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.
Odd-even echo inconsistencies result in Nyquist ghost artifacts in the reconstructed EPI images. The ghost artifacts reduce the image signal-to-noise ratio and make it difficult to correctly interpret the EPI data. In this article a new 2D phase mapping protocol and a postprocessing algorithm are presented for an effective Nyquist ghost artifacts removal. After an appropriate k-space data regrouping, a 2D map accurately encoding low- and high-order phase errors is derived from two phase-encoded reference scans, which were originally proposed by Hu and Le (Magn Reson Med 36:166-171;1996) for their 1D nonlinear correction method. The measured phase map can be used in the postprocessing algorithm developed to remove ghost artifacts in subsequent EPI experiments. Experimental results from phantom, animal, and human studies suggest that the new technique is more effective than previously reported methods and has a better tolerance to signal intensity differences between reference and actual EPI scans. The proposed method may potentially be applied to repeated EPI measurements without subject movements, such as functional MRI and diffusion coefficient mapping.
