Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI

Purpose MUSSELS is a one‐step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high‐resolution diffusion MRI studies. Methods The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging‐based methods that recover artifact‐free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low‐rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high‐resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole‐brain in vivo data, we show the utility of the IRLS MUSSELS for routine high‐resolution studies with reduced computational burden. Results IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden. Conclusions The proposed method is observed to be computationally efficient to enable routine high‐resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Mani

Aggarwal

Magnotta

et al. 2019

“…Isolated IA signals can be obtained from WM samples using diffusion dMRI acquired at very high b‐values or using magnetic resonance spectroscopy (dMRS). Indeed, high‐end scanners like the Connectom Scanner allow performing dMRI acquisitions at b‐values that are theoretically high enough to neglect the EA signal . The protocol should nevertheless be adapted to in vivo diffusivity and careful experiments should be conducted to ensure that the EA compartment can effectively be ignored (e.g., Monte Carlo simulations).…”

Section: Discussionmentioning

confidence: 99%

ActiveAx_ADD: Toward non‐parametric and orientationally invariant axon diameter distribution mapping using PGSE

Romascano

Baraković

Rafael-Patiño

et al. 2019

Purpose Non‐invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill‐posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to estimate the orientation from the signal, providing orientationally invariant estimates. The mean diameter is estimated instead of the distribution for the solution to be tractable. Here, we propose an extension (ActiveAxADD) that provides non‐parametric and orientationally invariant estimates of the whole distribution. Theory The accelerated microstructure imaging with convex optimization (AMICO) framework accelerates mean diameter estimation using a linear formulation combined with Tikhonov regularization to stabilize the solution. Here, we implement a new formulation (ActiveAxADD) that uses Laplacian regularization to provide robust estimates of the whole ADD. Methods The performance of ActiveAxADD was evaluated using Monte Carlo simulations on synthetic white matter samples mimicking axon distributions reported in histological studies. Results ActiveAxADD provided robust ADD reconstructions when considering the isolated intra‐axonal signal. However, our formulation inherited some common microstructure imaging limitations. When accounting for the extra axonal compartment, estimated ADDs showed spurious peaks and increased variability because of the difficulty of disentangling intra and extra axonal contributions. Conclusion Laplacian regularization solves the ill‐posedness regarding the intra axonal compartment. ActiveAxADD can potentially provide non‐parametric and orientationally invariant ADDs from isolated intra‐axonal signals. However, further work is required before ActiveAxADD can be applied to real data containing extra‐axonal contributions, as disentangling the 2 compartment appears to be an overlooked challenge that affects microstructure imaging methods in general.

“…High maximum gradient field amplitudes of ≥200 mT/m together with high slew rates (SRs) (>200 T/m/s) have previously been possible only in preclinical MRI systems, producing measurements of axonal diameter as well characterizing the extra‐axonal space. A dedicated high‐performance gradient system with a 56‐cm bore diameter for human imaging achieved a maximum gradient amplitude of 300 mT/m . However, that impressive whole‐body gradient system required 4 sets of gradient drivers per axis, and had a maximum SR of 200 T/m/s.…”

Section: Introductionmentioning

confidence: 99%

Highly efficient head‐only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging

Foo

Tan

Vermilyea

et al. 2019

Purpose To develop a highly efficient magnetic field gradient coil for head imaging that achieves 200 mT/m and 500 T/m/s on each axis using a standard 1 MVA gradient driver in clinical whole‐body 3.0T MR magnet. Methods A 42‐cm inner diameter head‐gradient used the available 89‐ to 91‐cm warm bore space in a whole‐body 3.0T magnet by increasing the radial separation between the primary and the shield coil windings to 18.6 cm. This required the removal of the standard whole‐body gradient and radiofrequency coils. To achieve a coil efficiency ~4× that of whole‐body gradients, a double‐layer primary coil design with asymmetric x‐y axes, and symmetric z‐axis was used. The use of all‐hollow conductor with direct fluid cooling of the gradient coil enabled ≥50 kW of total heat dissipation. Results This design achieved a coil efficiency of 0.32 mT/m/A, allowing 200 mT/m and 500 T/m/s for a 620 A/1500 V driver. The gradient coil yielded substantially reduced echo spacing, and minimum repetition time and echo time. In high b = 10,000 s/mm2 diffusion, echo time (TE) < 50 ms was achieved (>50% reduction compared with whole‐body gradients). The gradient coil passed the American College of Radiology tests for gradient linearity and distortion, and met acoustic requirements for nonsignificant risk operation. Conclusions Ultra‐high gradient coil performance was achieved for head imaging without substantial increases in gradient driver power in a whole‐body 3.0T magnet after removing the standard gradient coil. As such, any clinical whole‐body 3.0T MR system could be upgraded with 3‐4× improvement in gradient performance for brain imaging.