the presence of intrascan motion. In this study, motion correction using non-water-suppressed data acquisition is proposed to overcome the above difficulties. The time-domain matrix-pencil postprocessing method was used to extract water signals from the non-water-suppressed spectroscopic data, followed by phase and frequency corrections of the metabolite signals based on information obtained from the water signals. From in vivo experiments on seven healthy subjects at 3.0 T, quantification of metabolites using the unsuppressed water signal as a reference showed improved correlation with water-suppressed data acquired in the absence of motion (R(2) = 0.9669; slope = 0.94). The metabolite concentrations derived using the proposed approach were in good agreement with literature values. Computer simulations under various degrees of frequency and phase variations further demonstrated robust performance of the time-domain postprocessing approach.
