Magnetic resonance Spectroscopy with Linear Algebraic Modeling (SLAM) for higher speed and sensitivity

Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction

PURPOSE

To develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data.

METHODS

The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem.

RESULTS

The proposed method has been evaluated using simulations as well as in vivo  1 $$ {}^1 $$ H and  31 $$ {}^{31} $$ P MRSI data, demonstrating improved performance over state-of-the-art methods, with about 6× $$ \times $$ fewer averages needed than standard Fourier reconstruction for similar metabolite estimation variances and up to 100× $$ \times $$ reduction in processing time compared to a prior neural network constrained reconstruction method. Computational and theoretical analyses were performed to offer further insights into the effectiveness of the proposed method.

CONCLUSION

A novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.

Compartment-based reconstruction of acquisition-weighted 31P cardiac MRSI reduces sensitivity to cardiac motion and scan planning

Motivation: 31P magnetic resonance spectroscopic imaging (31P MRSI) is a powerful technique for investigating the metabolic effects of treatments for heart failure in vivo, allowing a better understanding of their mechanism of action in patient cohorts. Unfortunately, cardiac 31P MRSI is fundamentally limited by low SNR, which leads to compromises in acquisition, such as no cardiac or respiratory gating or low spatial resolution, in order to achieve reasonable scan times. Spectroscopy with linear algebra modeling (SLAM) reconstruction may be able to address these challenges and therefore improve repeatability by incorporating a segmented localizer into the reconstruction. Methods: Six healthy volunteers were scanned twice in a test-retest procedure to allow quantification of repeatability. Each scan consisted of anatomical localizers and two acquisition-weighted (AW) 31P MRSI acquisitions, which were acquired with and without cardiac gating. Five patients with heart failure with a preserved ejection fraction were then scanned with the same 31P MRSI sequence without cardiac gating. All 31P MRSI datasets were reconstructed with both conventional Fourier transform (FT)-based reconstruction and SLAM reconstruction, which were compared statistically. The effect of shifting the 31P MRSI acquisition field of view was also investigated. Results: In the healthy volunteer cohort, the spectral fit of the SLAM reconstructions had significantly improved Cramer-Rao lower bounds (CRLBs) compared to the FT-based reconstruction of non-cardiac gated data, as well as improved coefficients of variability and repeatability. The SLAM reconstruction found a significant difference in the PCr/ATP ratio between the healthy volunteer and patient cohorts, which the FT-based reconstruction did not find. Furthermore, the SLAM reconstruction was less influenced by the placement of the field of view (FOV) of the 31P MRSI acquisition in post hoc analysis. Discussion: The experimental benefits of the SLAM reconstruction for AW data were demonstrated by the improvements in fit confidence and repeatability seen in the healthy volunteer cohort and post hoc FOV analysis. The benefit of SLAM reconstruction of AW data for clinical studies was then illustrated by the patient cohort, which suggested improved sensitivity to clinically significant changes in the PCr/ATP ratio.

Individually tailored spatial-spectral pulsed CEST MRI for ratiometric mapping of myocardial energetic species at 3T

PURPOSE

CEST MRI has been used to probe changes in cardiac metabolism via assessment of CEST contrast from Cr. However, B1 variation across the myocardium leads to spatially variable Cr CEST contrast in healthy myocardium.

METHODS

We developed a spatial-spectral (SPSP) saturation pulsed CEST protocol to compensate for B1 variation. Flip angle maps were used to individually tailor SPSP pulses comprised of a train of one-dimensional spatially selective subpulses selective along the principal B1 gradient dimension. Complete Z-spectra in the hearts of (n = 10) healthy individuals were acquired using conventional Gaussian saturation and SPSP schemes and supported by phantom studies.

RESULTS

In simulations, the use of SPSP pulses reduced the average SD of the effective saturation B1 values within the myocardium (n = 10) from 0.12 ± 0.02 μT to 0.05 ± 0.01 μT (p < 0.01) and reduced the average SD of Cr CEST contrast in vivo from 10.0 ± 4.3% to 6.1 ± 3.5% (p < 0.05). Results from the hearts of human subjects showed a significant reduction of CEST contrast distribution at 2 ppm, as well as amplitude, when using SPSP saturation. Corresponding phantom experiments revealed PCr-specific contrast generation at body temperature when SPSP saturation was used but combined PCr and Cr contrast generation when Gaussian saturation was used.

CONCLUSION

The use of SPSP saturation pulsed CEST resulted in PCr-specific contrast generation and enabled ratiometric mapping of PCr to total Cr CEST contrast in the human heart at 3T.

Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging

Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.

Compartment-based reconstruction of 3D acquisition-weighted 31 P cardiac magnetic resonance spectroscopic imaging at 7 T: A reproducibility study

Even at 7 T, cardiac 31 P magnetic resonance spectroscopic imaging (MRSI) is fundamentally limited by low signal-to-noise ratio (SNR), leading to long scan times and poor temporal and spatial resolutions. Compartment-based reconstruction algorithms such as magnetic resonance spectroscopy with linear algebraic modeling (SLAM) and spectral localization by imaging (SLIM) may improve SNR or reduce scan time without changes to acquisition. Here, we compare the repeatability and SNR performance of these compartment-based methods, applied to three different acquisition schemes at 7 T. Twelve healthy volunteers were scanned twice. Each scan session consisted of a 6.5-min 3D acquisition-weighted (AW) cardiac 31 P phase encode-based MRSI acquisition and two 6.5-min truncated k-space acquisitions with increased averaging (4 × 4 × 4 central k-space phase encodes and fractional SLAM [fSLAM] optimized k-space phase encodes). Spectra were reconstructed using (i) AW Fourier reconstruction; (ii) AW SLAM; (iii) AW SLIM; (iv) 4 × 4 × 4 SLAM; (v) 4 × 4 × 4 SLIM; and (vi) fSLAM acquisition-reconstruction combinations. The phosphocreatine-to-adenosine triphosphate (PCr/ATP) ratio, the PCr SNR, and spatial response functions were computed, in addition to coefficients of reproducibility and variability. Using the compartment-based reconstruction algorithms with the AW 31 P acquisition resulted in a significant increase in SNR compared with previously published Fourier-based MRSI reconstruction methods while maintaining the measured PCr/ATP ratio and improving interscan reproducibility. The alternative acquisition strategies with truncated k-space performed no better than the common AW approach. Compartment-based spectroscopy approaches provide an attractive reconstruction method for cardiac 31 P spectroscopy at 7 T, improving reproducibility and SNR without the need for a dedicated k-space sampling strategy.

Accelerated MR spectroscopic imaging-a review of current and emerging techniques

Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.

Schulte R, Laustsen C. Comprehensive Literature Review of Hyperpolarized Carbon-13 MRI: The Road to Clinical Application

This review provides a comprehensive assessment of the development of hyperpolarized (HP) carbon-13 metabolic MRI from the early days to the present with a focus on clinical applications. The status and upcoming challenges of translating HP carbon-13 into clinical application are reviewed, along with the complexity, technical advancements, and future directions. The road to clinical application is discussed regarding clinical needs and technological advancements, highlighting the most recent successes of metabolic imaging with hyperpolarized carbon-13 MRI. Given the current state of hyperpolarized carbon-13 MRI, the conclusion of this review is that the workflow for hyperpolarized carbon-13 MRI is the limiting factor.

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models

Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.

Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces

PURPOSE

To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast 1 H-MRSI of the brain.

THEORY AND METHODS

A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning "empirical" distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method.

RESULTS

The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D 1 H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm3 in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing ( B 0 map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters.

CONCLUSIONS

The proposed method enables ultrafast 1 H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as D 1 ) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high-resolution MRSI.

Non-sedated functional imaging based on deep synchronization of PROPELLER MRI and NIRS

BACKGROUND AND OBJECTIVE

Periodically rotated overlapping parallel lines with enhanced reconstruction-echo planar imaging (PROPELLER-EPI) is a promising technique for non-sedated functional imaging due to its unique advantage of motion correction. However, its multiple-blades sampling blood-oxygen-level dependent (BOLD) signal leads to low sampling rate and aliasing of higher frequency physiological signal components such as the cardiac pulsation.

METHODS

In this study, we use near infrared spectroscopy (NIRS) synchronized with pulse sequences of PROPELLER-EPI, utilizing the fact that the optical sensing speed is inherently high. NIRS measures changes of oxyhemoglobin and deoxyhemoglobin to identify the transient states of on-BOLD and off-BOLD, and then labels each blade by temporal co-registration. The labeled blades from multiple epochs of a functional experiment are then used for the k-space data combination and subsequent image reconstruction. An eigenfunction model is proposed for temporal co-registration and to quantify the temporal resolution of the hemodynamic response.

RESULT

The experiment of NIRS labeled PROPELLER-EPI was carried out with the optical sampling rate of 10 Hz and the magnetic pulses repetition time of 1000 ms, and the temporal resolution is 20 times better than that of the state-of-the-art sliding-window PROPELLER-EPI. We compared the functional imaging results against the conventional magnetic resonance echo planar imaging-measured activity and achieved an accuracy of 0.9.

CONCLUSIONS

Using the synchronization of NIRS, the proposed imaging scheme provides an effective way to implement PROPELLER-EPI, which features motion free, high SNR, and enhanced spatial-temporal resolution.

Enhanced hyperpolarized chemical shift imaging based on a priori segmented information

PURPOSE

The purpose of the study was to develop an approach for improving the resolution and sensitivity of hyperpolarized ¹³ C MRSI based on a priori anatomical information derived from featured, water-based ¹ H images.

METHODS

A reconstruction algorithm exploiting ¹ H MRI for the redefinition of the ¹³ C MRSI anatomies was developed, based on a modification of the spectroscopy with linear algebraic modeling (SLAM) principle. To enhance ¹³ C spatial resolution and reduce spillover effects without compromising SNR, this model was extended by endowing it with a search allowing smooth variations in the ¹³ C MR intensity within the targeted regions of interest.

RESULTS

Experiments were performed in vitro on enzymatic solutions and in vivo on rodents, based on the administration of ¹³ C-enriched hyperpolarized pyruvate and urea. The spectral images reconstructed for these substrates and from metabolic products based on predefined ¹ H anatomical compartments using the new algorithm, compared favorably with those arising from conventional Fourier-based analyses of the same data. The new approach also delivered reliable kinetic ¹³ C results, for the kind of processes and timescales usually targeted by hyperpolarized MRSI.

CONCLUSION

A simple, flexible strategy is introduced to boost the sensitivity and resolution provided by hyperpolarized ¹³ C MRSI, based on readily available ¹ H MR information.

Orientation dependence and decay characteristics of T2 * relaxation in the human meniscus studied with 7 Tesla MR microscopy and compared to histology

Purpose

To evaluate: (1) the feasibility of MR microscopy T₂* mapping by performing a zonal analysis of spatially matched T₂* maps and histological images using microscopic in‐plane pixel resolution; (2) the orientational dependence of T₂* relaxation of the meniscus; and (3) the T₂* decay characteristics of the meniscus by statistically evaluating the quality of mono‐ and biexponential model.

Methods

Ultrahigh resolution T₂* mapping was performed with ultrashort echo time using a 7 Tesla MR microscopy system. Measurement of one meniscus was performed at three orientations to the main magnetic field (0, 55, and 90°). Histological assessment was performed with picrosirius red staining and polarized light microscopy. Quality of mono‐ and biexponential model fitting was tested using Akaike Information Criteria and F‐test.

Results

(1) The outer laminar layer, connective tissue fibers from the joint capsule, and the highly organized tendon‐like structures were identified using ultra‐highly resolved MRI. (2) Highly organized structures of the meniscus showed considerable changes in T₂* values with orientation. (3) No significant biexponential decay was found on a voxel‐by‐voxel–based evaluation. On a region‐of‐interest–averaged basis, significant biexponential decay was found for the tendon‐like region in a fiber‐to‐field angle of 0°.

Conclusion

The MR microscopy approach used in this study allows the identification of meniscus substructures and to quantify T₂* with a voxel resolution approximately 100 times higher than previously reported. T₂* decay showed a strong fiber‐to‐field angle dependence reflecting the anisotropic properties of the meniscal collagen fibers. No clear biexponential decay behavior was found for the meniscus substructures.

High-resolution and accelerated multi-parametric mapping with automated characterization of vessel disease using intravascular MRI

BACKGROUND

Atherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. This study tests whether intravascular magnetic resonance imaging (IVMRI) measures of relaxation times (T1, T2) and proton density (PD) in a clinical 3 Tesla scanner could characterize vessel disease, and evaluates a practical strategy for accelerated quantification.

METHODS

IVMRI was performed in fresh human artery segments and swine vessels in vivo, using fast multi-parametric sequences, 1-2 mm diameter loopless antennae and 200-300 μm resolution. T1, T2 and PD data were used to train a machine learning classifier (support vector machine, SVM) to automatically classify normal vessel, and early or advanced disease, using histology for validation. Disease identification using the SVM was tested with receiver operating characteristic curves. To expedite acquisition of T1, T2 and PD data for vessel characterization, the linear algebraic method ('SLAM') was modified to accommodate the antenna's highly-nonuniform sensitivity, and used to provide average T1, T2 and PD measurements from compartments of normal and pathological tissue segmented from high-resolution images at acceleration factors of R ≤ 18-fold. The results were validated using compartment-average measures derived from the high-resolution scans.

RESULTS

The SVM accurately classified ~80% of samples into the three disease classes. The 'area-under-the-curve' was 0.96 for detecting disease in 248 samples, with T1 providing the best discrimination. SLAM T1, T2 and PD measures for R ≤ 10 were indistinguishable from the true means of segmented tissue compartments.

CONCLUSION

High-resolution IVMRI measures of T1, T2 and PD with a trained SVM can automatically classify normal, early and advanced atherosclerosis with high sensitivity and specificity. Replacing relaxometric MRI with SLAM yields good estimates of T1, T2 and PD an order-of-magnitude faster to facilitate IVMRI-based characterization of vessel disease.

Ultrafast compartmentalized relaxation time mapping with linear algebraic modeling

PURPOSE

To dramatically accelerate compartmental-average longitudinal (T₁ ) and transverse (T₂ ) relaxation measurements using the minimal-acquisition linear algebraic modeling (SLAM) method, and to validate it in phantoms and humans.

METHODS

Relaxation times were imaged at 3 Tesla in phantoms, in the abdomens of six volunteers, and in six brain tumor patients using standard inversion recovery and multi-spin-echo sequences. k-space was fully sampled to provide reference T₁ and T₂ measurements, and SLAM was performed using a limited set of phase encodes from central k-space. Anatomical compartments were segmented on scout images post-acquisition, and SLAM reconstruction was implemented using two algorithms. Compartment-average T₁ and T₂ measurements were determined retroactively from fully sampled data sets, and proactively from SLAM data sets at acceleration factors of up to 16. Values were compared with reference measurements. The compartment's localization properties were analyzed using the discrete spatial response function.

RESULTS

At 16-fold acceleration, compartment-average SLAM T₁ measurements agreed with the full k-space compartment-average results to within 0.0% ± 0.7%, 1.4% ± 3.4%, and 0.5% ± 2.9% for phantom, abdominal, and brain T₁ measurements, respectively. The corresponding T₂ measurements agreed within 0.2% ± 1.9%, 0.9% ± 7.9%, and 0.4% ± 5.8%, respectively.

CONCLUSION

SLAM can dramatically accelerate relaxation time measurements when compartmental or lesion-average values can suffice, or when standard relaxometry is precluded by scan-time limitations. Magn Reson Med 79:286-297, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Ultrafast compartmental relaxation time mapping with linear algebraic modeling

Image contrast afforded by tissue longitudinal (T₁) and transverse (T₂) relaxation times is central to the success of modern MRI. Here, a recently-proposed 'spectroscopy with linear algebraic modeling' (SLAM) method is adapted to dramatically accelerate relaxation time imaging at 3 Tesla in phantoms, the abdomens of six volunteers and in six brain tumor patients.. SLAM is validated by omitting up to 15/16^ths (94%) of the data acquired retroactively from inversion recovery and multi-echo spin-echo sequences, and proactively applied to accelerate abdominal and brain tumor T₁ and T₂ measurements by up to 16-fold in humans..

Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies

Accurate quantitative metabolic imaging of the brain presents significant challenges due to the complexity and heterogeneity of its structures and compositions with distinct compartmentations of brain tissue types (e.g., gray and white matter). The brain is compartmentalized into various regions based on their unique functions and locations. In vivo magnetic resonance spectroscopy (MRS) techniques allow non-invasive measurements of neurochemicals in either single voxel or multiple voxels, yet the spatial resolution and detection sensitivity of MRS are significantly lower compared with MRI. A fundamentally different approach, namely spectral localization by imaging (SLIM) provides a new framework that overcomes major limitations of conventional MRS techniques. Conventional MRS allows only rectangular voxel shapes that do not conform to the shapes of brain structures or lesions, while SLIM allows compartments with arbitrary shapes. However, the restrictive assumption proposed in the original concept of SLIM, i.e., compartmental homogeneity, led to spectral localization errors, which have limited its broad applications. This review focuses on the recent technical frontiers of image-based MRS localization techniques that overcome the limitations of SLIM through the development and implementation of various new strategies, including incorporation of magnetic field inhomogeneity corrections, the use of multiple receiver coils, and prospective optimization of data acquisition.

Chemical exchange saturation transfer (CEST) imaging with fast variably-accelerated sensitivity encoding (vSENSE)

PURPOSE

The widespread clinical use of chemical exchange saturation transfer (CEST) imaging is hampered by relatively long scan times due to its requirement that multiple saturation-offset image frames be acquired. Here, a novel variably-accelerated sensitivity encoding (vSENSE) method is proposed that provides faster CEST acquisition than conventional SENSE.

THEORY AND METHODS

The vSENSE method fully samples one CEST saturation frame, then undersamples the other frames variably. The fully-sampled frame, in conjunction with newly proposed incoherence absorption and artifact suppression strategies, improves the accuracy of sensitivity maps and permits higher acceleration factors for the other undersampled frames than regular SENSE. vSENSE is validated in a phantom, a normal volunteer and eight brain tumor patients at 3 Tesla.

RESULTS

vSENSE with an acceleration factor of four generated a 3-6 times smaller error on average than conventional SENSE (P ≤ 0.02), with acceleration factors of 2-4, as compared with full k-space reconstruction. vSENSE permitted four-fold acceleration for amide proton transfer-weighted images, while regular SENSE could only provide a factor of two. When conventional SENSE is used with vSENSE's variable undersampling pattern, erroneous (∼9%) z-spectra result.

CONCLUSION

The vSENSE method enabled twice the acceleration and generated more accurate images than conventional SENSE. Magn Reson Med 77:2225-2238, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

High-resolution (1) H-MRSI of the brain using SPICE: Data acquisition and image reconstruction

PURPOSE

To develop data acquisition and image reconstruction methods to enable high-resolution (1) H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation).

THEORY AND METHODS

SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging /echo-planar spectroscopic imaging sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed echo-planar spectroscopic imaging sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the high-resolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization.

RESULTS

Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high-SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm in-plane and 4 mm through-plane resolution in about 25 min.

CONCLUSION

Special data acquisition and reconstruction methods have been developed for high-resolution (1) H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of (1) H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE. Magn Reson Med 76:1059-1070, 2016. © 2015 Wiley Periodicals, Inc.

Highly accelerated chemical exchange saturation transfer (CEST) measurements with linear algebraic modeling

PURPOSE

In clinical studies, compartmental average chemical exchange saturation transfer (CEST) measurements rather than voxel-by-voxel CEST images may suffice for evaluating its diagnostic value. A recently developed method-spectroscopy with linear algebraic modeling, or SLAM-could directly provide compartmental measures with dramatically reduced scan time and optimal signal-to-noise ratios. Here, we test whether SLAM can be adapted to significantly accelerate CEST acquisitions.

THEORY AND METHODS

Conventional anatomical images and raw CEST image k-space data were acquired from seven brain tumor patients. SLAM was applied to the CEST data using acceleration factors of R = 1-45, after segmenting compartments from co-registered images. SLAM-CEST measures were compared with average values from the identical compartments obtained by conventional Fourier transform (FT) CEST.

RESULTS

SLAM generated compartmental average CEST z-spectra that were indistinguishable from conventional FT-CEST for R ≤ 45. SLAM-CEST z-spectra at ±3.5 ppm were highly correlated with FT-CEST measures (r(2)  ≥ 0.98 for R ≤ 9; r ≥ 0.995 for R ≤ 45). The average error of SLAM-CEST versus FT-CEST measures was ≤10% for R ≤ 45, in acquisitions requiring as few as a single k-space phase-encoding step.

CONCLUSION

Applied to patients with brain tumors, SLAM-CEST can yield results that are quantitatively equivalent to conventional CEST up to 45 times faster, which could prove enabling in clinical settings where scan time is limiting. Magn Reson Med 76:136-144, 2016. © 2015 Wiley Periodicals, Inc.

Minimizing lipid signal bleed in brain (1) H chemical shift imaging by post-acquisition grid shifting

PURPOSE

Low spatial resolution in conventional 1H brain chemical shifting imaging (CSI) studies causes partial volume error (PVE) or signal "bleed" that is especially deleterious to voxels near the scalp. The standard spatial apodization approach adversely affects spatial resolution. Here, a novel automated post-processing strategy of partial volume correction employing grid shifting ("PANGS") is presented, which minimizes residual PVE without compromising spatial resolution.

METHODS

PANGS shifts the locations of the reconstruction coordinates in a designated region of image space-the scalp, to match the tissue "centers-of-mass" instead of the geometric centers of each voxel, by iteratively minimizing the PVE from the scalp into outside voxels. PANGS' performance was evaluated by numerical simulation, and in 3 Tesla 1H CSI human studies employing outer volume suppression and long echo times.

RESULTS

PANGS reduced lipid contamination of cortical spectra by up to 86% (54% on average). Metabolite maps exhibited significantly less lipid artifact than conventional and spatially-filtered CSI. All methods generated quantitatively identical spectral peak areas from central brain locations, but spatial filtering increased spectral linewidths and reduced spatial resolution.

CONCLUSION

PANGS significantly reduces lipid artifacts in 1H brain CSI spectra and metabolite maps, and improves metabolite detection in cortical regions without compromising resolution.

A subspace approach to high-resolution spectroscopic imaging

PURPOSE

To accelerate spectroscopic imaging using sparse sampling of (k,t)-space and subspace (or low-rank) modeling to enable high-resolution metabolic imaging with good signal-to-noise ratio.

METHODS

The proposed method, called SPectroscopic Imaging by exploiting spatiospectral CorrElation, exploits a unique property known as partial separability of spectroscopic signals. This property indicates that high-dimensional spectroscopic signals reside in a very low-dimensional subspace and enables special data acquisition and image reconstruction strategies to be used to obtain high-resolution spatiospectral distributions with good signal-to-noise ratio. More specifically, a hybrid chemical shift imaging/echo-planar spectroscopic imaging pulse sequence is proposed for sparse sampling of (k,t)-space, and a low-rank model-based algorithm is proposed for subspace estimation and image reconstruction from sparse data with the capability to incorporate prior information and field inhomogeneity correction.

RESULTS

The performance of the proposed method has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For two-dimensional spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in signal-to-noise ratio compared to the long chemical shift imaging experiments and with a significant gain in signal-to-noise ratio compared to the accelerated echo-planar spectroscopic imaging experiments.

CONCLUSION

The proposed method, SPectroscopic Imaging by exploiting spatiospectral CorrElation, is able to significantly accelerate spectroscopic imaging experiments, making high-resolution metabolic imaging possible.

Highly-accelerated quantitative 2D and 3D localized spectroscopy with linear algebraic modeling (SLAM) and sensitivity encoding

Noninvasive magnetic resonance spectroscopy (MRS) with chemical shift imaging (CSI) provides valuable metabolic information for research and clinical studies, but is often limited by long scan times. Recently, spectroscopy with linear algebraic modeling (SLAM) was shown to provide compartment-averaged spectra resolved in one spatial dimension with many-fold reductions in scan-time. This was achieved using a small subset of the CSI phase-encoding steps from central image k-space that maximized the signal-to-noise ratio. Here, SLAM is extended to two- and three-dimensions (2D, 3D). In addition, SLAM is combined with sensitivity-encoded (SENSE) parallel imaging techniques, enabling the replacement of even more CSI phase-encoding steps to further accelerate scan-speed. A modified SLAM reconstruction algorithm is introduced that significantly reduces the effects of signal nonuniformity within compartments. Finally, main-field inhomogeneity corrections are provided, analogous to CSI. These methods are all tested on brain proton MRS data from a total of 24 patients with brain tumors, and in a human cardiac phosphorus 3D SLAM study at 3T. Acceleration factors of up to 120-fold versus CSI are demonstrated, including speed-up factors of 5-fold relative to already-accelerated SENSE CSI. Brain metabolites are quantified in SLAM and SENSE SLAM spectra and found to be indistinguishable from CSI measures from the same compartments. The modified reconstruction algorithm demonstrated immunity to maladjusted segmentation and errors from signal heterogeneity in brain data. In conclusion, SLAM demonstrates the potential to supplant CSI in studies requiring compartment-average spectra or large volume coverage, by dramatically reducing scan-time while providing essentially the same quantitative results.