Echo-Planar Imaging

Because of their low bandwidth in the phase-encode (PE) direction, the susceptibility-induced off-resonance field causes distortions in echo planar imaging (EPI) images. It is therefore crucial to correct for susceptibility-induced distortions when performing diffusion studies using EPI. The susceptibility-induced field is caused by the object (head) disrupting the field and it is typically assumed that it remains constant within a framework defined by the object, (i.e. it follows the object as it moves in the scanner). However, this is only approximately true. When a non-spherical object rotates around an axis other than that parallel with the magnetic flux (the z-axis) it changes the way it disrupts the field, leading to different distortions. Hence, if using a single field to correct for distortions there will be residual distortions in the volumes where the object orientation is substantially different to that when the field was measured. In this paper we present a post-processing method for estimating the field as it changes with motion during the course of an experiment. It only requires a single measured field and knowledge of the orientation of the subject when that field was acquired. The volume-to-volume changes of the field as a consequence of subject movement are estimated directly from the diffusion data without the need for any additional or special acquisitions. It uses a generative model that predicts how each volume would look predicated on field change and inverts that model to yield an estimate of the field changes. It has been validated on both simulations and experimental data. The results show that we are able to track the field with high accuracy and that we are able to correct the data for the adverse effects of the changing field.

PurposeTo compare signal-to-noise ratio (SNR) efficiency and diffusion tensor metrics of cardiac diffusion tensor mapping using acceleration-compensated spin-echo (SE) and stimulated echo acquisition mode (STEAM) imaging.MethodsDiffusion weighted SE and STEAM sequences were implemented on a clinical 1.5 Tesla MR system. The SNR efficiency of SE and STEAM was measured (b = 50-450 s/mm(2) ) in isotropic agar, anisotropic diffusion phantoms and the in vivo human heart. Diffusion tensor analysis was performed on mean diffusivity, fractional anisotropy, helix and transverse angles.ResultsIn the isotropic phantom, the ratio of SNR efficiency for SE versus STEAM, SNRt (SE/STEAM), was 2.84 ± 0.08 for all tested b-values. In the anisotropic diffusion phantom the ratio decreased from 2.75 ± 0.05 to 2.20 ± 0.13 with increasing b-value, similar to the in vivo decrease from 2.91 ± 0.43 to 2.30 ± 0.30. Diffusion tensor analysis revealed reduced deviation of helix angles from a linear transmural model and reduced transverse angle standard deviation for SE compared with STEAM. Mean diffusivity and fractional anisotropy were measured to be statistically different (P ConclusionCardiac DTI using motion-compensated SE yields a 2.3-2.9× increase in SNR efficiency relative to STEAM and improved accuracy of tensor metrics. The SE method hence presents an attractive alternative to STEAM based approaches. Magn Reson Med 76:862-872, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

PurposeA comprehensive image-based characterization of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organization within the voxel. While progress has been made with diffusion MRI-based approaches to measure axonal morphology, to date available myelin metrics simply assign a single scalar value to the voxel, reflecting some form of average of its constituent fibres. Here, a new experimental framework that combines diffusion MRI and relaxometry is introduced. It provides, for the first time, the ability to assign to each unique fibre system within a voxel, a unique value of the longitudinal relaxation time, T1, which is largely influenced by the myelin content.MethodsWe demonstrate the method through simulations, in a crossing fibres phantom, in fixed brains and in vivo.ResultsThe method is capable of recovering unique values of T1 for each fibre population.ConclusionThe ability to extract fibre-specific relaxometry properties will provide enhanced specificity and, therefore, sensitivity to differences in white matter architecture, which will be invaluable in many neuroimaging studies. Further the enhanced specificity should ultimately lead to earlier diagnosis and access to treatment in a range of white matter diseases where axons are affected.

PurposeEcho-planar time-resolved imaging (EPTI) provides distortion- and T2/T2* blurring-free multi-echo/multi-contrast imaging with fast speed, making it an efficient acquisition method for various MRI applications. Here, we aim to achieve water-fat separation for EPTI to improve fat signal removal for brain/body imaging applications.Theory and methodsA water/fat separated EPTI (WFS-EPTI) technique is developed, which leverages the intrinsic multi-echo data provided by EPTI readout and introduces a novel strategy for highly-accelerated spatiotemporal encoding and reconstruction to separate water and fat signals. Specifically, DIXON encoding is integrated into the spatiotemporal acquisition by acquiring the echo train at scheduled TEs and echo-spacing, therefore modulating the rapidly changing fat phase evolution across EPTI readout into a more uniform, coherent manner (e.g., in-phase and out-of-phase conditions for odd and even image series). This enables robust subspace representations even in the presence of fat signals. A joint subspace reconstruction is then proposed, which separately performs data consistency for each echo group to ensure fidelity, while jointly leveraging shared information to improve conditioning. Furthermore, an auto-calibrated WFS-EPTI is developed to improve motion robustness for abdominal imaging, and a data-driven basis extraction is employed to address imperfect in-phase/out-of-phase conditions.ResultsBoth phantom experiments and in vivo human imaging across multiple body regions-including brain, head-neck, and abdomen-demonstrated the feasibility of the proposed method.ConclusionThe proposed WFS-EPTI can provide water/fat separation in rapid acquisition and obtain high resolution, distortion-free multi-contrast/quantitative imaging in the presence of fat signals.