Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

Lin, Fa‐Hsuan; Witzel, Thomas; Schultz, Gerrit; Gallichan, Daniel; Kuo, Wen Jui; Wang, Fu Nien; Hennig, Jürgen; Zaitsev, Maxim; Belliveau, John W.

doi:10.1002/mrm.24115

Cited by 32 publications

(44 citation statements)

References 16 publications

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“…The former might be useful for Delta Relaxation Enhanced MR (dreMR) imaging (Alford et al 2009) and the latter for PATLOC (Hennig et al 2008; Schultz et al 2010; Lin et al 2012) or O-space style encoding (Stockmann et al 2010). …”

Section: Gradient Design and Implementationmentioning

confidence: 99%

Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

et al. 2013

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Perhaps more than any other “-omics” endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depends on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an “image” of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain’s structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with Gmax = 300mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200 T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5 minutes. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coils at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.

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Section: Gradient Design and Implementationmentioning

confidence: 99%

Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

et al. 2013

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“…As an alternative to linear SEMs, nonlinear SEMs have been used to improve the dynamic range of MR signals (3) and to localize NMR signals without using selective excitation (4). Recently, it has been suggested that parallel imaging techniques using localized gradients (PatLoc) (5) can achieve a higher spatial resolution and reduce peripheral nerve stimulation using nonlinear surface SEM coils and an radiofrequency (RF) receiver array (6–8). O‐space imaging is a different imaging approach using the Z2 SEM together with linear SEMs to reconstruct accelerated images with high spatial resolution (9).…”

Section: Introductionmentioning

confidence: 99%

“…One price for using nonlinear imaging SEMs is that the images generally have anisotropic resolution and the image reconstruction becomes more complicated (8, 9). The anisotropic spatial resolution in PatLoc and O‐space imaging can be improved by using a combination of both linear and nonlinear SEMs in data acquisition (6, 7, 10). Also, PatLoc image reconstruction can be partially simplified using the iterative time‐domain reconstruction based on the conjugated gradient algorithm (7, 10).…”

Section: Introductionmentioning

confidence: 99%

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Multidimensionally encoded magnetic resonance imaging

Lin

2012

Magnetic Resonance in Med

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Magnetic resonance imaging typically achieves spatial encoding by measuring the projection of a q-dimensional object over q-dimensional spatial bases created by linear spatial encoding magnetic fields (SEMs). Recently, imaging strategies using nonlinear SEMs have demonstrated potential advantages for reconstructing images with higher spatiotemporal resolution and reducing peripheral nerve stimulation. In practice, nonlinear SEMs and linear SEMs can be used jointly to further improve the image reconstruction performance. Here we propose the multi-dimensionally encoded (MDE) MRI to map a q-dimensional object onto a p-dimensional encoding space where p > q. MDE MRI is a theoretical framework linking imaging strategies using linear and nonlinear SEMs. Using a system of eight surface SEM coils with an eight-channel RF coil array, we demonstrate the five-dimensional MDE MRI for a two-dimensional object as a further generalization of PatLoc imaging and O-space imaging. We also present a method of optimizing spatial bases in MDE MRI. Results show that MDE MRI with a higher dimensional encoding space can reconstruct images more efficiently and with a smaller reconstruction error when the k-space sampling distribution and the number of samples are controlled.

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“…Both SMASH/GRAPPA and SENSE, and indeed most of MRI, were designed to work with linear B 0 gradients. More recently, specialized non-linear gradient field coils [11] have been used with time varying gradient methods, like PatLoc [12][13][14], to reduce the ΔB max /Δt responsible for producing painful peripheral nerve stimulation. The efficiency of imaging with nonlinear B 0 encoding fields may be increased, for example in methods like O-space imaging [15] and null space imaging [16], by designing the B 1 fields to encode information complementary to that encoded by the SEM.…”

Section: Introductionmentioning

confidence: 99%

Cyclic generalized projection MRI

Sarty

2015

Magnetic Resonance Imaging

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Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

Cited by 32 publications

References 16 publications

Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

Multidimensionally encoded magnetic resonance imaging

Cyclic generalized projection MRI

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