Dynamic field map estimation using a spiral-in/spiral-out acquisition

Zero-echo-time sequences in highly inhomogeneous fields

PURPOSE

Zero-echo-time (ZTE) sequences have proven a powerful tool for MRI of ultrashortT 2 $$ {T}_2 $$ tissues, but they fail to produce useful images in the presence of strong field inhomogeneities (14 000 ppm). Here we seek a method to correct reconstruction artifacts from non-Cartesian acquisitions in highly inhomogeneousB 0 $$ {\mathrm{B}}_0 $$ , where the standard double-shot gradient-echo approach to field mapping fails.

METHODS

We present a technique based on magnetic field maps obtained from two geometric distortion-free point-wise (SPRITE) acquisitions. To this end, we employ three scanners with varying field homogeneities. These maps are used for model-based image reconstruction with iterative algebraic techniques (ART). For comparison, the same prior information is fed also to widely used Conjugate Phase (CP) algorithms.

RESULTS

Distortions and artifacts coming from severeB 0 $$ {\mathrm{B}}_0 $$ inhomogeneities, at the level of the encoding gradient, are largely reverted by our method, as opposed to CP reconstructions. This holds even close to the limit where intra-voxel bandwidths (determined byB 0 $$ {\mathrm{B}}_0 $$ inhomogeneities, up to 1.2 kHz) are comparable to the encoding inter-voxel bandwidth (determined by the gradient fields, 625 Hz in this work).

CONCLUSION

We have benchmarked the performance of a new method for ZTE imaging in highly inhomogeneous magnetic fields. For example, this can be exploited for dental imaging in affordable low-field MRI systems, and can be expanded for arbitrary pulse sequences and extreme magnet geometries, as in, for example, single-sided MRI.

Giving the prostate the boost it needs: Spiral diffusion MRI using a high-performance whole-body gradient system for high b-values at short echo times

PURPOSE

To address key issues of low SNR and image distortions in prostate diffusion MRI (dMRI) by means of using strong gradients, single-shot spiral readouts and an expanded encoding model for image reconstruction.

METHODS

Diffusion-weighted spin echo imaging with EPI and spiral readouts is performed on a whole-body system equipped with strong gradients (up to 250 mT/m). An expanded encoding model including static off-resonance, coil sensitivities, and magnetic field dynamics is employed for image reconstruction. The acquisitions are performed on a phantom and in vivo (one healthy volunteer and one patient with prostate cancer). The resulting images are compared to conventional dMRI EPI with navigator-based image reconstruction and assessed in terms of their congruence, SNR, tissue contrast, and quantitative parameters.

RESULTS

Using the expanded encoding model, high-quality images of the prostate gland are obtained across all b-values (up to 3 ms/μm2), clearly outperforming the results obtained with conventional image reconstruction. Compared to EPI, spiral imaging provides an SNR gain up to 45% within the gland and even higher in the lesion. In addition, prostate dMRI with single-shot spirals at submillimeter in-plane resolution (0.85 mm) is accomplished.

CONCLUSION

The combination of strong gradients and an expanded encoding model enables imaging of the prostate with unprecedented image quality. Replacing the commonly used EPI with spirals provides the inherent benefit of shorter echo times and superior readout efficiency and results in higher SNR, which is in particular relevant for considered applications.

Spherical echo-planar time-resolved imaging (sEPTI) for rapid 3D quantitative T 2 * and susceptibility imaging

PURPOSE

To develop a 3D spherical EPTI (sEPTI) acquisition and a comprehensive reconstruction pipeline for rapid high-quality whole-brain submillimeterT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification.

METHODS

For the sEPTI acquisition, spherical k-space coverage is utilized with variable echo-spacing and maximum kx ramp-sampling to improve efficiency and signal incoherency compared to existing EPTI approaches. For reconstruction, an iterative rank-shrinking B0 estimation and odd-even high-order phase correction algorithms were incorporated into the reconstruction to better mitigate artifacts from field imperfections. A physics-informed unrolled network was utilized to boost the SNR, where 1-mm and 0.75-mm isotropic whole-brain imaging were performed in 45 and 90 s at 3 T, respectively. These protocols were validated through simulations, phantom, and in vivo experiments. Ten healthy subjects were recruited to provide sufficient data for the unrolled network. The entire pipeline was validated on additional five healthy subjects where different EPTI sampling approaches were compared. Two additional pediatric patients with epilepsy were recruited to demonstrate the generalizability of the unrolled reconstruction.

RESULTS

sEPTI achieved 1.4× $$ \times $$ faster imaging with improved image quality and quantitative map precision compared to existing EPTI approaches. The B0 update and the phase correction provide improved reconstruction performance with lower artifacts. The unrolled network boosted the SNR, achieving high-qualityT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification with single average data. High-quality reconstruction was also obtained in the pediatric patients using this network.

CONCLUSION

sEPTI achieved whole-brain distortion-free multi-echo imaging andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification at 0.75 mm in 90 s which has the potential to be useful for wide clinical applications.

Joint [Formula: see text] and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning

OBJECTIVE

We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0  ∼  50 mT) MRI.

METHODS

Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the B0-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees.

RESULTS

We compare our model to different model-based approaches at distinct noise levels and various B0-field distributions. Our results show that our physics-informed neural network approach outperforms the purely model-based methods by improving the PSNR up to 11.7% and the RMSE up to 86.3%.

CONCLUSION

Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and B0-field maps in the low-field regime.

SIGNIFICANCE

low-field MRI is becoming increasingly more popular as it enables access to MR in challenging situations such as intensive care units or resource poor areas. Our method allows for fast and accurate image reconstruction in such low-field imaging with B0-inhomogeneity compensation under a wide range of various environmental conditions.

Off-resonance artifact correction for MRI: A review

In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low-field and/or portable MRI, we also highlight the advantages and challenges of imaging at low field with respect to off-resonance.

Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction

This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R2∗ , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k -space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps. The proposed free-breathing radial technique was tested on a water/fat & iron phantom, a young volunteer, and obesity/diabetes/hepatic steatosis patients. Quantitative fat fraction and R2∗ accuracy were confirmed by comparing our technique with the reference breath-hold Cartesian scan. The multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. Moreover, the proposed motion-resolved model-based reconstruction allows for free-breathing liver fat and R2∗ quantification in multiple motion states. Overall, our proposed technique offers a convenient tool for non-invasive liver assessment with no breath holding requirement.

High-resolution dynamic susceptibility contrast perfusion imaging using higher-order temporal smoothness regularization

PURPOSE

To improve image quality and resolution of dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) by developing acquisition and reconstruction methods exploiting the temporal regularity property of DSC-PWI signal.

THEORY AND METHODS

A novel regularized reconstruction is proposed that recovers DSC-PWI series from interleaved segmented spiral k-space acquisition using higher order temporal smoothness (HOTS) properties of the DSC-PWI signal. The HOTS regularization is designed to tackle representational insufficiency of the standard first-order temporal regularizations for supporting higher accelerations. The higher accelerations allow for k-space coverage with shorter spiral interleaves resulting in improved acquisition point spread function, and acquisition of images at multiple TEs for more accurate DSC-PWI analysis.

RESULTS

The methods were evaluated in simulated and in-vivo studies. HOTS regularization provided increasingly more accurate models for DSC-PWI than the standard first-order methods with either quadratic or robust norms at the expense of increased noise. HOTS DSC-PWI optimized for noise and accuracy demonstrated significant advantages over both spiral DSC-PWI without temporal regularization and traditional echo-planar DSC-PWI, improving resolution and mitigating image artifacts associated with long readout, including blurring and geometric distortions. In context of multi-echo DSC-PWI, the novel methods allowed ∼4.3× decrease of voxel volume, providing 2× number of TEs compared to the previously published results.

CONCLUSIONS

Proposed HOTS reconstruction combined with dynamic spiral sampling represents a valid mechanism for improving image quality and resolution of DSC-PWI significantly beyond those available with established fast imaging techniques.

Estimation of undistorted images in brain echo-planar images with distortions using the conjugate gradient method with anatomical regularization

PURPOSE

Although echo-planar imaging (EPI) is widely used for diffusion magnetic resonance (MR) imaging, EPI images suffer from susceptibility-induced geometric distortions. We herein propose a new estimation method for undistorted EPI images using anatomical T₁ -weighted images (T₁ WIs) based on the physics of MR imaging.

METHODS

Our proposed method estimates the undistorted EPI image in the image domain while estimating the magnetic field inhomogeneity map using the conjugate gradient method with anatomical regularization. Our method synthesizes the distorted image to match the measured EPI image containing geometric distortions by alternately updating the undistorted EPI image and the magnetic field inhomogeneity map. We evaluated our proposed method and compared it with a nonrigid registration-based distortion correction method using simulated data and using real data. In the evaluation of the estimation of the magnetic field inhomogeneity map, we used the normalized root-mean-squared error (NRMSE) between the estimated results and the ground truth. In the evaluation of the estimation of undistorted images, we used mutual information (MI) between the undistorted EPI image and the anatomical T₁ WI.

RESULTS

Using the simulated data, the means and standard deviations of the NRMSE values in the nonrigid registration-based method and proposed method were 1.29 ± 0.63 and 0.64 ± 0.30, respectively. The MI values in the proposed method were larger than those in the nonrigid registration-based method in all evaluated slices. For the real data, the proposed method improved the distortion, and the MI values in the proposed method were larger than those in the nonrigid registration-based method. In the estimation of the magnetic field inhomogeneity map, the NRMSE values in our method were smaller than those in the nonrigid registration-based method.

CONCLUSIONS

We demonstrated that our proposed method can estimate the regions with compressed distortions that are not well represented by the nonrigid registration-based methods. The results suggest that the proposed method could be useful in analyses combining EPI images with T₁ WIs.

Iterative static field map estimation for off-resonance correction in non-Cartesian susceptibility weighted imaging

Purpose

Patient‐induced inhomogeneities in the magnetic field cause distortions and blurring during acquisitions with long readouts such as in susceptibility‐weighted imaging (SWI). Most correction methods require collecting an additional ΔB0 field map to remove these artifacts.

Theory

The static ΔB0 field map can be approximated with an acceptable error directly from a single echo acquisition in SWI. The main component of the observed phase is linearly related to ΔB0 and the echo time (TE), and the relative impact of non‐ ΔB0 terms becomes insignificant with TE >20 ms at 3 T for a well‐tuned system.

Methods

The main step is to combine and unfold the multi‐channel phase maps wrapped many times, and several competing algorithms are compared for this purpose. Four in vivo brain data sets collected using the recently proposed 3D spreading projection algorithm for rapid k‐space sampling (SPARKLING) readouts are used to assess the proposed method.

Results

The estimated 3D field maps generated with a 0.6 mm isotropic spatial resolution provide overall similar off‐resonance corrections compared to reference corrections based on an external ΔB0 acquisitions, and even improved for 2 of 4 individuals. Although a small estimation error is expected, no aftermath was observed in the proposed corrections, whereas degradations were observed in the references.

Conclusion

A static ΔB0 field map estimation method was proposed to take advantage of acquisitions with long echo times, and outperformed the reference technique based on an external field map. The difference can be attributed to an inherent robustness to mismatches between volumes and external ΔB0 maps, and diverse other sources investigated.

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Data‐driven motion‐corrected brain MRI incorporating pose‐dependent B 0 fields

Purpose

To develop a fully data‐driven retrospective intrascan motion‐correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose‐dependent changes in polarizing magnetic (B₀) fields.

Theory and Methods

Tissue susceptibility induces spatially varying B₀ distributions in the head, which change with pose. A physics‐inspired B₀ model has been deployed to model the B₀ variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B₀ variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo.

Results

The applicability of the physics‐inspired B₀ model was confirmed in vivo. Simulations show the need to include the pose‐dependent B₀ fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B₀ evolution from the acquired data. The proposed motion and B₀ correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo.

Conclusion

We have developed a motion‐correction framework that accounts for and estimates pose‐dependent B₀ fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B₀ dependencies cannot be neglected. The use of a compact physics‐inspired B₀ model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.

Fast 3D MR elastography of the whole brain using spiral staircase: Data acquisition, image reconstruction, and joint deblurring

PURPOSE

To address the need for a method to acquire 3D data for MR elastography (MRE) of the whole brain with substantially improved spatial resolution, high SNR, and reduced acquisition time compared with conventional methods.

METHODS

We combined a novel 3D spiral staircase data-acquisition method with a spoiled gradient-echo pulse sequence and MRE motion-encoding gradients (MEGs). The spiral-out acquisition permitted use of longer-duration motion-encoding gradients (ie, over two full oscillatory cycles) to enhance displacement SNR, while still maintaining a reasonably short TE for good phase-SNR. Through-plane parallel imaging with low noise penalties was implemented to accelerate acquisition along the slice direction. Shared anatomical information was exploited in the deblurring procedure to further boost SNR for stiffness inversion.

RESULTS

In vivo and phantom experiments demonstrated the feasibility of the proposed method in producing brain MRE results comparable to the spin-echo-based approaches, both qualitatively and quantitatively. High-resolution (2-mm isotropic) brain MRE data were acquired in 5 minutes using our method with good SNR. Joint deblurring with shared anatomical information produced SNR-enhanced images, leading to upward stiffness estimation.

CONCLUSION

A novel 3D gradient-echo-based approach has been designed and implemented, and shown to have promising potential for fast and high-resolution in vivo MRE of the whole brain.

Joint image and field map estimation for multi-echo hyperpolarized 13 C metabolic imaging of the heart

PURPOSE

Image reconstruction of metabolic images from hyperpolarized ¹³ C multi-echo data acquisition is sensitive to susceptibility-induced phase offsets, which are particularly challenging in the heart. A model-based framework for joint estimation of metabolite images and field map from echo shift-encoded data is proposed. Using simulations, it is demonstrated that correction of signal spilling due to incorrect decomposition of metabolites and geometrical distortions over a wide range of off-resonance gradients is possible. In vivo feasibility is illustrated using hyperpolarized [1-¹³ C]pyruvate in the pig heart.

METHODS

The model-based reconstruction for multi-echo, multicoil data was implemented as a nonconvex minimization problem jointly optimizing for metabolic images and B₀ . A comprehensive simulation framework for echo shift-encoded hyperpolarized [1-¹³ C]pyruvate imaging was developed and applied to assess reconstruction performance and distortion correction of the proposed method. In vivo data were obtained in four pigs using hyperpolarized [1-¹³ C]pyruvate on a clinical 3T MR system with a six-channel receiver coil. Dynamic images were acquired during suspended ventilation using cardiac-triggered multi-echo single-shot echo-planar imaging in short-axis orientation.

RESULTS

Simulations revealed that off-resonance gradients up to ±0.26 ppm/pixel can be corrected for with reduced signal spilling and geometrical distortions yielding an accuracy of ≥90% in terms of Dice similarity index. In vivo, improved geometrical consistency (10% Dice improvement) compared to image reconstruction without field map correction and with reference to anatomical data was achieved.

CONCLUSION

Joint image and field map estimation allows addressing off-resonance-induced geometrical distortions and metabolite spilling in hyperpolarized ¹³ C metabolic imaging of the heart.

MULTI-ECHO RECOVERY WITH FIELD INHOMOGENEITY COMPENSATION USING STRUCTURED LOW-RANK MATRIX COMPLETION

Echo-planar imaging (EPI), which is the main workhorse of functional MRI, suffers from field inhomogeneity-induced geometric distortions. The amount of distortion is proportional to the readout duration, which restricts the maximum achievable spatial resolution. The spatially varying nature of the T 2 * decay makes it challenging for EPI schemes with a single echo time to obtain good sensitivity to functional activations in different brain regions. Despite the use of parallel MRI and multislice acceleration, the number of different echo times that can be acquired in a reasonable TR is limited. The main focus of this work is to introduce a rosette-based acquisition scheme and a structured low-rank reconstruction algorithm to overcome the above challenges. The proposed scheme exploits the exponential structure of the time series to recover distortion-free images from several echoes simultaneously.

Joint B0 and image estimation integrated with model based reconstruction for field map update and distortion correction in prostate diffusion MRI

In prostate Diffusion Weighted MRI, differences in susceptibility values exist at the interface between the prostate and rectal-air. This can result in off-resonance magnetic field leading to geometric distortions including signal stretching and signal pile-up in the reconstructed images. Using a set of EPI data acquired with blip-up and blip-down phase encoding gradient directions, model based reconstruction has recently been proposed that can correct these distortions by using a B0 field estimated from a separate B0 scan. However, change in the size of the rectal air region across time can occur that can result in a mismatch of the B0 field to the EPI scan. Also, the measured B0 field itself can be erroneous in regions of low Signal to Noise ratio around the prostate rectal air interface. In this work, using a set of single shot EPI data acquired with blip-up and blip-down phase encoding gradient directions, a novel joint model based reconstruction is proposed that can account for changes in the off resonance effects between the B0 and EPI scans. For ten prostate patients, using a measured B0 field as an initial B0 estimate, on a 5-point scale (1-5) image quality scores evaluated by an experienced radiologist, the proposed framework achieved scores of 3.50 ± 0.85 and 3.40 ± 0.51 for b-values of 0 and 500 s/mm2, respectively compared to 3.40 ± 0.70 and 3.30 ± 0.67 for model based reconstruction. The proposed framework is also capable of estimating a distortion corrected EPI image even without an initial B0 field estimate in situations where a separate B0 scan cannot be obtained due to time constraint.

Resting State fMRI: Going Through the Motions

Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.

Distortion-free imaging: A double encoding method (DIADEM) combined with multiband imaging for rapid distortion-free high-resolution diffusion imaging on a compact 3T with high-performance gradients

BACKGROUND

Distortion-free, high-resolution diffusion imaging using DIADEM (Distortion-free Imaging: A Double Encoding Method), proposed recently, has great potential for clinical applications. However, it can suffer from prolonged scan times and its reliability for quantitative diffusion imaging has not been evaluated.

PURPOSE

To investigate the clinical feasibility of DIADEM-based high-resolution diffusion imaging on a novel compact 3T (C3T) by evaluating the reliability of quantitative diffusion measurements and utilizing both the high-performance gradients (80 mT/m, 700 T/m/s) and the sequence optimization with the navigator acquisition window reduction and simultaneous multislice (multiband) imaging.

STUDY TYPE

Prospective feasibility study.

PHANTOM/SUBJECTS

Diffusion quality control phantom scans to evaluate the reliability of quantitative diffusion measurements; 36 normal control scans for B₀ -field mapping; six healthy and two patient subject scans with a brain tumor for comparisons of diffusion and anatomical imaging.

FIELD STRENGTH/SEQUENCE

3T; the standard single-shot echo-planar-imaging (EPI), multishot DIADEM diffusion, and anatomical (2D-FSE [fast-spin-echo], 2D-FLAIR [fluid-attenuated-inversion-recovery], and 3D-MPRAGE [magnetization prepared rapid acquisition gradient echo]) imaging.

ASSESSMENT

The scan time reduction, the reliability of quantitative diffusion measurements, and the clinical efficacy for high-resolution diffusion imaging in healthy control and brain tumor volunteers.

STATISTICAL TEST

Bland-Altman analysis.

RESULTS

The scan time for high in-plane (0.86 mm² ) resolution, distortion-free, and whole brain diffusion imaging were reduced from 10 to 5 minutes with the sequence optimizations. All of the mean apparent diffusion coefficient (ADC) values in phantom were within the 95% confidence interval in the Bland-Altman plot. The proposed acquisition with a total off-resonance coverage of 597.2 Hz wider than the expected bandwidth of 500 Hz in human brain could yield a distortion-free image without foldover artifacts. Compared with EPI, therefore, this approach allowed direct image matching with the anatomical images and enabled improved delineation of the tumor boundaries.

DATA CONCLUSION

The proposed high-resolution diffusion imaging approach is clinically feasible on C3T due to a combination of hardware and sequence improvements.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;51:296-310.

Calibration-Free B0 Correction of EPI Data Using Structured Low Rank Matrix Recovery

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in echo planar imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time. Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements. The temporal profile at each pixel is modeled as a single exponential, which is exploited to fill in the missing entries. We show that the exponential behavior at each pixel, along with the spatial smoothness of the exponential parameters, can be exploited to derive a 3-D annihilation relation in the Fourier domain. This relation translates to a low rank property on a structured multi-fold Toeplitz matrix, whose entries correspond to the measured k-space samples. We introduce a fast two-step algorithm for the completion of the Toeplitz matrix from the available samples. In the first step, we estimate the null space vectors of the Toeplitz matrix using only its fully sampled rows. The null space is then used to estimate the signal subspace, which facilitates the efficient recovery of the time series of images. We finally demonstrate the proposed approach on spherical MR phantom data and human data and show that the artifacts are significantly reduced.

Maximising BOLD sensitivity through automated EPI protocol optimisation

Gradient echo echo-planar imaging (GE EPI) is used for most fMRI studies but can suffer substantially from image distortions and BOLD sensitivity (BS) loss due to susceptibility-induced magnetic field inhomogeneities. While there are various post-processing methods for correcting image distortions, signal dropouts cannot be recovered and therefore need to be addressed at the data acquisition stage. Common approaches for reducing susceptibility-related BS loss in selected brain areas are: z-shimming, inverting the phase encoding (PE) gradient polarity, optimizing the slice tilt and increasing spatial resolution. The optimization of these parameters can be based on atlases derived from multiple echo-planar imaging (EPI) acquisitions. However, this requires resource and time, which imposes a practical limitation on the range over which parameters can be optimised meaning that the chosen settings may still be sub-optimal. To address this issue, we have developed an automated method that can be used to optimize across a large parameter space. It is based on numerical signal simulations of the BS loss predicted by physical models informed by a large database of magnetic field (B₀) maps acquired on a broad cohort of participants. The advantage of our simulation-based approach compared to previous methods is that it saves time and expensive measurements and allows for optimizing EPI protocols by incorporating a broad range of factors, including different resolutions, echo times or slice orientations. To verify the numerical optimisation, results are compared to those from an earlier study and to experimental BS measurements carried out in six healthy volunteers.

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Significant BOLD sensitivity increase by optimization of z-shim, gradient polarity and slice tilt.

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Method based on numerical signal simulations informed by large database of magnetic field maps.

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Saves time and expensive measurements.

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Automated and flexible optimization of multiple EPI parameter settings.

Correcting image blur in spiral, retraced in/out (RIO) acquisitions using a maximized energy objective

PURPOSE

Images acquired with spiral k-space trajectories can suffer from off-resonance image blur. Previous work showed that averaging 2 images acquired with a retraced, in/out (RIO) trajectory self-corrects image blur so long as off-resonant spins accrue less than 1 half-cycle of relative phase over the readout. Practical scenarios frequently exceed this threshold. Here, we derive and characterize a more-robust off-resonance image blur correction method for RIO acquisitions.

METHODS

Phantom and human volunteer data were acquired using a RIO trajectory with readout durations ranging from 4 to 60 ms. The resulting images were deblurred using 3 candidate methods: conventional linear correction of the component images; semiautomatic deblurring of the component images using an established minimized phase objective function; and semiautomatic deblurring of the average of the component images using a maximized energy objective function, derived below. Deblurring errors were estimated relative to images acquired with 4 ms readouts.

RESULTS

All 3 methods converged to similar solutions in cases where less than 2 and 4 cycles of phase accrued over the readout in in vivo and phantom images, respectively (<13 ms readout at 3T). Above this threshold, the linear and minimized phase methods introduced several errors. The maximized energy function provided accurate deblurring so long as less than 6 and 10 cycles of phase accrued over the readout in in vivo and phantom images, respectively (<34 ms readout at 3T).

CONCLUSION

The maximized energy objective function can accurately deblur RIO acquisitions over a wide spectrum of off resonance frequencies.

Fast dynamic ventilation MRI of hyperpolarized 129 Xe using spiral imaging

Purpose

To develop and optimize a rapid dynamic hyperpolarized ¹²⁹Xe ventilation (DXeV) MRI protocol and investigate the feasibility of capturing pulmonary signal‐time curves in human lungs.

Theory and Methods

Spiral k‐space trajectories were designed with the number of interleaves N_int = 1, 2, 4, and 8 corresponding to voxel sizes of 8 mm, 5 mm, 4 mm, and 2.5 mm, respectively, for field of view = 15 cm. DXeV images were acquired from a gas‐flow phantom to investigate the ability of N_int = 1, 2, 4, and 8 to capture signal‐time curves. A finite element model was constructed to investigate gas‐flow dynamics corroborating the experimental signal‐time curves. DXeV images were also carried out in six subjects (three healthy and three chronic obstructive pulmonary disease subjects).

Results

DXeV images and numerical modelling of signal‐time curves permitted the quantification of temporal and spatial resolutions for different numbers of spiral interleaves. The two‐interleaved spiral (N_int = 2) was found to be the most time‐efficient to obtain DXeV images and signal‐time curves of whole lungs with a temporal resolution of 624 ms for 13 slices. Signal‐time curves were well matched in three healthy volunteers. The Spearman's correlations of chronic obstructive pulmonary disease subjects were statistically different from three healthy subjects (P < 0.05).

Conclusion

The N_int = 2 spiral demonstrates the successful acquisition of DXeV images and signal‐time curves in healthy subjects and chronic obstructive pulmonary disease patients. Magn Reson Med 79:2597–2606, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Rapid anatomical brain imaging using spiral acquisition and an expanded signal model

We report the deployment of spiral acquisition for high-resolution structural imaging at 7T. Long spiral readouts are rendered manageable by an expanded signal model including static off-resonance and B₀ dynamics along with k-space trajectories and coil sensitivity maps. Image reconstruction is accomplished by inversion of the signal model using an extension of the iterative non-Cartesian SENSE algorithm. Spiral readouts up to 25 ms are shown to permit whole-brain 2D imaging at 0.5 mm in-plane resolution in less than a minute. A range of options is explored, including proton-density and T₂* contrast, acceleration by parallel imaging, different readout orientations, and the extraction of phase images. Results are shown to exhibit competitive image quality along with high geometric consistency.

An Efficient Reconstruction Algorithm Based on the Alternating Direction Method of Multipliers for Joint Estimation of ${R}_{{2}}^{*}$ and Off-Resonance in fMRI

R*₂ mapping is a useful tool in blood-oxygen-level dependent fMRI due to its quantitative-nature. However, like T*₂-weighted imaging, standard R*₂ mapping based on multi-echo EPI suffers from geometric distortion, due to strong off-resonance near the air-tissue interface. Joint mapping of R*₂ and off-resonance can correct the geometric distortion and is less susceptible to motion artifacts. Single-shot joint mapping of R*₂ and off-resonance is possible with a rosette trajectory due to its frequent sampling of the k-space center. However, the corresponding reconstruction is nonlinear, ill-conditioned, large-scale, and computationally inefficient with current algorithms. In this paper, we propose a novel algorithm for joint mapping of R*₂ and off-resonance, using rosette k-space trajectories. The new algorithm, based on the alternating direction method of multipliers, improves the reconstruction efficiency by simplifying the original complicated cost function into a composition of simpler optimization steps. Compared with a recently developed trust region algorithm, the new algorithm achieves the same accuracy and an acceleration of threefold to sixfold in reconstruction time. Based on the new algorithm, we present simulation and in vivo data from single-shot, double-shot, and quadruple-shot rosettes and demonstrate the improved image quality and reduction of distortions in the reconstructed R*₂ map.

Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.

Suppressing Respiration Effects when Geometric Distortion Is Corrected Dynamically by Phase Labeling for Additional Coordinate Encoding (PLACE) during Functional MRI

Echo planar imaging (EPI) suffers from geometric distortions caused by magnetic field inhomogeneities, which can be time-varying as a result of small amounts of head motion that occur over seconds and minutes during fMRI experiments, also known as “dynamic geometric distortion”. Phase Labeling for Additional Coordinate Encoding (PLACE) is a promising technique for geometric distortion correction without reduced temporal resolution and in principle can be used to correct for motion-induced dynamic geometric distortion. PLACE requires at least two EPI images of the same anatomy that are ideally acquired with no variation in the magnetic field inhomogeneities. However, head motion and lung ventilation during the respiratory cycle can cause changes in magnetic field inhomogeneities within the EPI pair used for PLACE. In this work, we exploited dynamic off-resonance in k-space (DORK) and averaging to correct the within EPI pair magnetic field inhomogeneities; and hence proposed a combined technique (DORK+PLACE+averaging) to mitigate dynamic geometric distortion in EPI-based fMRI while preserving the temporal resolution. The performance of the combined DORK, PLACE and averaging technique was characterized through several imaging experiments involving test phantoms and six healthy adult volunteers. Phantom data illustrate reduced temporal standard deviation of fMRI signal intensities after use of combined dynamic PLACE, DORK and averaging compared to the standard processing and static geometric distortion correction. The combined technique also substantially improved the temporal standard deviation and activation maps obtained from human fMRI data in comparison to the results obtained by standard processing and static geometric distortion correction, highlighting the utility of the approach.

Motion correction in MRI of the brain

Subject motion in MRI is a relevant problem in the daily clinical routine as well as in scientific studies. Since the beginning of clinical use of MRI, many research groups have developed methods to suppress or correct motion artefacts. This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research. It explains the sources and types of motion and related artefacts, classifies and describes existing techniques for motion detection, compensation and correction and lists established and experimental approaches. Retrospective motion correction modifies the MR image data during the reconstruction, while prospective motion correction performs an adaptive update of the data acquisition. Differences, benefits and drawbacks of different motion correction methods are discussed.

Trust Region Methods for the Estimation of a Complex Exponential Decay Model in MRI With a Single-Shot or Multi-Shot Trajectory

Joint estimation of spin density R2* decay and OFF-resonance frequency maps is very useful in many magnetic resonance imaging applications. The standard multi-echo approach can achieve high accuracy but requires a long acquisition time for sampling multiple k-space frames. There are many approaches to accelerate the acquisition. Among them, single-shot or multi-shot trajectory-based sampling has recently drawn attention due to its fast data acquisition. However, this sampling strategy destroys the Fourier relationship between k-space and images, leading to a great challenge for the reconstruction. In this paper, we present two trust region methods based on two different linearization strategies for the nonlinear signal model. A trust region is defined as a local area in the variable space where a local linear approximation is trustable. In each iteration, the method minimizes a local approximation within a trust region so that the step size can be kept in a suitable scale. A continuation scheme is applied to reduce the regularization gradually over the parameter maps and facilitates convergence from poor initializations. The two trust region methods are compared with the two other previously proposed methods--the nonlinear conjugate gradients and the gradual refinement algorithm. Experiments based on various synthetic data and real phantom data show that the two trust region methods have a clear advantage in both speed and stability.

Quantification and correction of respiration induced dynamic field map changes in fMRI using 3D single shot techniques

PURPOSE

Respiration induced dynamic field map changes in the brain are quantified and the influence on the magnitude signal (physiological noise) is investigated. Dynamic off-resonance correction allows to reduce the signal fluctuations overlaying the blood oxygenation level dependent signal in T2*-weighted functional imaging.

THEORY AND METHODS

A single-shot whole brain imaging technique with 100 ms temporal resolution was used to measure dynamic off-resonance maps that were calculated from the incremental changes of the image phase. These off-resonance maps are then used to dynamically update the off-resonance corrected reconstruction.

RESULTS

A global resonance offset and a pronounced gradient in head-foot direction were identified as the main components of the change during a respiration cycle. On average, correction for these fluctuations decreases the magnitude fluctuations by around 30%.

CONCLUSION

Single shot 3D imaging allows for a robust quantification of dynamic off-resonance changes in the brain. Correction for these fluctuations removes the physiological noise component associated with dynamic point spread function changes.

A majorize-minimize framework for Rician and non-central chi MR images

The statistics of many MR magnitude images are described by the non-central chi (NCC) family of probability distributions, which includes the Rician distribution as a special case. These distributions have complicated negative log-likelihoods that are nontrivial to optimize. This paper describes a novel majorize-minimize framework for NCC data that allows penalized maximum likelihood estimates to be obtained by solving a series of much simpler regularized least-squares surrogate problems. The proposed framework is general and can be useful in a range of applications. We illustrate the potential advantages of the framework with real and simulated data in two contexts: 1) MR image denoising and 2) diffusion profile estimation in high angular resolution diffusion MRI. The proposed approach is shown to yield improved results compared to methods that model the noise statistics inaccurately and faster computation relative to commonly-used nonlinear optimization techniques.

Trajectory Auto-Corrected image reconstruction

PURPOSE

To estimate k-space trajectory errors in non-Cartesian acquisitions and reconstruct distortion-free images, without trajectory measurements or gradient calibrations.

THEORY AND METHODS

The Trajectory Auto-Corrected image Reconstruction method jointly estimates k-space trajectory errors and images, based on SENSE and SPIRiT parallel imaging reconstruction. The underlying idea is that parallel imaging and oversampling in the center of k-space provides data redundancy that can be exploited to simultaneously reconstruct images and correct trajectory errors. Trajectory errors are represented as weighted sums of trajectory-dependent error basis functions, the coefficients of which are estimated using gradient-based optimization.

RESULTS

Trajectory Auto-Corrected image Reconstruction was applied to reconstruct images and errors in golden angle radial, center-out radial, and spiral in vivo 7 Tesla brain acquisitions in five subjects. Compared to reconstructions using nominal trajectories, Trajectory auto-corrected image reconstructions contained considerably less blurring and streaking and were of similar quality to images reconstructed using measured k-space trajectories in the center-out radial and spiral cases. Reconstruction cost function reductions and improvements in normalized image gradient squared were also similar to those for images reconstructed using measured trajectories.

CONCLUSION

Trajectory Auto-Corrected image Reconstruction enables non-Cartesian image reconstructions free from trajectory errors without the need for separate gradient calibrations or trajectory measurements. Magn Reson Med 76:757-768, 2016. © 2015 Wiley Periodicals, Inc.

R(∗)2 mapping for robust brain function detection in the presence of magnetic field inhomogeneity

T(*)2 mapping or R(*)2 mapping for brain function offers advantages such as providing quantitative measurements independent of the MRI acquisition parameters (e.g. echo time TE). However, magnetic field susceptibility in the human brain can prevent an accurate estimation of R(*)2, which in turn impacts the ability to study brain function. The present work investigates the effects of in-plane magnetic susceptibility-induced magnetic field gradients on R(*)2 decay. An iterative method is developed for R(*)2 estimation with an increased robustness to field inhomogeneity. The new method is further tested in a visual fMRI experiment with and without magnetic field gradients and its performance is compared to a standard BOLD fMRI and a BOLD fMRI based on echo summation. Reduced sensitivity in fMRI to in-plane magnetic gradients is obtained with the present methodology.

Snapshot MR technique to measure OEF using rapid frequency mapping

Magnetic resonance (MR)-based oxygen extraction fraction (OEF) measurement techniques that use blood oxygen level-dependent (BOLD)-based approaches require the measurement of the R2' decay rate and deoxygenated blood volume to derive the local oxygen saturation in vivo. We describe here a novel approach to measure OEF using rapid local frequency mapping. By modeling the MR decay process in the static dephasing regime as two separate dissipative and oscillatory effects, we calculate the OEF from local frequencies measured across the brain by assuming that the biophysical mechanisms causing OEF-related frequency changes can be determined from the oscillatory effects. The Parameter Assessment by Retrieval from Signal Encoding (PARSE) technique was used to acquire the local frequency change maps. The PARSE images were taken on 11 normal volunteers, and 1 patient exhibiting hemodynamic stress. The mean MR-OEF in 11 normal subjects was 36.66±7.82%, in agreement with positron emission tomography (PET) literature. In regions of hemodynamic stress induced by vascular steal, OEF exhibits the predicted focal increases. These preliminary results show that it is possible to measure OEF using a rapid frequency mapping technique. Such a technique has numerous advantages including speed of acquisition, is noninvasive, and has sufficient spatial and temporal resolution.

Rapid single-breath-hold 3D late gadolinium enhancement cardiac MRI using a stack-of-spirals acquisition

PURPOSE

To develop a rapid single-breath-hold 3D late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) method, and demonstrate its feasibility in cardiac patients.

MATERIALS AND METHODS

An inversion recovery dual-density 3D stack-of-spirals imaging sequence was developed. The spiral acquisition was 2-fold accelerated by self-consistent parallel imaging reconstruction (SPIRiT), which resulted in a total scan time of 12 heartbeats. Field map-based linear off-resonance correction was incorporated to the SPIRiT reconstruction. The 3D spiral LGE scans were performed in 15 patients who were referred for clinically ordered cardiac MR examinations that included the standard 2D multislice LGE imaging. Image sharpness and overall quality were qualitatively assessed based on 5-point scales.

RESULTS

Scar-induced hyper-LGE was identified in 4 out of the 15 patients by both 3D spiral and 2D multislice LGE tests. On average over all datasets (n = 15), the image sharpness scores were 3.9 (3D spiral) and 4.0 (2D multislice), and the image quality scores were 4.1 (3D spiral) and 4.0 (2D multislice) with no significant difference in both metrics (paired t-test; P > 0.1). The average scar contrast enhancement ratios were 0.72 and 0.75 in 3D and 2D images, respectively (n = 4). The average difference of fractional scar volumes measured in 3D and 2D images was 4.3% (n = 3).

CONCLUSION

Stack-of-spiral acquisition combined with non-Cartesian SPIRiT parallel imaging enables rapid 3D LGE MRI in a 12 heartbeat-long breath-hold.J.

Localization errors in MR spectroscopic imaging due to the drift of the main magnetic field and their correction

PURPOSE

To analyze the effect of B0 field drift on multivoxel MR spectroscopic imaging and to propose an approach for its correction.

THEORY AND METHODS

It is shown, both theoretically and in a phantom, that for ∼30 min acquisitions a linear B0 drift (∼0.1 ppm/h) will cause localization errors that can reach several voxels (centimeters) in the slower varying phase encoding directions. An efficient and unbiased estimator is proposed for tracking the drift by interleaving short (∼ T2*), nonlocalized acquisitions on the nonsuppressed water each pulse repetition time, as shown in 10 volunteers at 1.5 and 3 T.

RESULTS

The drift is shown to be predominantly linear in both the phantom and volunteers at both fields. The localization errors are observed and quantified in both phantom and volunteers. The unbiased estimator is shown to reliably track the instantaneous frequency in vivo despite only using a small portion of the FID.

CONCLUSION

Contrary to single-voxel MR spectroscopy, where it leads to line broadening, field drift can lead to localization errors in the longer chemical shift imaging experiments. Fortunately, this drift can be obtained at a negligible cost to sequence timing, and corrected for in post processing.

High efficiency multishot interleaved spiral-in/out: acquisition for high-resolution BOLD fMRI

Growing demand for high spatial resolution blood oxygenation level dependent (BOLD) functional magnetic resonance imaging faces a challenge of the spatial resolution versus coverage or temporal resolution tradeoff, which can be addressed by methods that afford increased acquisition efficiency. Spiral acquisition trajectories have been shown to be superior to currently prevalent echo-planar imaging in terms of acquisition efficiency, and high spatial resolution can be achieved by employing multiple-shot spiral acquisition. The interleaved spiral in/out trajectory is preferred over spiral-in due to increased BOLD signal contrast-to-noise ratio (CNR) and higher acquisition efficiency than that of spiral-out or noninterleaved spiral in/out trajectories (Law & Glover. Magn Reson Med 2009; 62:829-834.), but to date applicability of the multishot interleaved spiral in/out for high spatial resolution imaging has not been studied. Herein we propose multishot interleaved spiral in/out acquisition and investigate its applicability for high spatial resolution BOLD functional magnetic resonance imaging. Images reconstructed from interleaved spiral-in and -out trajectories possess artifacts caused by differences in T2 decay, off-resonance, and k-space errors associated with the two trajectories. We analyze the associated errors and demonstrate that application of conjugate phase reconstruction and spectral filtering can substantially mitigate these image artifacts. After applying these processing steps, the multishot interleaved spiral in/out pulse sequence yields high BOLD CNR images at in-plane resolution below 1 × 1 mm while preserving acceptable temporal resolution (4 s) and brain coverage (15 slices of 2 mm thickness). Moreover, this method yields sufficient BOLD CNR at 1.5 mm isotropic resolution for detection of activation in hippocampus associated with cognitive tasks (Stern memory task). The multishot interleaved spiral in/out acquisition is a promising technique for high spatial resolution BOLD functional magnetic resonance imaging applications.

Subject-specific models of susceptibility-induced B0 field variations in breast MRI

PURPOSE

To rapidly calculate and validate subject-specific field maps based on the three-dimensional shape of the bilateral breast volume.

MATERIALS AND METHODS

Ten healthy female volunteers were scanned at 3 Tesla using a multi-echo sequence that provides water, fat, in-phase, out-of-phase, and field map images. A shape-specific binary mask was automatically generated to calculate a computed field map using a dipole field model. The measured and computed field maps were compared by visualizing the spatial distribution of the difference field map, the mean absolute error, and the 80% distribution widths of frequency histograms.

RESULTS

The 10 computed field maps had a mean absolute error of 38 Hz (0.29 ppm) compared with the measured field maps. The average 80% distribution widths for the histograms of all of the computed, measured, and difference field maps are 205 Hz, 233 Hz, and 120 Hz, respectively.

CONCLUSION

The computed field maps had substantial overall agreement with the measured field maps, indicating that breast MRI field maps can be computed based on the air-tissue interfaces. These estimates may provide a predictive model for field variations and thus have the potential to improve applications in breast MRI.

Simultaneous dynamic and functional MRI scanning (SimulScan) of natural swallows

In studies of swallowing, dynamic and functional MRI are increasingly used to observe motor oropharyngeal behaviors and identify associated brain regions. However, monitoring of motor performance during a functional examination requires disruptive monitoring sensors, visual or auditory cued tasks, and strict subject compliance to stimuli. In this work, a simultaneous acquisition (SimulScan) was developed to provide dynamic images to monitor oropharyngeal motions during swallowing (1 mid-sagittal slice at 14.5 frames per second) simultaneous with functional MRI (24 oblique-axial slices with a TR of 1.6512 s). Data were acquired while three healthy adult subjects passively viewed a movie during three 15-min scans with the purpose of covertly studying uncued natural swallows. Dynamic MR images were used to determine timing of swallow onsets for subsequent functional analysis. Resulting functional maps show significant areas of activation that agree with previous functional magnetic resonance imaging studies of cued water swallows, except for regions associated with processing the task stimulus. SimulScan may prove a useful tool in developing new techniques for studying swallowing and associated neuromuscular disorders.

Faster dynamic imaging of speech with field inhomogeneity corrected spiral fast low angle shot (FLASH) at 3 T

PURPOSE

To evaluate the impact of magnetic field inhomogeneity correction on achievable imaging speeds for magnetic resonance imaging (MRI) of articulating oropharyngeal structures during speech and to determine if sufficient acquisition speed is available for visualizing speech structures with real-time MRI.

MATERIALS AND METHODS

We designed a spiral fast low angle shot (FLASH) sequence that combines several acquisition techniques with an advanced image reconstruction approach that includes magnetic field inhomogeneity correction. A simulation study was performed to examine the interaction between imaging speed, image quality, number of spiral shots, and field inhomogeneity correction. Six volunteer subjects were scanned to demonstrate adequate visualization of articulating structures during simple speech samples.

RESULTS

The simulation study confirmed that magnetic field inhomogeneity correction improves the available tradeoff between image quality and speed. Our optimized sequence co-acquires magnetic field maps for image correction and achieves a dynamic imaging rate of 21.4 frames per second, significantly faster than previous studies. Improved visualization of anatomical structures, such as the soft palate, was also seen from the field-corrected reconstructions in data acquired on volunteer subjects producing simple speech samples.

CONCLUSION

Adequate temporal resolution of articulating oropharyngeal structures during speech can be obtained by combining outer volume suppression, multishot spiral imaging, and magnetic field corrected image reconstruction. Correcting for the large, dynamic magnetic field variation in the oropharyngeal cavity improves image quality and allows for higher temporal resolution.

Application of k-space energy spectrum analysis for inherent and dynamic B0 mapping and deblurring in spiral imaging

Spiral imaging is vulnerable to spatial and temporal variations of the amplitude of the static magnetic field (B(0)) caused by susceptibility effects, eddy currents, chemical shifts, subject motion, physiological noise, and system instabilities, resulting in image blurring. Here, a novel off-resonance correction method is proposed to address these issues. A k-space energy spectrum analysis algorithm is first applied to inherently and dynamically generate a B(0) map from the k-space data at each time point, without requiring any additional data acquisition, pulse sequence modification, or phase unwrapping. A simulated phase evolution rewinding algorithm and an automatic residual deblurring algorithm are then used to correct for the blurring caused by both spatial and temporal B(0) variations, resulting in a high spatial and temporal fidelity. This method is validated against conventional B(0) mapping and deblurring methods, and its advantages for dynamic MRI applications are demonstrated in functional MRI studies.

MODEL-BASED IMAGE RECONSTRUCTION FOR MRI

Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging modality. Traditionally, MR images are reconstructed from the raw measurements by a simple inverse 2D or 3D fast Fourier transform (FFT). However, there are a growing number of MRI applications where a simple inverse FFT is inadequate, e.g., due to non-Cartesian sampling patterns, non-Fourier physical effects, nonlinear magnetic fields, or deliberate under-sampling to reduce scan times. Such considerations have led to increasing interest in methods for model-based image reconstruction in MRI.

Direct magnetic field estimation based on echo planar raw data

Gradient recalled echo echo planar imaging is widely used in functional magnetic resonance imaging. The fast data acquisition is, however, very sensitive to field inhomogeneities which manifest themselves as artifacts in the images. Typically used correction methods have the common deficit that the data for the correction are acquired only once at the beginning of the experiment, assuming the field inhomogeneity distribution B(0) does not change over the course of the experiment. In this paper, methods to extract the magnetic field distribution from the acquired k-space data or from the reconstructed phase image of a gradient echo planar sequence are compared and extended. A common derivation for the presented approaches provides a solid theoretical basis, enables a fair comparison and demonstrates the equivalence of the k-space and the image phase based approaches. The image phase analysis is extended here to calculate the local gradient in the readout direction and improvements are introduced to the echo shift analysis, referred to here as "k-space filtering analysis." The described methods are compared to experimentally acquired B(0) maps in phantoms and in vivo. The k-space filtering analysis presented in this work demonstrated to be the most sensitive method to detect field inhomogeneities.

Robust reconstruction of MRSI data using a sparse spectral model and high resolution MRI priors

We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables us to inject a range of prior information into the data processing scheme, thus constraining the reconstructions. We use high resolution, 3-D estimate of the magnetic field inhomogeneity map to generate an accurate forward model, while a high resolution estimate of the fat/water boundary is used to minimize spectral leakage artifacts. We parameterize the spectrum at each voxel as a sparse linear combination of spikes and polynomials to capture the metabolite and baseline components, respectively. The constrained model makes the problem better conditioned in regions with significant field inhomogeneity, thus enabling the recovery even in regions with high field map variations. To exploit the high resolution MR information, we formulate the problem as an anatomically constrained total variation optimization scheme on a grid with the same spacing as the magnetic resonance imaging data. We analyze the performance of the proposed scheme using phantom and human subjects. Quantitative and qualitative comparisons indicate a significant improvement in spectral quality and lower leakage artifacts.

Basic properties of SS-PARSE parameter estimates

Single shot parameter assessment by retrieval from signal encoding (SS-PARSE) is a recently introduced method to obtain quantitative parameter maps from a single-shot (typically 65 ms) magnetic resonance imaging (MRI) signal. Because it explicitly models local magnetization decay and phase evolution occurring during the signal 1) it can provide quantitative estimates of local transverse magnetization magnitude and phase, frequency, and relaxation rate and 2) it is free of geometric distortion or blurring due to field nonuniformities within the tissues. These properties promise to be advantageous in functional brain MRI (fMRI) and other dynamic imaging applications. In this paper, the basic phenomena underlying the performance of SS-PARSE in practice are discussed. Basic sources of bias errors in the parameter estimates are discussed, and performance of the method is characterized in terms of parameter estimates from simulation, experimental phantoms, and in vivo studies. Characteristics of the sum-of-square-error cost function and the iterative search algorithm are discussed, and their relative roles in determining estimation accuracy are described. Practical guidelines for use of the method are presented and discussed. In vivo parameter maps are also presented.

Effect of fat-saturation pulse on EPI time series in the presence of B0 drift

PURPOSE

We examined the hypothesis that the fat-saturation pulse in a global off-resonance state caused by magnetic field (B(0)) drift produced signal fluctuation in echo planar imaging (EPI) time series.

METHODS

We performed 3 experiments using 2 types of phantoms, one of which was homemade and contained water and a well emulsified fat source.

RESULTS

We found that B(0) drift was approximately +30 Hz in the first 30-min EPI time series and +15 Hz in the second series. We experimentally reproduced the signal fluctuations observed during actual measurement in an artificial global off-resonance state using a fat-saturation pulse, the frequency profile of which also affected the pattern of fluctuation.

CONCLUSION

These results are direct evidence that the fat-saturation pulse is a source of signal fluctuation in the presence of B(0) drift.

A network flow method for improved MR field map estimation in the presence of water and fat

Field map estimation is an important problem in MRI, with applications such as water/fat separation and correction of fast acquisitions. However, it constitutes a nonlinear and severely ill-posed problem requiring regularization. In this paper, we introduce an improved method for regularized field map estimation, based on a statistically motivated formulation, as well as a novel algorithm for the solution of the corresponding optimization problem using a network flow approach. The proposed method provides theoretical guarantees (local optimality with respect to a large move), as well as an efficient implementation. It has been applied to the water/fat separation problem and tested on a number of challenging datasets, showing high-quality results.

Joint estimation and correction of geometric distortions for EPI functional MRI using harmonic retrieval

Magnetic resonance imaging (MRI) uses applied spatial variations in the magnetic field to encode spatial position. Therefore, nonuniformities in the main magnetic field can cause image distortions. In order to correct the image distortions, it is desirable to simultaneously acquire data with a field map in registration. We propose a joint estimation (JE) framework with a fast, noniterative approach using harmonic retrieval (HR) methods, combined with a multi-echo echo-planar imaging (EPI) acquisition. The connection with HR establishes an elegant framework to solve the JE problem through a sequence of 1-D HR problems in which efficient solutions are available. We also derive the condition on the smoothness of the field map in order for HR techniques to recover the image with high signal-to-noise ratio. Compared to other dynamic field mapping methods, this method is not constrained by the absolute level of the field inhomogeneity over the slice, but relies on a generous pixel-to-pixel smoothness. Moreover, this method can recover image, field map, and T2* map simultaneously.

Iterative off-resonance and signal decay estimation and correction for multi-echo MRI

Signal dephasing due to field inhomogeneity and signal decay due to transverse relaxation lead to perturbations of the Fourier encoding commonly applied in magnetic resonance imaging. Hence, images acquired with long readouts suffer from artifacts such as blurring, distortion, and intensity variation. These artifacts can be removed in reconstruction, usually based on separately collected information in form of field and relaxation maps. In this work, a recently proposed gridding-based algorithm for off-resonance correction is extended to also address signal decay. It is integrated into a new fixed-point iteration, which permits the joint estimation of an image and field and relaxation maps from multi-echo acquisitions. This approach is then applied in simulations and in vivo experiments and demonstrated to improve both images and maps. The rapid convergence of the fixed-point iteration in combination with the efficient gridding-based correction promises to render the running time of such a joint estimation acceptable.