Motion compensated structured low-rank reconstruction for 3D multi-shot EPI

PURPOSE

The 3D multi-shot EPI imaging offers several benefits including higher SNR and high isotropic resolution compared to 2D single shot EPI. However, it suffers from shot-to-shot inconsistencies arising from physiologically induced phase variations and bulk motion. This work proposed a motion compensated structured low-rank (mcSLR) reconstruction method to address both issues for 3D multi-shot EPI.

METHODS

Structured low-rank reconstruction has been successfully used in previous work to deal with inter-shot phase variations for 3D multi-shot EPI imaging. It circumvents the estimation of phase variations by reconstructing an individual image for each phase state which are then sum-of-squares combined, exploiting their linear interdependency encoded in structured low-rank constraints. However, structured low-rank constraints become less effective in the presence of inter-shot motion, which corrupts image magnitude consistency and invalidates the linear relationship between shots. Thus, this work jointly models inter-shot phase variations and motion corruptions by incorporating rigid motion compensation for structured low-rank reconstruction, where motion estimates are obtained in a fully data-driven way without relying on external hardware or imaging navigators.

RESULTS

Simulation and in vivo experiments at 7T have demonstrated that the mcSLR method can effectively reduce image artifacts and improve the robustness of 3D multi-shot EPI, outperforming existing methods which only address inter-shot phase variations or motion, but not both.

CONCLUSION

The proposed mcSLR reconstruction compensates for rigid motion, and thus improves the validity of structured low-rank constraints, resulting in improved robustness of 3D multi-shot EPI to both inter-shot motion and phase variations.

Efficient 3D cone trajectory design for improved combined angiographic and perfusion imaging using arterial spin labeling

PURPOSE

To improve the spatial resolution and repeatability of a non-contrast MRI technique for simultaneous time resolved 3D angiography and perfusion imaging by developing an efficient 3D cone trajectory design.

METHODS

A novel parameterized 3D cone trajectory design incorporating the 3D golden angle was integrated into 4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) to achieve higher spatial resolution and sampling efficiency for both dynamic angiography and perfusion imaging with flexible spatiotemporal resolution. Numerical simulations and physical phantom scanning were used to optimize the cone design. Eight healthy volunteers were scanned to compare the original radial trajectory in 4D CAPRIA with our newly designed cone trajectory. A locally low rank reconstruction method was used to leverage the complementary k-space sampling across time.

RESULTS

The improved sampling in the periphery of k-space obtained with the optimized 3D cone trajectory resulted in improved spatial resolution compared with the radial trajectory in phantom scans. Improved vessel sharpness and perfusion visualization were also achieved in vivo. Less dephasing was observed in the angiograms because of the short TE of our cone trajectory and the improved k-space sampling efficiency also resulted in higher repeatability compared to the original radial approach.

CONCLUSION

The proposed 3D cone trajectory combined with 3D golden angle ordering resulted in improved spatial resolution and image quality for both angiography and perfusion imaging and could potentially benefit other applications that require an efficient sampling scheme with flexible spatial and temporal resolution.

An Accelerated PETALUTE MRI Sequence for In Vivo Quantification of Sodium Content in Human Articular Cartilage at 3T

In this work, we demonstrate the sodium magnetic resonance imaging (MRI) capabilities of a three-dimensional (3D) dual-echo ultrashort echo time (UTE) sequence with a novel rosette petal trajectory (PETALUTE), in comparison to the 3D density-adapted (DA) radial spokes UTE sequence. We scanned five healthy subjects using a 3D dual-echo PETALUTE acquisition and two comparable implementations of 3D DA-radial spokes acquisitions, one matching the number of k-space projections (Radial-Matched Trajectories) and the other matching the total number of samples (Radial-Matched Samples) acquired in k-space. The PETALUTE acquisition enabled equivalent sodium quantification in articular cartilage volumes of interest (168.8 ± 29.9 mM) to those derived from the 3D radial acquisitions (171.62 ± 28.7 mM and 149.8 ± 22.2 mM, respectively). We achieved a shorter scan time of 2:06 for 3D PETALUTE, compared to 3:36 for 3D radial acquisitions. We also evaluated the feasibility of further acceleration of the PETALUTE sequence through retrospective compressed sensing with 2× and 4× acceleration of the first echo and showed structural similarity of 0.89 ± 0.03 and 0.87 ± 0.03 when compared to non-retrospectively accelerated reconstruction. Together, these results demonstrate improved scan time with equivalent performance of the PETALUTE sequence compared to the 3D DA-radial sequence for sodium MRI of articular cartilage.

3D ultra-short echo time 31P-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI

Phosphorus-31 magnetic resonance spectroscopic imaging (31P-MRSI) provides valuable non-invasive in vivo information on tissue metabolism but is burdened by poor sensitivity and prolonged scan duration. Ultra-short echo time (UTE) acquisitions minimize signal loss when probing signals with relatively short spin-spin relaxation time (T2), while also preventing first-order dephasing. Here, a three-dimensional (3D) UTE sequence with a rosette k-space trajectory is applied to 31P-MRSI at 3T. Conventional chemical shift imaging (CSI) employs highly regular Cartesian k-space sampling, susceptible to substantial artifacts when accelerated via undersampling. In contrast, this novel sequence's "petal-like" pattern offers incoherent sampling more suitable for compressed sensing (CS). These results showcase the competitive performance of UTE rosette 31P-MRSI against conventional weighted CSI with simulation, phantom, and in vivo leg muscle comparisons.

Test-Retest Reproducibility of Reduced-Field-of-View Density-Weighted CRT MRSI at 3T

Quantifying an imaging modality's ability to reproduce results is important for establishing its utility. In magnetic resonance spectroscopic imaging (MRSI), new acquisition protocols are regularly introduced which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-field-of-view density-weighted concentric ring trajectory (rFOV-DW-CRT) MRSI) by calculating the coefficient of variance of data acquired from a test-retest experiment. The posterior cingulate cortex (PCC) and the right superior corona radiata (SCR) were selected as the regions of interest (ROIs) for grey matter (GM) and white matter (WM), respectively. CVs for between-subject and within-subject were consistently around or below 15% for Glx, tCho, and Myo-Ins, and below 5% for tNAA and tCr.

A Theoretical Framework for Self-Supervised MR Image Reconstruction Using Sub-Sampling via Variable Density Noisier2Noise

In recent years, there has been attention on leveraging the statistical modeling capabilities of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data. Most proposed methods assume the existence of a representative fully-sampled dataset and use fully-supervised training. However, for many applications, fully sampled training data is not available, and may be highly impractical to acquire. The development and understanding of self-supervised methods, which use only sub-sampled data for training, are therefore highly desirable. This work extends the Noisier2Noise framework, which was originally constructed for self-supervised denoising tasks, to variable density sub-sampled MRI data. We use the Noisier2Noise framework to analytically explain the performance of Self-Supervised Learning via Data Undersampling (SSDU), a recently proposed method that performs well in practice but until now lacked theoretical justification. Further, we propose two modifications of SSDU that arise as a consequence of the theoretical developments. Firstly, we propose partitioning the sampling set so that the subsets have the same type of distribution as the original sampling mask. Secondly, we propose a loss weighting that compensates for the sampling and partitioning densities. On the fastMRI dataset we show that these changes significantly improve SSDU's image restoration quality and robustness to the partitioning parameters.

Optimization of 4D combined angiography and perfusion using radial imaging and arterial spin labeling

PURPOSE

To extend and optimize a non-contrast MRI technique to obtain whole head 4D (time-resolved 3D) qualitative angiographic and perfusion images from a single scan.

METHODS

4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) uses pseudocontinuous labeling with a 3D golden ratio ("koosh ball") readout to continuously image the blood water as it travels through the arterial system and exchanges into the tissue. High spatial/temporal resolution angiograms and low spatial/temporal resolution perfusion images can be flexibly reconstructed from the same raw k-space data. Constant and variable flip angle (CFA and VFA, respectively) excitation schedules were optimized through simulations and tested in healthy volunteers. A conventional sensitivity encoding (SENSE) reconstruction was compared against a locally low rank (LLR) reconstruction, which leverages spatiotemporal correlations. Comparison was also made with time-matched time-of-flight angiography and multi-delay EPI perfusion images. Differences in image quality were assessed through split-scan repeatability.

RESULTS

The optimized VFA schedule (2-9°) resulted in a significant (p < 0.001) improvement in image quality (up to 84% vs. CFA), particularly for the lower SNR perfusion images. The LLR reconstruction provided effective denoising without biasing the signal timecourses, significantly improving angiographic and perfusion image quality and repeatability (up to 143%, p < 0.001). 4D CAPRIA performed well compared with time-of-flight angiography and had better perfusion signal repeatability than the EPI-based approach (p < 0.001).

CONCLUSION

4D CAPRIA optimized using a VFA schedule and LLR reconstruction can yield high quality whole head 4D angiograms and perfusion images from a single scan.

High-resolution 3D ultra-short echo time MRI with Rosette k-space pattern for brain iron content mapping

BACKGROUND

The iron concentration increases during normal brain development and is identified as a risk factor for many neurodegenerative diseases, it is vital to monitor iron content in the brain non-invasively.

PURPOSE

This study aimed to quantify in vivo brain iron concentration with a 3D rosette-based ultra-short echo time (UTE) magnetic resonance imaging (MRI) sequence.

METHODS

A cylindrical phantom containing nine vials of different iron concentrations (iron (II) chloride) from 0.5 millimoles to 50 millimoles and six healthy subjects were scanned using 3D high-resolution (0.94 ×0.94 ×0.94 mm3) rosette UTE sequence at an echo time (TE) of 20 μs.

RESULTS

Iron-related hyperintense signals (i.e., positive contrast) were detected based on the phantom scan, and were used to establish an association between iron concentration and signal intensity. The signal intensities from in vivo scans were then converted to iron concentrations based on the association. The deep brain structures, such as the substantia nigra, putamen, and globus pallidus, were highlighted after the conversion, which indicated potential iron accumulations.

CONCLUSION

This study suggested that T1-weighted signal intensity could be used for brain iron mapping.

Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography

PURPOSE

Dynamic angiography using arterial spin labeling (ASL) can provide detailed hemodynamic information. However, the long time-resolved readouts require small flip angles to preserve ASL signal for later timepoints, limiting SNR. By using time-encoded ASL to generate temporal information, the readout can be shortened. Here, the SNR improvements from using larger flip angles, made possible by the shorter readout, are quantitatively investigated.

METHODS

The SNR of a conventional protocol with nine Look-Locker readouts and a 4 × $$ \times $$ 3 time-encoded protocol with three Look-Locker readouts (giving nine matched timepoints) were compared using simulations and in vivo data. Both protocols were compared using readouts with constant flip angles (CFAs) and variable flip angles (VFAs), where the VFA scheme was designed to produce a consistent ASL signal across readouts. Optimization of the background suppression to minimize physiological noise across readouts was also explored.

RESULTS

The time-encoded protocol increased in vivo SNR by 103% and 96% when using CFAs or VFAs, respectively. Use of VFAs improved SNR compared with CFAs by 25% and 21% for the conventional and time-encoded protocols, respectively. The VFA scheme also removed signal discontinuities in the time-encoded data. Preliminary data suggest that optimizing the background suppression could improve in vivo SNR by a further 16%.

CONCLUSIONS

Time encoding can be used to generate additional temporal information in ASL angiography. This enables the use of larger flip angles, which can double the SNR compared with a non-time-encoded protocol. The shortened time-encoded readout can also lead to improved background suppression, reducing physiological noise and further improving SNR.

Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T

Three-dimensional (3D) encoding methods are increasingly being explored as alternatives to two-dimensional (2D) multi-slice acquisitions in fMRI, particularly in cases where high isotropic resolution is needed. 3D multi-shot EPI acquisition, as the workhorse of 3D fMRI imaging, is susceptible to physiological fluctuations which can induce inter-shot phase variations, and thus reducing the achievable tSNR, negating some of the benefit of 3D encoding. This issue can be particularly problematic at ultra-high fields like 7T, which have more severe off-resonance effects. In this work, we aim to improve the temporal stability of 3D multi-shot EPI at 7T by improving its robustness to inter-shot phase variations. We presented a 3D segmented CAIPI sampling trajectory ("seg-CAIPI") and an improved reconstruction method based on Hankel structured low-rank matrix recovery. Simulation and in-vivo results demonstrate that the combination of the seg-CAIPI sampling scheme and the proposed structured low-rank reconstruction is a promising way to effectively reduce the unwanted temporal variance induced by inter-shot physiological fluctuations, and thus improve the robustness of 3D multi-shot EPI for fMRI.

Ultra-short T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern

PURPOSE

This study aimed to develop a new 3D dual-echo rosette k-space trajectory, specifically designed for UTE MRI applications. The imaging of the ultra-short transverse relaxation time (uT₂ ) of brain was acquired to test the performance of the proposed UTE sequence.

THEORY AND METHODS

The rosette trajectory was developed based on rotations of a "petal-like" pattern in the k_x -k_y plane, with oscillated extensions in the k_z -direction for 3D coverage. Five healthy volunteers underwent 10 dual-echo 3D rosette UTE scans with various TEs. Dual-exponential complex model fitting was performed on the magnitude data to separate uT₂ signals, with the output of uT₂ fraction, uT₂ value, and long-T₂ value.

RESULTS

The 3D rosette dual-echo UTE sequence showed better performance than a 3D radial UTE acquisition. More significant signal intensity decay in white matter than gray matter was observed along with the TEs. The white matter regions had higher uT₂ fraction values than gray matter (10.9% ± 1.9% vs. 5.7% ± 2.4%). The uT₂ value was approximately 0.10 ms in white matter .

CONCLUSION

The higher uT₂ fraction value in white matter compared to gray matter demonstrated the ability of the proposed sequence to capture rapidly decaying signals.

Ultrahigh Resolution fMRI at 7T Using Radial-Cartesian TURBINE Sampling

Purpose

We investigate the use of TURBINE, a 3D radial‐Cartesian acquisition scheme in which EPI planes are rotated about the phase‐encoding axis to acquire a cylindrical k‐space for high‐fidelity ultrahigh isotropic resolution fMRI at 7 Tesla with minimal distortion and blurring.

Methods

An improved, completely self‐navigated version of the TURBINE sampling scheme was designed for fMRI at 7 Telsa. To demonstrate the image quality and spatial specificity of the acquisition, thin‐slab visual and motor BOLD fMRI at 0.67 mm isotropic resolution (16 mm slab, TRvol = 2.32 s), and 0.8 × 0.8 × 2.0 mm (whole‐brain, TRvol = 2.4 s) data were acquired. To prioritize the high spatial fidelity, we employed a temporally regularized reconstruction to improve sensitivity without any spatial bias.

Results

TURBINE images provide high structural fidelity with almost no distortion, dropout, or T₂* blurring for the thin‐slab acquisitions compared to conventional 3D EPI owing to the radial sampling in‐plane and the short echo train used. This results in activation that can be localized to pre‐ and postcentral gyri in a motor task, for example, with excellent correspondence to brain structure measured by a T₁‐MPRAGE. The benefits of TURBINE (low distortion, dropout, blurring) are reduced for the whole‐brain acquisition due to the longer EPI train. We demonstrate robust BOLD activation at 0.67 mm isotropic resolution (thin‐slab) and also anisotropic 0.8 × 0.8 × 2.0 mm (whole‐brain) acquisitions.

Conclusion

TURBINE is a promising acquisition approach for high‐resolution, minimally distorted fMRI at 7 Tesla and could be particularly useful for fMRI in areas of high B₀ inhomogeneity.

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Characteristics of articulatory gestures in stuttered speech: A case study using real-time magnetic resonance imaging

INTRODUCTION

Most of the previous articulatory studies of stuttering have focussed on the fluent speech of people who stutter. However, to better understand what causes the actual moments of stuttering, it is necessary to probe articulatory behaviors during stuttered speech. We examined the supralaryngeal articulatory characteristics of stuttered speech using real-time structural magnetic resonance imaging (RT-MRI). We investigated how articulatory gestures differ across stuttered and fluent speech of the same speaker.

METHODS

Vocal tract movements of an adult man who stutters during a pseudoword reading task were recorded using RT-MRI. Four regions of interest (ROIs) were defined on RT-MRI image sequences around the lips, tongue tip, tongue body, and velum. The variation of pixel intensity in each ROI over time provided an estimate of the movement of these four articulators.

RESULTS

All disfluencies occurred on syllable-initial consonants. Three articulatory patterns were identified. Pattern 1 showed smooth gestural formation and release like fluent speech. Patterns 2 and 3 showed delayed release of gestures due to articulator fixation or oscillation respectively. Block and prolongation corresponded to either pattern 1 or 2. Repetition corresponded to pattern 3 or a mix of patterns. Gestures for disfluent consonants typically exhibited a greater constriction than fluent gestures, which was rarely corrected during disfluencies. Gestures for the upcoming vowel were initiated and executed during these consonant disfluencies, achieving a tongue body position similar to the fluent counterpart.

CONCLUSION

Different perceptual types of disfluencies did not necessarily result from distinct articulatory patterns, highlighting the importance of collecting articulatory data of stuttering. Disfluencies on syllable-initial consonants were related to the delayed release and the overshoot of consonant gestures, rather than the delayed initiation of vowel gestures. This suggests that stuttering does not arise from problems with planning the vowel gestures, but rather with releasing the overly constricted consonant gestures.

Model-based dynamic off-resonance correction for improved accelerated fMRI in awake behaving nonhuman primates

Purpose

To estimate dynamic off‐resonance due to vigorous body motion in accelerated fMRI of awake behaving nonhuman primates (NHPs) using the echo‐planar imaging reference navigator, in order to attenuate the effects of time‐varying off‐resonance on the reconstruction.

Methods

In NHP fMRI, the animal’s head is usually head‐posted, and the dynamic off‐resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off‐resonance at each frame can be approximated as a spatially linear perturbation of the off‐resonance at a reference frame, and is manifested as a relative linear shift in k‐space. Using GRAPPA operators, we estimated these shifts by comparing the navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data.

Results

The proposed method is shown to successfully estimate spatially low‐order dynamic off‐resonance perturbations, including induced linear off‐resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multislice in vivo acquisitions in awake‐behaving NHPs.

Conclusion

A method is proposed that does not need sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state‐of‐the‐art human imaging.

Uncertainty in denoising of MRSI using low-rank methods

PURPOSE

Low-rank denoising of MRSI data results in an apparent increase in spectral SNR. However, it is not clear if this translates to a lower uncertainty in metabolite concentrations after spectroscopic fitting. Estimation of the true uncertainty after denoising is desirable for downstream analysis in spectroscopy. In this work, the uncertainty reduction from low-rank denoising methods based on spatiotemporal separability and linear predictability in MRSI are assessed. A new method for estimating metabolite concentration uncertainty after denoising is proposed. Automatic rank threshold selection methods are also assessed in simulated low SNR regimes.

METHODS

Assessment of denoising methods is conducted using Monte Carlo simulation of proton MRSI data and by reproducibility of repeated in vivo acquisitions in 5 subjects.

RESULTS

In simulated and in vivo data, spatiotemporal based denoising is shown to reduce the concentration uncertainty, but linear prediction denoising increases uncertainty. Uncertainty estimates provided by fitting algorithms after denoising consistently underestimate actual metabolite uncertainty. However, the proposed uncertainty estimation, based on an analytical expression for entry-wise variance after denoising, is more accurate. It is also shown automated rank threshold selection using Marchenko-Pastur distribution can bias the data in low SNR conditions. An alternative soft-thresholding function is proposed.

CONCLUSION

Low-rank denoising methods based on spatiotemporal separability do reduce uncertainty in MRS(I) data. However, thorough assessment is needed as assessment by SNR measured from residual baseline noise is insufficient given the presence of non-uniform variance. It is also important to select the right rank thresholding method in low SNR cases.

Speech Movement Variability in People Who Stutter: A Vocal Tract Magnetic Resonance Imaging Study

Purpose People who stutter (PWS) have more unstable speech motor systems than people who are typically fluent (PWTF). Here, we used real-time magnetic resonance imaging (MRI) of the vocal tract to assess variability and duration of movements of different articulators in PWS and PWTF during fluent speech production. Method The vocal tracts of 28 adults with moderate to severe stuttering and 20 PWTF were scanned using MRI while repeating simple and complex pseudowords. Midsagittal images of the vocal tract from lips to larynx were reconstructed at 33.3 frames per second. For each participant, we measured the variability and duration of movements across multiple repetitions of the pseudowords in three selected articulators: the lips, tongue body, and velum. Results PWS showed significantly greater speech movement variability than PWTF during fluent repetitions of pseudowords. The group difference was most evident for measurements of lip aperture using these stimuli, as reported previously, but here, we report that movements of the tongue body and velum were also affected during the same utterances. Variability was not affected by phonological complexity. Speech movement variability was unrelated to stuttering severity within the PWS group. PWS also showed longer speech movement durations relative to PWTF for fluent repetitions of multisyllabic pseudowords, and this group difference was even more evident as complexity increased. Conclusions Using real-time MRI of the vocal tract, we found that PWS produced more variable movements than PWTF even during fluent productions of simple pseudowords. PWS also took longer to produce multisyllabic words relative to PWTF, particularly when words were more complex. This indicates general, trait-level differences in the control of the articulators between PWS and PWTF. Supplemental Material https://doi.org/10.23641/asha.14782092.

Accelerated calibrationless parallel transmit mapping using joint transmit and receive low-rank tensor completion

Purpose

To evaluate an algorithm for calibrationless parallel imaging to reconstruct undersampled parallel transmit field maps for the body and brain.

Methods

Using a combination of synthetic data and in vivo measurements from brain and body, 3 different approaches to a joint transmit and receive low-rank tensor completion algorithm are evaluated. These methods included: 1) virtual coils using the product of receive and transmit sensitivities, 2) joint-receiver coils that enforces a low rank structure across receive coils of all transmit modes, and 3) transmit low rank that uses a low rank structure for both receive and transmit modes simultaneously. The performance of each is investigated for different noise levels and different acceleration rates on an 8-channel parallel transmit 7 Tesla system.

Results

The virtual coils method broke down with increasing noise levels or acceleration rates greater than 2, producing normalized RMS error greater than 0.1. The joint receiver coils method worked well up to acceleration factors of 4, beyond which the normalized RMS error exceeded 0.1. Transmit low rank enabled an eightfold acceleration, with most normalized RMS errors remaining below 0.1.

Conclusion

This work demonstrates that undersampling factors of up to eightfold are feasible for transmit array mapping and can be reconstructed using calibrationless parallel imaging methods.

High-resolution metabolic mapping of the cerebellum using 2D zoom magnetic resonance spectroscopic imaging

PURPOSE

The human cerebellum plays an important role in the functional activity of the cerebrum, ranging from motor to cognitive systems given its relaying role between the spinal cord and cerebrum. The cerebellum poses many challenges to Magnetic Resonance Spectroscopic Imaging (MRSI) due to its caudal location, susceptibility to physiological artifacts, and partial volume artifacts resulting from its complex anatomical structure. Thus, in the present study, we propose a high-resolution MRSI acquisition scheme for the cerebellum.

METHODS

A zoom or reduced field of view (rFOV) metabolite-cycled MRSI acquisition at 3 Tesla, with a grid of 48 × 48, was developed to achieve a nominal resolution of 62.5 μL. Single-slice rFOV MRSI data were acquired from the cerebellum of 5 healthy subjects with a nominal resolution of 2.5 × 2.5 × 10 mm³ in 9.6 min. Spectra were quantified using the LCModel package. A spatially unbiased atlas template of the cerebellum was used to analyze metabolite distributions in the cerebellum.

RESULTS

The superior quality of the achieved spectra-enabled generation of high-resolution metabolic maps of total N-acetylaspartate, total Creatine (tCr), total Choline (tCho), glutamate+glutamine, and myo-inositol, with Cramér-Rao lower bounds below 50%. A template-based regions of interest (ROI) analysis resulted in spatially dependent metabolite distributions in 9 ROIs. The group-averaged high-resolution metabolite maps across subjects increased the contrast-to-noise ratio between cerebellum regions.

CONCLUSION

These findings indicate that very high-resolution metabolite probing of the cerebellum is feasible using rFOV or zoomed MRSI at 3 Tesla.

Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis

Susceptibility weighted magnetic resonance imaging (MRI) is sensitive to the local concentration of iron and myelin. Here, we describe a robust image processing pipeline for quantitative susceptibility mapping (QSM) and R2* mapping of fixed post-mortem, whole-brain data. Using this pipeline, we compare the resulting quantitative maps in brains from patients with amyotrophic lateral sclerosis (ALS) and controls, with validation against iron and myelin histology. Twelve post-mortem brains were scanned with a multi-echo gradient echo sequence at 7T, from which susceptibility and R2* maps were generated. Semi-quantitative histological analysis for ferritin (the principal iron storage protein) and myelin proteolipid protein was performed in the primary motor, anterior cingulate and visual cortices. Magnetic susceptibility and R2* values in primary motor cortex were higher in ALS compared to control brains. Magnetic susceptibility and R2* showed positive correlations with both myelin and ferritin estimates from histology. Four out of nine ALS brains exhibited clearly visible hyperintense susceptibility and R2* values in the primary motor cortex. Our results demonstrate the potential for MRI-histology studies in whole, fixed post-mortem brains to investigate the biophysical source of susceptibility weighted MRI signals in neurodegenerative diseases like ALS.

Highly accelerated vessel-selective arterial spin labeling angiography using sparsity and smoothness constraints

Purpose

To demonstrate that vessel selectivity in dynamic arterial spin labeling angiography can be achieved without any scan‐time penalty or noticeable loss of image quality compared with conventional arterial spin labeling angiography.

Methods

Simulations on a numerical phantom were used to assess whether the increased sparsity of vessel‐encoded angiograms compared with non‐vessel‐encoded angiograms alone can improve reconstruction results in a compressed‐sensing framework. Further simulations were performed to study whether the difference in relative sparsity between nonselective and vessel‐selective dynamic angiograms was sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden‐angle radial trajectory and reconstructed by enforcing image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed‐sensing framework.

Results

Relative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel‐selective and nonselective angiography were negligible compared with the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand‐tuning the parameters of the reconstruction.

Conclusion

The increase in relative sparsity of vessel‐selective angiograms compared with nonselective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel‐selective information at no scan‐time cost.

Improved statistical efficiency of simultaneous multi-slice fMRI by reconstruction with spatially adaptive temporal smoothing

We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves statistical efficiency. The method incorporates regularization to adjust temporal smoothness in a spatially varying, encoding-dependent manner, reducing the g-factor noise amplification per temporal degree of freedom. This results in a net improvement in tSNR and GLM efficiency, where the efficiency gain can be derived analytically as a function of the encoding and reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Analytical predictions, simulated and experimental results demonstrate a marked improvement in statistical efficiency in the temporally regularized reconstructions compared to conventional slice-GRAPPA reconstructions, particularly in central brain regions. Furthermore, experimental results confirm that residual slice leakage and aliasing errors are not noticeably increased compared to slice-GRAPPA reconstruction. This approach to temporally regularized image reconstruction in SMS-fMRI improves statistical power, and allows for explicit choice of reconstruction parameters by directly assessing their impact on noise variance per degree of freedom.

Volume-localized measurement of oxygen extraction fraction in the brain using MRI

Purpose

T₂‐relaxation‐under‐spin‐tagging (TRUST) is an MR technique for the non‐invasive assessment of whole‐brain cerebral oxygen extraction fraction (OEF), through measurement of the venous blood T₂ relaxation time in the sagittal sinus. A key limitation of TRUST, however, is the lack of spatial specificity of the measurement. We sought to develop a modified TRUST sequence, selective localized TRUST (SL‐TRUST), having sensitivity to venous blood T₂ within a targeted brain region, and therefore achieving spatially localized measurements of cerebral tissue OEF, while still retaining acquisition in the sagittal sinus.

Methods

A method for selective localization of TRUST sequence was developed, and the reproducibility of the technique was evaluated in healthy participants. Regional measurements were achieved for a single hemisphere and for a 3D‐localized 70 × 70 × 80 mm³ tissue region using SL‐TRUST and compared to a global TRUST measure. An additional measure of venous blood T₁ in the sagittal sinus was used to estimate subject‐specific hematocrit. Six subjects were scanned over 4 sessions, including intra‐session repeat measurements.

Results

The average T₂ in the sagittal sinus was found to be 60.8 ± 8.9, 62.7 ± 7.9, 64.6 ± 8.4, and 66.3 ± 10.3 ms (mean ± SD) for conventional TRUST, global SL‐TRUST, hemispheric SL‐TRUST, and 3D‐localized SL‐TRUST, respectively. Intra‐, inter‐session, and inter‐subject coefficients of variation for OEF using SL‐TRUST were found to be comparable and in some cases superior to those obtained using TRUST.

Conclusion

OEF comparison of 2 contralateral regions was achievable in under 5 min suggesting SL‐TRUST offers potential for quantifying regional OEF differences in both healthy and clinical populations.

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features.

Metabolite-cycled density-weighted concentric rings k-space trajectory (DW-CRT) enables high-resolution 1 H magnetic resonance spectroscopic imaging at 3-Tesla

Magnetic resonance spectroscopic imaging (MRSI) is a promising technique in both experimental and clinical settings. However, to date, MRSI has been hampered by prohibitively long acquisition times and artifacts caused by subject motion and hardware-related frequency drift. In the present study, we demonstrate that density weighted concentric ring trajectory (DW-CRT) k-space sampling in combination with semi-LASER excitation and metabolite-cycling enables high-resolution MRSI data to be rapidly acquired at 3 Tesla. Single-slice full-intensity MRSI data (short echo time (TE) semi-LASER TE = 32 ms) were acquired from 6 healthy volunteers with an in-plane resolution of 5 × 5 mm in 13 min 30 sec using this approach. Using LCModel analysis, we found that the acquired spectra allowed for the mapping of total N-acetylaspartate (median Cramer-Rao Lower Bound [CRLB] = 3%), glutamate+glutamine (8%), and glutathione (13%). In addition, we demonstrate potential clinical utility of this technique by optimizing the TE to detect 2-hydroxyglutarate (long TE semi-LASER, TE = 110 ms), to produce relevant high-resolution metabolite maps of grade III IDH-mutant oligodendroglioma in a single patient. This study demonstrates the potential utility of MRSI in the clinical setting at 3 Tesla.

Density-weighted concentric rings k-space trajectory for 1 H magnetic resonance spectroscopic imaging at 7 T

It has been shown that density-weighted (DW) k-space sampling with spiral and conventional phase encoding trajectories reduces spatial side lobes in magnetic resonance spectroscopic imaging (MRSI). In this study, we propose a new concentric ring trajectory (CRT) for DW-MRSI that samples k-space with a density that is proportional to a spatial, isotropic Hanning window. The properties of two different DW-CRTs were compared against a radially equidistant (RE) CRT and an echo-planar spectroscopic imaging (EPSI) trajectory in simulations, phantoms and in vivo experiments. These experiments, conducted at 7 T with a fixed nominal voxel size and matched acquisition times, revealed that the two DW-CRT designs improved the shape of the spatial response function by suppressing side lobes, also resulting in improved signal-to-noise ratio (SNR). High-quality spectra were acquired for all trajectories from a specific region of interest in the motor cortex with an in-plane resolution of 7.5 × 7.5 mm2 in 8 min 3 s. Due to hardware limitations, high-spatial-resolution spectra with an in-plane resolution of 5 × 5 mm2 and an acquisition time of 12 min 48 s were acquired only for the RE and one of the DW-CRT trajectories and not for EPSI. For all phantom and in vivo experiments, DW-CRTs resulted in the highest SNR. The achieved in vivo spectral quality of the DW-CRT method allowed for reliable metabolic mapping of eight metabolites including N-acetylaspartylglutamate, γ-aminobutyric acid and glutathione with Cramér-Rao lower bounds below 50%, using an LCModel analysis. Finally, high-quality metabolic mapping of a whole brain slice using DW-CRT was achieved with a high in-plane resolution of 5 × 5 mm2 in a healthy subject. These findings demonstrate that our DW-CRT MRSI technique can perform robustly on MRI systems and within a clinically feasible acquisition time.

PEAR: PEriodic And fixed Rank separation for fast fMRI

PURPOSE

In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High-quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements.

METHODS

The proposed approach decomposes the fMRI signal into a component which has a fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI.

RESULTS

Experimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R = 8,10.66 (for simulated data) and R = 6.66,10 (for real data).

CONCLUSIONS

This paper presents PEAR, an undersampled fMRI reconstruction approach based on decomposing the fMRI signal to periodic and fixed rank components. PEAR results in reconstruction with higher fidelity than when using a fixed-rank based model or a conventional Low-rank + Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state-of-the-art methods.

PEAR: PEriodic and ApeRiodic signal separation for fast FMRI

Undersampling of functional MRI (fMRI) data leads to increased temporal resolution, as it allows shorter acquisition time per frame. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the fMRI data. Recent publications suggest that the fMRI signal is a superposition of periodic and aperiodic signals. In this paper we develop an fMRI reconstruction approach based on this modeling. The fMRI data is assumed to be composed of two components: a component that holds a sum of periodic signals which is sparse in the temporal Fourier domain and an component that holds the remaining imaging information (consisting of the background and aperiodic signals) which has low rank. Data reconstruction is done by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic and ApeRiodic signal separation for fast fMRI. Experimental results are based on fMRI reconstruction using realistic timecourses. Evaluation was performed both quantitatively and visually versus ground truth. Results demonstrate PEAR's improvement in estimating the realistic timecourses versus state-of-the-art approaches at acceleration ratio of R=16.6.

Non-water-suppressed short-echo-time magnetic resonance spectroscopic imaging using a concentric ring k-space trajectory

Water-suppressed MRS acquisition techniques have been the standard MRS approach used in research and for clinical scanning to date. The acquisition of a non-water-suppressed MRS spectrum is used for artefact correction, reconstruction of phased-array coil data and metabolite quantification. Here, a two-scan metabolite-cycling magnetic resonance spectroscopic imaging (MRSI) scheme that does not use water suppression is demonstrated and evaluated. Specifically, the feasibility of acquiring and quantifying short-echo (TE  = 14 ms), two-dimensional stimulated echo acquisition mode (STEAM) MRSI spectra in the motor cortex is demonstrated on a 3 T MRI system. The increase in measurement time from the metabolite-cycling is counterbalanced by a time-efficient concentric ring k-space trajectory. To validate the technique, water-suppressed MRSI acquisitions were also performed for comparison. The proposed non-water-suppressed metabolite-cycling MRSI technique was tested for detection and correction of resonance frequency drifts due to subject motion and/or hardware instability, and the feasibility of high-resolution metabolic mapping over a whole brain slice was assessed. Our results show that the metabolite spectra and estimated concentrations are in agreement between non-water-suppressed and water-suppressed techniques. The achieved spectral quality, signal-to-noise ratio (SNR) > 20 and linewidth <7 Hz allowed reliable metabolic mapping of five major brain metabolites in the motor cortex with an in-plane resolution of 10 × 10 mm2 in 8 min and with a Cramér-Rao lower bound of less than 20% using LCModel analysis. In addition, the high SNR of the water peak of the non-water-suppressed technique enabled voxel-wise single-scan frequency, phase and eddy current correction. These findings demonstrate that our non-water-suppressed metabolite-cycling MRSI technique can perform robustly on 3 T MRI systems and within a clinically feasible acquisition time.

Accelerating functional MRI using fixed-rank approximations and radial-cartesian sampling

PURPOSE

Recently, k-t FASTER (fMRI Accelerated in Space-time by means of Truncation of Effective Rank) was introduced for rank-constrained acceleration of fMRI data acquisition. Here we demonstrate improvements achieved through a hybrid three-dimensional radial-Cartesian sampling approach that allows posthoc selection of acceleration factors, as well as incorporation of coil sensitivity encoding in the reconstruction.

METHODS

The multicoil rank-constrained reconstruction used hard thresholding and shrinkage on matrix singular values of the space-time data matrix, using sensitivity encoding and the nonuniform Fast Fourier Transform to enforce data consistency in the multicoil non-Cartesian k-t domain. Variable acceleration factors were made possible using a radial increment based on the golden ratio. Both retrospective and prospectively under-sampled data were used to assess the fidelity of the enhancements to the k-t FASTER technique in resting and task-fMRI data.

RESULTS

The improved k-t FASTER is capable of tailoring acceleration factors for recovery of different signal components, achieving up to R = 12.5 acceleration in visual-motor task data. The enhancements reduce data matrix reconstruction errors even at much higher acceleration factors when compared directly with the original k-t FASTER approach.

CONCLUSION

We have shown that k-t FASTER can be used to significantly accelerate fMRI data acquisition with little penalty to data quality. Magn Reson Med 76:1825-1836, 2016. © 2016 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.

Motion correction for functional MRI with three-dimensional hybrid radial-Cartesian EPI

Purpose

Subject motion is a major source of image degradation for functional MRI (fMRI), especially when using multishot sequences like three‐dimensional (3D EPI). We present a hybrid radial‐Cartesian 3D EPI trajectory enabling motion correction in k‐space for functional MRI.

Methods

The EPI “blades” of the 3D hybrid radial‐Cartesian EPI sequence, called TURBINE, are rotated about the phase‐encoding axis to fill out a cylinder in 3D k‐space. Angular blades are acquired over time using a golden‐angle rotation increment, allowing reconstruction at flexible temporal resolution. The self‐navigating properties of the sequence are used to determine motion parameters from a high temporal‐resolution navigator time series. The motion is corrected in k‐space as part of the image reconstruction, and evaluated for experiments with both cued and natural motion.

Results

We demonstrate that the motion correction works robustly and that we can achieve substantial artifact reduction as well as improvement in temporal signal‐to‐noise ratio and fMRI activation in the presence of both severe and subtle motion.

Conclusion

We show the potential for hybrid radial‐Cartesian 3D EPI to substantially reduce artifacts for application in fMRI, especially for subject groups with significant head motion. The motion correction approach does not prolong the scan, and no extra hardware is required. Magn Reson Med 78:527–540, 2017. © 2016 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.

k-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints

PURPOSE

In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data.

THEORY AND METHODS

Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs).

RESULTS

The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3-4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration.

CONCLUSION

With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis.

Constrained source space imaging: application to fast, region-based functional MRI

A new technique called constrained source space imaging is introduced that holds promise for ultrafast acquisition of functional magnetic resonance imaging data. A sparse set of arbitrarily positioned, coarse voxels is first localized using radiofrequency selective excitation, from which magnetization signals are separated using only the spatial sensitivities of multichannel receiver coils, without the need for k-space encoding using imaging gradients. This method permits very fast acquisitions of targeted magnetization without complex or time-consuming image reconstruction techniques. Furthermore, because the data acquisition is performed without imaging gradients, T2* decays can be densely sampled and processed for contrast enhancement to improve functional magnetic resonance imaging data quality. Here, the constrained source space imaging technique is validated in proof-of-concept form, for a simple functional magnetic resonance imaging motor task using a prototype dual-band stimulated echo acquisition mode excitation to image four voxels at TR = 250 ms. Results demonstrate good voxel signal separation and good characterization of hemodynamic responses in primary motor cortices (M1) and supplementary motor areas through T2* fitting of the measured signals. With further refinement, the constrained source space imaging method has potential utility in a priori ROI-based functional magnetic resonance imaging experiments with TR values under 100 ms. Rapid, multivoxel measurements of other sources of MR signal contrast are also possible.

Real-time correction by optical tracking with integrated geometric distortion correction for reducing motion artifacts in functional MRI

Head motion artifacts are a major problem in functional MRI that limit its use in neuroscience research and clinical settings. Real-time scan-plane correction by optical tracking has been shown to correct slice misalignment and nonlinear spin-history artifacts; however, residual artifacts due to dynamic magnetic field nonuniformity may remain in the data. A recently developed correction technique, Phase Labeling for Additional Coordinate Encoding, can correct for absolute geometric distortion using only the complex image data from two echo planar images with slightly shifted k-space trajectories. An approach is presented that integrates Phase Labeling for Additional Coordinate Encoding into a real-time scan-plane update system by optical tracking, applied to a tissue-equivalent phantom undergoing complex motion and an functional MRI finger tapping experiment with overt head motion to induce dynamic field nonuniformity. Experiments suggest that such integrated volume-by-volume corrections are very effective at artifact suppression, with potential to expand functional MRI applications.

Spin-history artifact during functional MRI: potential for adaptive correction

PURPOSE

Functional magnetic resonance imaging (fMRI) is limited by sensitivity to millimetre-scale head motion. Adaptive correction is a strategy to adjust the imaging plane in response to measured head motion, thereby suppressing motion artifacts. This strategy should correct for motion in all six degrees of freedom and also holds promise for through-plane motion that creates "spin-history" artifact that cannot easily be removed by postprocessing methods. Improved quantitative understanding of the MRI signal behavior associated with spin-history artifact would be useful for implementing adaptive correction robustly.

METHODS

A numerical simulation was developed to predict MRI artifact signal amplitude in a single-slice for simple motions, implemented with and without adaptive correction, and compared with experiment by imaging a phantom at 3.0 T. Functional MRI was also performed of a human volunteer to illustrate adaptive correction in the presence of spin-history artifact.

RESULTS

Good agreement was achieved between simulation and experimental results. Although time-averaged artifact signal amplitude was observed to correlate linearly with motion speed, artifact time-courses were nonlinearly related to motion waveforms. In addition, experimental results demonstrated effective adaptive correction of spin-history artifact when the phantom underwent complex motions. Adaptive correction during human fMRI suppressed spin-history artifacts and spurious activations associated with task-correlated motion.

CONCLUSIONS

Overall, this work suggests that adaptive correction, especially when implemented with minimal lag between motion measurement and scan plane update, may help to expand the populations for which fMRI can be performed robustly.

BOLD contrast and noise characteristics of densely sampled multi-echo fMRI data

Blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) can be enhanced using multi-echo imaging and postprocessing techniques that combine the echoes in weighted summation. Here, existing echo-weighting methods are reassessed in the context of an explicit physiological noise model, and a new method is introduced: weights that scale linearly with echo time. Additionally, a method using data-driven weights defined using principal component analysis (PCA) is included for comparison. Differences in BOLD contrast enhancement between methods were compared analytically where possible, and using Monte Carlo simulations for different noise conditions and different combinations of acquisition parameters. The comparisons were also validated through densely sampled (256-echo) multi-echo fMRI experimental data acquired at 1.5T and 3.0T. Results indicated that the contrast-to-noise ratio (CNR) of the studied weighting methods have a strong dependence on the physiological noise, echo spacing and the width of the sampling window. With low noise correlations between echoes, contrast gain for all weighting methods was shown to have a square root dependence on the echo sampling density, and in typical experimental noise conditions, increasing the sampling window beyond 3·T2* produced marginal additional benefit. Simulations and experiments also emphasized that noise correlations between echoes are likely the main factor limiting the potential CNR gains achievable by densely sampled multi-echo fMRI.