Reconstruction by calibration over tensors for multi‐coil multi‐acquisition balanced SSFP imaging

JUST-Net: Jointly unrolled cross-domain optimization based spatio-temporal reconstruction network for accelerated 3D myelin water imaging

PURPOSE

We introduced a novel reconstruction network, jointly unrolled cross-domain optimization-based spatio-temporal reconstruction network (JUST-Net), aimed at accelerating 3D multi-echo gradient-echo (mGRE) data acquisition and improving the quality of resulting myelin water imaging (MWI) maps.

METHOD

An unrolled cross-domain spatio-temporal reconstruction network was designed. The main idea is to combine frequency and spatio-temporal image feature representations and to sequentially implement convolution layers in both domains. The k-space subnetwork utilizes shared information from adjacent frames, whereas the image subnetwork applies separate convolutions in both spatial and temporal dimensions. The proposed reconstruction network was evaluated for both retrospectively and prospectively accelerated acquisition. Furthermore, it was assessed in simulation studies and real-world cases with k-space corruptions to evaluate its potential for motion artifact reduction.

RESULTS

The proposed JUST-Net enabled highly reproducible and accelerated 3D mGRE acquisition for whole-brain MWI, reducing the acquisition time from fully sampled 15:23 to 2:22 min within a 3-min reconstruction time. The normalized root mean squared error of the reconstructed mGRE images increased by less than 4.0%, and the correlation coefficients for MWI showed a value of over 0.68 when compared to the fully sampled reference. Additionally, the proposed method demonstrated a mitigating effect on both simulated and clinical motion-corrupted cases.

CONCLUSION

The proposed JUST-Net has demonstrated the capability to achieve high acceleration factors for 3D mGRE-based MWI, which is expected to facilitate widespread clinical applications of MWI.

Optimization of flip angle and radiofrequency pulse phase to maximize steady-state magnetization in three-dimensional missing pulse steady-state free precession

Missing pulse (MP) steady-state free precession (SSFP) is a magnetic resonance imaging (MRI) pulse sequence that is highly tolerant to the magnetic field inhomogeneity. In this study, optimal flip angle and radiofrequency (RF) phase scheduling in three-dimensional (3D) MP-SSFP is introduced to maximize the steady-state magnetization while keeping broadband excitation to cover widely distributed frequencies generated by inhomogeneous magnetic fields. Numerical optimization based on extended phase graph (EPG) simulation was performed to maximize the MP-SSFP steady-state magnetization. To limit the specific absorption rate (SAR) associated with the broadband excitation in 3D MP-SSFP, SAR constraint was introduced in the numerical optimization. Optimized flip angle and RF phase settings were experimentally tested by introducing a linear inhomogeneous magnetic field in a range of 10-20 mT/m and using a phantom with known T1/T2 relaxation and diffusion parameters at 3 T. The experimental results were validated through comparisons with EPG simulation. Image contrasts and molecular diffusion effects were investigated in in vivo human brain imaging with 3D MP-SSFP with the optimal flip angle and RF phase settings. In the phantom measurements, the optimal flip angle and RF phase settings improved the MP-SSFP steady-state magnetization/signal-to-noise ratio by up to 41% under the fixed SAR conditions, which matched well with EPG simulation results. In vivo brain imaging with the optimal RF pulse settings provided T2-like image contrasts. Diffusion effects were relatively minor with the linear inhomogeneous field of 10-20 mT/m for white and gray matter, but cerebrospinal fluid showed conspicuous signal intensity attenuation as the linear inhomogeneous field increased. Numerical optimization achieved significant improvement in the steady-state magnetization in MP-SSFP compared with the RF pulse settings used in previous studies. The proposed flip angle and RF phase optimization is promising to improve 3D MP-SSFP image quality for MRI in inhomogeneous magnetic fields.

Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction

PURPOSE

To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL).

METHODS

We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 min for 2 mm × 2 mm × 2 mm $2\ {\rm mm} \times 2\ {\rm mm} \times 2\ {\rm mm}$ 3D coverage.

RESULTS

The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean-square error and high-frequency error norm values of the reconstruction with high similarity to the fully sampled MWI.

CONCLUSION

Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE-based MWI.

Phase-cycled balanced SSFP imaging for non-contrast-enhanced functional lung imaging

PURPOSE

To introduce phase-cycled balanced SSFP (bSSFP) acquisition as an alternative in Fourier decomposition MRI for improved robustness against field inhomogeneities.

METHODS

Series 2D dynamic lung images were acquired in 5 healthy volunteers at 1.5 T and 3 T using bSSFP sequence with multiple RF phase increments and compared with conventional single RF phase increment acquisitions. The approach was evaluated based on functional map homogeneity analysis, while ensuring image and functional map quality by means of SNR and contrast-to-noise ratio analyses.

RESULTS

At both field strengths, functional maps obtained with phase-cycled acquisitions displayed improved robustness against local signal losses compared with single-phase acquisitions. The coefficient of variation (mean ± SD, across volunteers) measured in the ventilation maps resulted in 29.7 ± 2.6 at 1.5 T and 37.5 ± 3.1 at 3 T for phase-cycled acquisitions, compared with 39.9 ± 5.2 at 1.5 T and 49.5 ± 3.7 at 3 T for single-phase acquisitions, indicating a significant improvement ( p < 0.05 $$ p<0.05 $$ ) in ventilation map homogeneity.

CONCLUSIONS

Phase-cycled bSSFP acquisitions improve robustness against field inhomogeneity artifacts and significantly improve ventilation map homogeneity at both field strengths. As such, phase-cycled bSSFP may serve as a robust alternative in lung function assessments.

Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging

In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.

Constrained Ellipse Fitting for Efficient Parameter Mapping With Phase-Cycled bSSFP MRI

Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field inhomogeneity and nonstandard [Formula: see text]-weighted tissue contrast. To address these limitations, multiple bSSFP images of the same anatomy are commonly acquired with a set of different RF phase-cycling increments. Joint processing of phase-cycled acquisitions serves to mitigate sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions were also leveraged to estimate relaxation parameters based on explicit signal models. While effective, these model-based methods often involve a large number of acquisitions (N ≈ 10-16), degrading scan efficiency. Here, we propose a new constrained ellipse fitting method (CELF) for parameter estimation with improved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on the elliptical signal model framework for complex bSSFP signals; and it introduces geometrical constraints on ellipse properties to improve estimation efficiency, and dictionary-based identification to improve estimation accuracy. CELF generates maps of [Formula: see text], [Formula: see text], off-resonance and on-resonant bSSFP signal by employing a separate [Formula: see text] map to mitigate sensitivity to flip angle variations. Our results indicate that CELF can produce accurate off-resonance and banding-free bSSFP maps with as few as N = 4 acquisitions, while estimation accuracy for relaxation parameters is notably limited by biases from microstructural sensitivity of bSSFP imaging.

Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks

PURPOSE

Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T₁ , T₂ , and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI.

METHODS

The JPI and JDL methods are extended and combined to improve reconstruction for better-quality, synthesized images. JPI is performed as a first step to estimate the missing k-space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS-Net) is modified and extended to form a joint variable splitting network (JVS-Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under-sampling using acceleration factors between 4 and 8.

RESULTS

It is demonstrated that the normalized root-mean-square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast-weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts.

CONCLUSION

Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

INTRODUCTION

The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming.

MATERIALS AND METHODS

A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based receiver coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T receiver coil sensitivity maps) are thoroughly assessed for 3T receiver coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T receiver coil sensitivity maps.

RESULT AND CONCLUSION

Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the receiver coil sensitivity maps estimated by the proposed method.

Factorized sensitivity estimation for artifact suppression in phase-cycled bSSFP MRI

OBJECTIVE

Balanced steady-state free precession (bSSFP) imaging suffers from banding artifacts in the presence of magnetic field inhomogeneity. The purpose of this study is to identify an efficient strategy to reconstruct banding-free bSSFP images from multi-coil multi-acquisition datasets.

METHOD

Previous techniques either assume that a naïve coil-combination is performed a priori resulting in suboptimal artifact suppression, or that artifact suppression is performed for each coil separately at the expense of significant computational burden. Here we propose a tailored method that factorizes the estimation of coil and bSSFP sensitivity profiles for improved accuracy and/or speed.

RESULTS

In vivo experiments show that the proposed method outperforms naïve coil-combination and coil-by-coil processing in terms of both reconstruction quality and time.

CONCLUSION

The proposed method enables computationally efficient artifact suppression for phase-cycled bSSFP imaging with modern coil arrays. Rapid imaging applications can efficiently benefit from the improved robustness of bSSFP imaging against field inhomogeneity.

Prospective GIRF-based RF phase cycling to reduce eddy current-induced steady-state disruption in bSSFP imaging

Purpose

To propose an explicit Balanced steady‐state free precession (bSSFP) signal model that predicts eddy current‐induced steady‐state disruptions and to provide a prospective, practical, and general eddy current compensation method.

Theory and Methods

Gradient impulse response functions (GIRF) were used to simulate trajectory‐specific eddy current‐induced phase errors at the end of a repetition block. These phase errors were included in bloch simulations to establish a bSSFP signal model to predict steady‐state disruptions and their corresponding image artifacts. The signal model was embedded in the MR system and used to compensate the phase errors by prospectively modifying the phase cycling scheme of the RF pulse. The signal model and eddy current compensation method were validated in phantom and in vivo experiments. In addition, the signal model was used to analyze pre‐existing eddy current mitigation methods, such as 2D tiny golden angle radial and 3D paired phase encoded Cartesian acquisitions.

Results

The signal model predicted eddy current‐induced image artifacts, with the zeroth‐order GIRF being the primary factor to predict the steady‐state disruption. Prospective RF phase cycling schemes were automatically computed online and considerably reduced eddy current‐induced image artifacts. The signal model provides a direct relationship for the smoothness of k‐space trajectories, which explains the effectiveness of phase encode pairing and tiny golden angle trajectory.

Conclusions

The proposed signal model can accurately predict eddy current‐induced steady‐state disruptions for bSSFP imaging. The signal model can be used to derive the eddy current‐induced phase errors required for trajectory‐specific RF phase cycling schemes, which considerably reduce eddy current‐induced image artifacts.

Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI

The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from the undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method that uses computationally efficient projections onto epigraph sets of the l₁ and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection.

Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo [Formula: see text]-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.

Frequency-modulated SSFP with radial sampling and subspace reconstruction: A time-efficient alternative to phase-cycled bSSFP

PURPOSE

A novel subspace-based reconstruction method for frequency-modulated balanced steady-state free precession (fmSSFP) MRI is presented. In this work, suitable data acquisition schemes, subspace sizes, and efficiencies for banding removal are investigated.

THEORY AND METHODS

By combining a fmSSFP MRI sequence with a 3D stack-of-stars trajectory, scan efficiency is maximized as spectral information is obtained without intermediate preparation phases. A memory-efficient reconstruction routine is implemented by introducing the low-frequency Fourier transform as a subspace which allows for the formulation of a convex reconstruction problem. The removal of banding artifacts is investigated by comparing the proposed acquisition and reconstruction technique to phase-cycled bSSFP MRI. Aliasing properties of different undersampling schemes are analyzed and water/fat separation is demonstrated by reweighting the reconstructed subspace coefficients to generate virtual spectral responses in a post-processing step.

RESULTS

A simple root-of-sum-of-squares combination of the reconstructed subspace coefficients yields high-SNR images with the characteristic bSSFP contrast but without banding artifacts. Compared to Golden-Angle trajectories, turn-based sampling schemes were superior in minimizing aliasing across reconstructed subspace coefficients. Water/fat separated images of the human knee were obtained by reweighting subspace coefficients.

CONCLUSIONS

The novel subspace-based fmSSFP MRI technique emerges as a time-efficient alternative to phase-cycled bSFFP. The method does not need intermediate preparation phases, offers high SNR and avoids banding artifacts. Reweighting of the reconstructed subspace coefficients allows for generating virtual spectral responses with applications to water/fat separation.