NonCartesian MR image reconstruction with integrated gradient nonlinearity correction

Abstract

Purpose: To derive a noniterative gridding-type reconstruction framework for nonCartesian magnetic resonance imaging (MRI) that prospectively accounts for gradient nonlinearity (GNL)-induced image geometrical distortion during MR image reconstruction, as opposed to the standard, image-domain based GNL correction that is applied after reconstruction; to demonstrate that such framework is able to reduce the image blurring introduced by the conventional GNL correction, while still offering effective correction of GNL-induced geometrical distortion and compatibility with off-resonance correction. Methods: After introducing the nonCartesian MRI signal model that explicitly accounts for the effects of GNL and off-resonance, a noniterative gridding-type reconstruction framework with integrated GNL correction based on the type-III nonuniform fast Fourier transform (NUFFT) is derived. A novel type-III NUFFT implementation is then proposed as a numerically efficient solution to the proposed framework. The incorporation of simultaneous B 0 off-resonance correction to the proposed framework is then discussed. Several phantom and in vivo data acquired via various 2D and 3D nonCartesian acquisitions, including 2D Archimedean spiral, 3D shells with integrated radial and spiral, and 3D radial sampling, are used to compare the results of the proposed and the standard GNL correction methods. Results: Various phantom and in vivo data demonstrate that both the proposed and the standard GNL correction methods are able to correct the coarse-scale geometric distortion and blurring induced by GNL and off-resonance. However, the standard GNL correction method also introduces blurring effects to corrected images, causing blurring of resolution inserts in the phantom images and loss of small vessel clarity in the angiography examples. On the other hand, the results after the proposed GNL correction show better depiction of resolution inserts and higher clarity of small vessel. Conclusions: The proposed GNL-integrated nonCartesian reconstruction method can mitigate the resolution loss that occurs during standard image-domain GNL correction, while still providing effective correction of coarse-scale geometric distortion and blurring induced by GNL and off-resonance. C 2015 American Association of Physicists in Medicine. [http://dx

Gradient pre-emphasis to counteract first-order concomitant fields on asymmetric MRI gradient systems

“…It can be implemented either in the pulse sequence or hardware (e.g., eddy current pre-emphasis firmware), offering substantial implementation flexibility. When implemented in hardware, it can be performed together with gradient eddy current pre-emphasis, as done in the present study, and therefore is transparent to pulse sequence design or subsequent corrections of offresonance (27), gradient delays, eddy currents (35,36), gradient nonlinearity (22,26,37), or second-order concomitant fields (1,(3)(4)(5). Note that simultaneous correction during image reconstruction of off-resonance, gradient nonlinearity, and second-order concomitant fields were demonstrated in our experimental results.…”

“…As a reference, retrospective phase correction was also performed on the phase image reconstructed from data acquired without gradient pre‐emphasis by calculating and subtracting the phase accumulation from the first‐order concomitant fields induced by the gradient lobe. The phantom and brain spiral data were both reconstructed onto 256 × 256 image matrices using a noniterative, nonuniform fast Fourier transform (NUFFT)‐based reconstruction framework with simultaneous gradient nonlinearity and off‐resonance corrections . Retrospective correction for the first‐order concomitant fields on the data acquired without concomitant field pre‐emphasis were also performed.…”

Rapid compressed sensing reconstruction of 3D non‐Cartesian MRI

“…Further, the parallelizability of Toeplitz and NUFFT approaches differ, which may have consequences on run-times. Interestingly, while the Toeplitz method was first reported more than a decade ago, many recent studies still report using NUFFT during iterations (10)(11)(12). Here, we investigate the speed of the two approaches for various NUFFT kernel sizes and oversampling ratios for both GPU and CPU implementations.…”

Image-based gradient non-linearity characterization to determine higher-order spherical harmonic coefficients for improved spatial position accuracy in magnetic resonance imaging

“…If gradient nonlinearity is not accounted for during image reconstruction, the generated MR images will exhibit geometric spatial distortion. This distortion can be retrospectively corrected after via image-based interpolation[11], or circumvented by using a model-based inverse problems framework that prospectively accounts for GNL[12–14]. If GNL is not accounted for in any manner, however, the generated images will exhibit severe geometric spatial distortion, which typically manifests as either a “barrel” or “pincushion” type of effect.…”