Purpose Previous studies in phantoms and animals using animal MR systems have shown promising results in using oscillating gradient spin echo (OGSE) diffusion acquisition to depict microstructure information. The OGSE approach has also been shown to be a sensitive biomarker of tumor treatment response and white matter-related diseases. Translating these studies to a human MR scanner faces multiple challenges due to the much weaker gradient system. The goals of the current study are to optimize the OGSE acquisition for a human MR system and investigate its applicability in the in vivo human brain. Methods An analytical analysis of the OGSE modulation spectrum was provided. Based on this analysis and thorough simulation experiments, the OGSE acquisition was optimized in terms of diffusion waveform shape, waveform timing, and sequence timing – to achieve higher diffusion sensitivity and better sampling of the diffusion spectrum. Results The trapezoid-cosine waveform was found to be the optimal OGSE waveform. At the three employed peak encoding frequencies of 18 Hz, 44 Hz, and 63 Hz, the waveform polarity for the least blurry sampling of the diffusion spectrum was 90+/180−, 90+/180+, and 90+/180+, respectively. For the highest diffusion to noise ratio (DNR) at 63 Hz, the b-value was 200 s/mm2 and the echo time was 116 ms. Using the optimized sequence, a frequency dependence of the measured ADCs was observed in white-matter-dominant regions such as the corpus callosum. Conclusion The obtained results demonstrate, for the first time, the potential of utilizing an OGSE acquisition for investigating microstructure information on a human MR system.

This study shows the benefits of improved resolution and reduced distortion of readout-segmented EPI in evaluating the orbit, skull base, and posterior fossa, sites of common neuropathologic abnormalities in children.

Diffusion tensor imaging of localized anatomic regions, such as brainstem, cervical spinal cord, and optic nerve, is challenging because of the existence of significant susceptibility differences, severe physiologic motion in the surrounding tissues, and the need for high spatial resolution to resolve the underlying complex neuroarchitecture. The aim of the methodology presented here is to achieve high-resolution diffusion tensor imaging in localized regions of the central nervous system that is motion insensitive and immune to susceptibility while acquiring a set of two-dimensional images with more than six diffusion encoding directions within a reasonable total scan time. We accomplish this aim by implementing self-navigated, multishot, variable-density, spiral encoding with outer volume suppression. We establish scan protocols for achieving equal signal-to-noise ratio at 1.2 mm and 0.8 mm in-plane resolution for reduced field-of-view diffusion tensor imaging of the brainstem. In vivo application of the technique on the human pons of three subjects shows a clear delineation of the multiple local neural tracts. By comparing scans acquired with varying in-plane resolution but with constant signal-to-noise ratio, we demonstrate that increasing the resolution and reducing the partial volume effect result in higher fractional anisotropy values for the corticospinal tracts.

A multishot data acquisition strategy is one way to mitigate B0 distortion and T2∗ blurring for high-resolution diffusion-weighted magnetic resonance imaging experiments. However, different object motions that take place during different shots cause phase inconsistencies in the data, leading to significant image artifacts. This work proposes a maximum likelihood estimation and k-space correction of motion-induced phase errors in 3D multishot diffusion tensor imaging. The proposed error estimation is robust, unbiased, and approaches the Cramer-Rao lower bound. For rigid body motion, the proposed correction effectively removes motion-induced phase errors regardless of the k-space trajectory used and gives comparable performance to the more computationally expensive 3D iterative nonlinear phase error correction method. The method has been extended to handle multichannel data collected using phased-array coils. Simulation and in vivo data are shown to demonstrate the performance of the method.

Purpose To propose a method for mitigating slab boundary artifacts in 3D multislab diffusion imaging with no or minimal increases in scan time. Methods The multislab acquisition was treated as parallel imaging acquisition where the slab profiles acted as the traditional receiver sensitivity profiles. All the slabs were then reconstructed simultaneously along the slab direction using Cartesian-based sensitivity encoding (SENSE) reconstruction. The slab profile estimation was performed using either a Bloch simulation or a calibration scan. Results Both phantom and in vivo results showed negligible slab boundary artifacts after reconstruction using the proposed method. The performance of the proposed method is comparable to the state-of-the-art slab combination method without the scan time penalty that depends on the number of acquired volumes. The obtained g-factor map of the SENSE reconstruction problem showed a maximum g-factor of 1.7 in the region of interest. Conclusion We proposed a novel method for mitigating slab boundary artifacts in 3D diffusion imaging by treating the multislab acquisition as a parallel imaging acquisition and reconstructing all slabs simultaneously using Cartesian SENSE. Unlike existing methods, the scan time increase, if any, does not scale with the number of image volumes acquired.