Variable-density Spiral Trajectory Research Articles

HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.

Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05). In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging. HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.

Deep residual network for highly accelerated fMRI reconstruction using variable density spiral trajectory

Compressed sensing has proved itself as a useful technique for accelerating time-consuming fMRI acquisition. However, its intrinsic iterative algorithm of solving optimization problems limits its practical usage. In addition, it may still suffer from residual errors and artifacts when more aggressive acceleration factors are adopted. Deep neural networks have recently shown great potential in computer vision and image processing. Nevertheless, few attempts have been made concerning fMRI reconstruction. In this paper, we propose a deep residual network for faster and better image reconstruction with 20x acceleration. The network is made up of various residual blocks, and is trained to identify the mapping relationship between an aliased image and a fully-recovered image. Mean square error criterion refined with data-consistency loss is employed to evaluate the ‘distance’ between the network output and ground truth. Results showed that the proposed method can achieve superior image quality and better preserves dynamic features than other state-of-art methods. In addition, the reconstruction can be extremely fast with a one-way deployment on a feed-forward network.

SPARKLING: variable-density k-space filling curves for accelerated T2* -weighted MRI.

To present a new optimition-driven design of optimal k-space trajectories in the context of compressed sensing: Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING). The SPARKLING algorithm is a versatile method inspired from stippling techniques that automatically generates optimized sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These non-Cartesian sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples (e.g., variable density) and a locally uniform k-space coverage. Ex vivo and in vivo prospective -weighted acquisitions were performed on a 7-Tesla scanner using the SPARKLING trajectories for various setups and target densities. Our method was compared to radial and variable-density spiral trajectories for high-resolution imaging. Combining sampling efficiency with compressed sensing, the proposed sampling patterns allowed up to 20-fold reductions in MR scan time (compared to fully sampled Cartesian acquisitions) for two-dimensional -weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains for a high in-plane resolution of 390μm. In comparison to existing non-Cartesian sampling strategies, the proposed technique also yielded superior image quality. The proposed optimization-driven design of k-space trajectories is a versatile framework that is able to enhance MR sampling performance in the context of compressed sensing.

Fast magnetic resonance fingerprinting for dynamic contrast-enhanced studies in mice.

The goal of this study was to develop a fast MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping in DCE-MRI studies in mice. The MRF sequences based on balanced SSFP and fast imaging with steady-state precession were implemented and evaluated on a 7T preclinical scanner. The readout used a zeroth-moment-compensated variable-density spiral trajectory that fully sampled the entire k-space and the inner 10 × 10 k-space with 48 and 4 interleaves, respectively. In vitro and in vivo studies of mouse brain were performed to evaluate the accuracy of MRF measurements with both fully sampled and undersampled data. The application of MRF to dynamic T1 and T2 mapping in DCE-MRI studies were demonstrated in a mouse model of heterotopic glioblastoma using gadolinium-based and dysprosium-based contrast agents. The T1 and T2 measurements in phantom showed strong agreement between the MRF and the conventional methods. The MRF with spiral encoding allowed up to 8-fold undersampling without loss of measurement accuracy. This enabled simultaneous T1 and T2 mapping with 2-minute temporal resolution in DCE-MRI studies. Magnetic resonance fingerprinting provides the opportunity for dynamic quantification of contrast agent distribution in preclinical tumor models on high-field MRI scanners.

Variable density sampling and non-Cartesian super-resolved reconstruction for spatiotemporally encoded single-shot MRI

Spatiotemporally encoded (SPEN) single-shot MRI is an emerging ultrafast technique, which is capable of spatially selective acquisition and reduced field-of-view imaging. Compared to uniform sampling, variable density sampling has great potential in reducing aliasing artifacts and improving sampling efficiency. In this study, variable density spiral trajectory and non-Cartesian super-resolved (SR) reconstruction method are developed for SPEN MRI. The gradient waveforms design of spiral trajectory is mathematically described as an optimization problem subjected to the limitations of hardware. Non-Cartesian SR reconstruction with specific gridding method is developed to retrieve a resolution enhanced image from raw SPEN data. The robustness and efficiency of the proposed methods are demonstrated by numerical simulation and various experiments. The results indicate that variable density SPEN MRI can provide better spatial resolution and fewer aliasing artifacts compared to Cartesian counterpart. The proposed methods will facilitate the development of variable density SPEN MRI.

Multiscale reconstruction for MR fingerprinting.

To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc.

Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

To accelerate the acquisition of simultaneously high spatial and angular resolution diffusion imaging. Accelerated imaging is achieved by recovering the diffusion signal at all voxels simultaneously from under-sampled k-q space data using a compressed sensing algorithm. The diffusion signal at each voxel is modeled as a sparse complex Gaussian mixture model. The joint recovery scheme enables incoherent under-sampling of the 5-D k-q space, obtained by randomly skipping interleaves of a multishot variable density spiral trajectory. This sampling and reconstruction strategy is observed to provide considerably improved reconstructions than classical k-q under-sampling and reconstruction schemes. The complex model enables to account for the noise statistics without compromising the computational efficiency and theoretical convergence guarantees. The reconstruction framework also incorporates compensation of motion induced phase errors that result from the multishot acquisition. Reconstructions of the diffusion signal from under-sampled data using the proposed method yields accurate results with errors less that 5% for different accelerations and b-values. The proposed method is also shown to perform better than standard k-q acceleration schemes. The proposed scheme can significantly accelerate the acquisition of high spatial and angular resolution diffusion imaging by accurately reconstructing crossing fiber architectures from under-sampled data.

High‐resolution multishot spiral diffusion tensor imaging with inherent correction of motion‐induced phase errors

To develop and compare three novel reconstruction methods designed to inherently correct for motion-induced phase errors in multishot spiral diffusion tensor imaging without requiring a variable-density spiral trajectory or a navigator echo. The first method simply averages magnitude images reconstructed with sensitivity encoding from each shot, whereas the second and third methods rely on sensitivity encoding to estimate the motion-induced phase error for each shot and subsequently use either a direct phase subtraction or an iterative conjugate gradient algorithm, respectively, to correct for the resulting artifacts. Numerical simulations and in vivo experiments on healthy volunteers were performed to assess the performance of these methods. The first two methods suffer from a low signal-to-noise ratio or from residual artifacts in the reconstructed diffusion-weighted images and fractional anisotropy maps. In contrast, the third method provides high-quality, high-resolution diffusion tensor imaging results, revealing fine anatomical details such as a radial diffusion anisotropy in cortical gray matter. The proposed sensitivity encoding + conjugate gradient method can inherently and effectively correct for phase errors, signal loss, and aliasing artifacts caused by both rigid and nonrigid motion in multishot spiral diffusion tensor imaging, without increasing the scan time or reducing the signal-to-noise ratio.

Improved first‐pass spiral myocardial perfusion imaging with variable density trajectories

To develop and evaluate variable-density spiral first-pass perfusion pulse sequences for improved efficiency and off-resonance performance and to demonstrate the utility of an apodizing density compensation function (DCF) to improve signal-to-noise ratio (SNR) and reduce dark-rim artifact caused by cardiac motion and Gibbs Ringing. Three variable density spiral trajectories were designed, simulated, and evaluated in 18 normal subjects, and in eight patients with cardiac pathology on a 1.5T scanner. By using a DCF, which intentionally apodizes the k-space data, the sidelobe amplitude of the theoretical point spread function (PSF) is reduced by 68%, with only a 13% increase in the full-width at half-maximum of the main-lobe when compared with the same data corrected with a conventional variable-density DCF, and has an 8% higher resolution than a uniform density spiral with the same number of interleaves and readout duration. Furthermore, this strategy results in a greater than 60% increase in measured SNR when compared with the same variable-density spiral data corrected with a conventional DCF (P < 0.01). Perfusion defects could be clearly visualized with minimal off-resonance and dark-rim artifacts. Variable-density spiral pulse sequences using an apodized DCF produce high-quality first-pass perfusion images with minimal dark-rim and off-resonance artifacts, high SNR and contrast-to-noise ratio, and good delineation of resting perfusion abnormalities.

Adenosine stress CMR with spiral pulse sequences accurately detect CAD

Summary We demonstrate that adenosine stress CMR using variable density spiral perfusion pulse sequences accurately detects obstructive coronary artery disease. These pulse sequences produce high quality perfusion images with minimal artifacts resulting in high diagnostic accuracy. Background Adenosine stress perfusion imaging with CMR has been limited by motion-induced dark-rim artifacts, which may be mistaken for true perfusion abnormalities. We have previously demonstrated that spiral pulse sequences can produce high quality first-pass perfusion images.[1] We have further improved this spiral technique using high-resolution variable density spiral trajectories and a novel density compensation function which reduces Gibbs ringing.[2] We aimed to test the clinical performance of this improved spiral perfusion pulse sequences with adenosine stress for the detection of obstructive coronary artery disease (CAD). Methods CMR perfusion imaging was performed during adenosine stress (140µg/kg-min) and at rest on a Siemens 1.5T Avanto scanner in 23 subjects scheduled to undergo cardiac catheterization for evaluation of chest pain. Subjects with prior coronary artery bypass surgery were excluded. Perfusion images were acquired during injection of 0.1mmol/kg Gd-DTPA at 3 short-axis locations using a saturation recovery (SR) interleaved variable-density spiral pulse sequence with an integrated field-map for off-resonance correction during reconstruction. Sequence parameters included: SR time 80 ms, FOV 320-340mm 2 ,n ominal resolution 2.0 mm 2 ,8 spiral interleaves, FA 30 0 , TR/TE 10ms/1ms. Cine and late gadolinium enhanced (LGE) images were also obtained using standard methodology. All subjects underwent cardiac catheterization following the CMR and significant stenosis was defined as >50%. Two blinded reviewers evaluated the spiral perfusion images for the presence of adenosine-induced perfusion abnormalities. Image quality was graded on a 5 point scale (1 - poor to 5- excellent). Results Figure 1 shows stress and rest spiral perfusion images from a subject who had normal cardiac function and no LGE on his CMR study. These high quality images show a reversible perfusion abnormality in the anterior wall and anteroseptum. This patient had a 90% stenosis in his LAD at cardiac catheterization. Overall the prevalence of obstructive CAD was 65% and LGE was present in 35% of the patients. The average image quality score was 4.2±0.8 with no studies showing more than minimal dark rim artifact. The average sensitivity, specificity, and overall accuracy of the two readers were 87%, 94%, and 89% respectively. There was very good inter-reader reliability with a kappa statistic of 0.73. Conclusions Spiral adenosine stress CMR results in high diagnostic accuracy for the detection of obstructive coronary artery disease with excellent image quality. Funding

Inherent correction of motion‐induced phase errors in multishot spiral diffusion‐weighted imaging

Multishot spiral imaging is a promising alternative to echo-planar imaging for high-resolution diffusion-weighted imaging and diffusion tensor imaging. However, subject motion in the presence of diffusion-weighting gradients causes phase inconsistencies among different shots, resulting in signal loss and aliasing artifacts in the reconstructed images. Such artifacts can be reduced using a variable-density spiral trajectory or a navigator echo, however at the cost of a longer scan time. Here, a novel iterative phase correction method is proposed to inherently correct for the motion-induced phase errors without requiring any additional scan time. In this initial study, numerical simulations and in vivo experiments are performed to demonstrate that the proposed method can effectively and efficiently correct for spatially linear phase errors caused by rigid-body motion in multishot spiral diffusion-weighted imaging of the human brain.

${K}$-Space and Image-Space Combination for Motion-Induced Phase-Error Correction in Self-Navigated Multicoil Multishot DWI

Motion during diffusion encodings leads to different phase errors in different shots of multishot diffusion-weighted acquisitions. Phase error incoherence among shots results in undesired signal cancellation when data from all shots are combined. Motion-induced phase error correction for multishot diffusion-weighted imaging (DWI) has been studied extensively and there exist multiple phase error correction algorithms. A commonly used correction method is the direct phase subtraction (DPS). DPS, however, can suffer from incomplete phase error correction due to the aliasing of the phase errors in the high spatial resolution phases. Furthermore, improper sampling density compensation is also a possible issue of DPS. Recently, motion-induced phase error correction was incorporated in the conjugate gradient (CG) image reconstruction procedure to get a nonlinear phase correction method that is also applicable to parallel DWI. Although the CG method overcomes the issues of DPS, its computational requirement is high. Further, CG restricts to sensitivity encoding (SENSE) for parallel reconstruction. In this paper, a new time-efficient and flexible k-space and image-space combination (KICT) algorithm for rigid body motion-induced phase error correction is introduced. KICT estimates the motion-induced phase errors in image space using the self-navigated capability of the variable density spiral trajectory. The correction is then performed in k -space. The algorithm is shown to overcome the problem of aliased phase errors. Further, the algorithm preserves the phase of the imaging object and receiver coils in the corrected k -space data, which is important for parallel imaging applications. After phase error correction, any parallel reconstruction method can be used. The KICT algorithm is tested with both simulated and in vivo data with both multishot single-coil and multishot multicoil acquisitions. We show that KICT correction results in diffusion-weighted images with higher signal-to-noise ratio (SNR) and fractional anisotropy (FA) maps with better resolved fiber tracts as compared to DPS. In peripheral-gated acquisitions, KICT is comparable to the CG method.

213 Optimally undersampled variable density spiral trajectories applied to real-time cardiac MRI at 3 Tesla

213 Optimally undersampled variable density spiral trajectories applied to real-time cardiac MRI at 3 Tesla

Spatial excitation using variable-density spiral trajectories.

To examine the usefulness of variable-density k-space trajectories for the design of multi-dimensional spatially selective RF pulses. Experimental phantom and in vivo studies were performed and confirmed by simulations. Two-dimensional spatially selective magnetization patterns were excited using variable-density spiral trajectories and analyzed with respect to the signal excitation outside the excitation field of view (FOX). By using variable-density trajectories, signal excitation outside the FOX was drastically reduced compared to trajectories with a uniform density, while maintaining fairly short pulse durations. A main advantage of the method is that unwanted signal excitation outside the nominal FOX can be reduced without significantly increasing the duration of the RF excitation pulse. The variable-density approach is useful for all applications that require a well-defined spatial excitation profile, e.g., to perform imaging in a reduced field of view (FOV), for spatial saturation pulses, for curved slice imaging or in MR spectroscopy.

Efficient off-resonance correction for spiral imaging.

A new spiral imaging technique incorporates the acquisition of a field map into imaging interleaves. Variable density spiral trajectories are designed to oversample the central region of k-space, and interleaves are acquired at two different echo times. A field map is extracted from this data and multifrequency reconstruction is used to form an off-resonance corrected image using the entire dataset. Simulation, phantom, and in vivo results indicate that this technique can be used to achieve higher image and/or field map spatial resolution compared to conventional techniques. Magn Reson Med 45:521-524, 2001.