Truncation artifact reduction in spectroscopic imaging using a dual-density spiral k-space trajectory

Proton metabolic mapping of the brain at 7 T using a two-dimensional free induction decay-echo-planar spectroscopic imaging readout with lipid suppression

The increased signal-to-noise ratio (SNR) and chemical shift dispersion at high magnetic fields (≥7 T) have enabled neuro-metabolic imaging at high spatial resolutions. To avoid very long acquisition times with conventional magnetic resonance spectroscopic imaging (MRSI) phase-encoding schemes, solutions such as pulse-acquire or free induction decay (FID) sequences with short repetition time and inner volume selection methods with acceleration (echo-planar spectroscopic imaging [EPSI]), have been proposed. With the inner volume selection methods, limited spatial coverage of the brain and long echo times may still impede clinical implementation. FID-MRSI sequences benefit from a short echo time and have a high SNR per time unit; however, contamination from strong extra-cranial lipid signals remains a problem that can hinder correct metabolite quantification. L2-regularization can be applied to remove lipid signals in cases with high spatial resolution and accurate prior knowledge. In this work, we developed an accelerated two-dimensional (2D) FID-MRSI sequence using an echo-planar readout and investigated the performance of lipid suppression by L2-regularization, an external crusher coil, and the combination of these two methods to compare the resulting spectral quality in three subjects. The reduction factor of lipid suppression using the crusher coil alone varies from 2 to 7 in the lipid region of the brain boundary. For the combination of the two methods, the average lipid area inside the brain was reduced by 2% to 38% compared with that of unsuppressed lipids, depending on the subject's region of interest. 2D FID-EPSI with external lipid crushing and L2-regularization provides high in-plane coverage and is suitable for investigating brain metabolite distributions at high fields.

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.

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

Water removal in MR spectroscopic imaging with L2 regularization

PURPOSE

An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T.

METHODS

Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals.

RESULTS

The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect.

CONCLUSIONS

With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.

Fast data acquisition techniques in magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) is an important technique for assessing the spatial variation of metabolites in vivo. The long scan times in MRSI limit clinical applicability due to patient discomfort, increased costs, motion artifacts, and limited protocol flexibility. Faster acquisition strategies can address these limitations and could potentially facilitate increased adoption of MRSI into routine clinical protocols with minimal addition to the current anatomical and functional acquisition protocols in terms of imaging time. Not surprisingly, a lot of effort has been devoted to the development of faster MRSI techniques that aim to capture the same underlying metabolic information (relative metabolite peak areas and spatial distribution) as obtained by conventional MRSI, in greatly reduced time. The gain in imaging time results, in some cases, in a loss of signal-to-noise ratio and/or in spatial and spectral blurring. This review examines the current techniques and advances in fast MRSI in two and three spatial dimensions and their applications. This review categorizes the acceleration techniques according to their strategy for acquisition of the k-space. Techniques such as fast/turbo-spin echo MRSI, echo-planar spectroscopic imaging, and non-Cartesian MRSI effectively cover the full k-space in a more efficient manner per T_R . On the other hand, techniques such as parallel imaging and compressed sensing acquire fewer k-space points and employ advanced reconstruction algorithms to recreate the spatial-spectral information, which maintains statistical fidelity in test conditions (ie no statistically significant differences on voxel-wise comparisions) with the fully sampled data. The advantages and limitations of each state-of-the-art technique are reviewed in detail, concluding with a note on future directions and challenges in the field of fast spectroscopic imaging.

Compartmentalized low-rank recovery for high-resolution lipid unsuppressed MRSI

PURPOSE

To introduce a novel algorithm for the recovery of high-resolution magnetic resonance spectroscopic imaging (MRSI) data with minimal lipid leakage artifacts, from dual-density spiral acquisition.

METHODS

The reconstruction of MRSI data from dual-density spiral data is formulated as a compartmental low-rank recovery problem. The MRSI dataset is modeled as the sum of metabolite and lipid signals, each of which is support limited to the brain and extracranial regions, respectively, in addition to being orthogonal to each other. The reconstruction problem is formulated as an optimization problem, which is solved using iterative reweighted nuclear norm minimization.

RESULTS

The comparisons of the scheme against dual-resolution reconstruction algorithm on numerical phantom and in vivo datasets demonstrate the ability of the scheme to provide higher spatial resolution and lower lipid leakage artifacts. The experiments demonstrate the ability of the scheme to recover the metabolite maps, from lipid unsuppressed datasets with echo time (TE) = 55 ms.

CONCLUSION

The proposed reconstruction method and data acquisition strategy provide an efficient way to achieve high-resolution metabolite maps without lipid suppression. This algorithm would be beneficial for fast metabolic mapping and extension to multislice acquisitions. Magn Reson Med 78:1267-1280, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Magnetic resonance spectroscopic imaging at superresolution: Overview and perspectives

The notion of non-invasive, high-resolution spatial mapping of metabolite concentrations has long enticed the medical community. While magnetic resonance spectroscopic imaging (MRSI) is capable of achieving the requisite spatio-spectral localization, it has traditionally been encumbered by significant resolution constraints that have thus far undermined its clinical utility. To surpass these obstacles, research efforts have primarily focused on hardware enhancements or the development of accelerated acquisition strategies to improve the experimental sensitivity per unit time. Concomitantly, a number of innovative reconstruction techniques have emerged as alternatives to the standard inverse discrete Fourier transform (DFT). While perhaps lesser known, these latter methods strive to effect commensurate resolution gains by exploiting known properties of the underlying MRSI signal in concert with advanced image and signal processing techniques. This review article aims to aggregate and provide an overview of the past few decades of so-called "superresolution" MRSI reconstruction methodologies, and to introduce readers to current state-of-the-art approaches. A number of perspectives are then offered as to the future of high-resolution MRSI, with a particular focus on translation into clinical settings.

In vivo brain rosette spectroscopic imaging (RSI) with LASER excitation, constant gradient strength readout, and automated LCModel quantification for all voxels

PURPOSE

To optimize the Rosette trajectories for high-sensitivity in vivo brain spectroscopic imaging and reduced gradient demands.

METHODS

Using LASER localization, a rosette based sampling scheme for in vivo brain spectroscopic imaging data on a 3 Tesla (T) system is described. The two-dimensional (2D) and 3D rosette spectroscopic imaging (RSI) data were acquired using 20 × 20 in-plane resolution (8 × 8 mm(2) ), and 1 (2D) -18 mm (1.1 cc) or 12 (3D) -8 mm partitions (0.5 cc voxels). The performance of the RSI acquisition was compared with a conventional spectroscopic imaging (SI) sequence using LASER localization and 2D or 3D elliptical phase encoding (ePE). Quantification of the entire RSI data set was performed using an LCModel based pipeline.

RESULTS

The RSI acquisitions took 32 s for the 2D scan, and as short as 5 min for the 3D 20 × 20 × 12 scan, using a maximum gradient strength Gmax=5.8 mT/m and slew-rate Smax=45 mT/m/ms. The Bland-Altman agreement between RSI and ePE CSI, characterized by the 95% confidence interval for their difference (RSI-ePE), is within 13% of the mean (RSI+ePE)/2. Compared with the 3D ePE at the same nominal resolution, the effective RSI voxel size was three times smaller while the measured signal-to-noise ratio sensitivity, after normalization for differences in effective size, was 43% greater.

CONCLUSION

3D LASER-RSI is a fast, high-sensitivity spectroscopic imaging sequence, which can acquire medium-to-high resolution SI data in clinically acceptable scan times (5-10 min), with reduced stress on the gradient system. Magn Reson Med 76:380-390, 2016. © 2015 Wiley Periodicals, Inc.

Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model

PURPOSE

To remove nuisance signals (e.g., water and lipid signals) for (1) H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage.

METHODS

A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage.

RESULTS

The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k-space coverage, the proposed method produced "lipid-free" spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals.

CONCLUSION

Nuisance signals in (1) H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from (1) H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution (1) H MRSI of the brain.

Fast image reconstruction with L2-regularization

PURPOSE

We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction.

MATERIALS AND METHODS

We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality.

RESULTS

The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation.

CONCLUSION

For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.

Radiofrequency field inhomogeneity compensation in high spatial resolution magnetic resonance spectroscopic imaging

Clinical magnetic resonance spectroscopy imaging (MRSI) is a non-invasive functional technique, whose mathematical framework falls into the category of linear inverse problems. However, its use in medical diagnostics is hampered by two main problems, both linked to the Fourier-based technique usually implemented for spectra reconstruction: poor spatial resolution and severe blurring in the spatial localization of the reconstructed spectra. Moreover, the intrinsic ill-posedness of the MRSI problem might be worsened by (i) spatially dependent distortions of the static magnetic field (B0) distribution, as well as by (ii) inhomogeneity in the power deposition distribution of the radiofrequency magnetic field (B1). Among several alternative methods, slim (Spectral Localization by IMaging) and bslim (B0 compensated slim) are reconstruction algorithms in which a priori information concerning the spectroscopic target is introduced into the reconstruction kernel. Nonetheless, the influence of the B1 field, particularly when its operating wavelength is close to the size of the human organs being studied, continues to be disregarded. starslim (STAtic and Radiofrequency-compensated slim), an evolution of the slim and bslim methods, is therefore proposed, in which the transformation kernel also includes the B1 field inhomogeneity map, thus allowing almost complete 3D modelling of the MRSI problem. Moreover, an original method for the experimental determination of the B1 field inhomogeneity map specific to the target under evaluation is also included. The compensation capabilities of the proposed method have been tested and illustrated using synthetic raw data reproducing the human brain.

Data-driven MRSI spectral localization via low-rank component analysis

Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.

Investigation of the PSF-choice method for reduced lipid contamination in prostate MR spectroscopic imaging

The purpose of this work was to evaluate a previously proposed approach that aims to improve the point spread function (PSF) of MR spectroscopic imaging (MRSI) to avoid corruption by lipid signal arising from neighboring voxels. Retrospective spatial filtering can be used to alter the PSF; however, this either reduces spatial resolution or requires extending the acquisition in k-space at the cost of increased imaging time. Alternatively, the method evaluated here, PSF-choice, can modify the PSF localization to reduce the contamination from adjacent lipids by conforming the signal response more closely to the desired MRSI voxel grid. This is done without increasing scan time or degrading SNR of important metabolites. PSF-choice achieves improvements in spatial localization through modifications to the radiofrequency excitation pulses. An implementation of this method is reported for MRSI of the prostate, where it is demonstrated that, in 13 of 16 pilot prostate MRSI scans, intravoxel spectral contamination from lipid was significantly reduced when using PSF-choice. Phantom studies were also performed that demonstrate, compared with MRSI with standard Fourier phase encoding, out-of-voxel signal contamination of spectra was significantly reduced in MRSI with PSF-choice.

Removal of truncation artefacts in magnetic resonance images by recovering missing spectral data

Truncation artefacts are often present in many archived clinical magnetic resonance (MR) images due to the need of shortening the acquisition time by sampling a part of their k-space. This artificial information degrades the quality of the image and may hamper clinical diagnosis. In this paper, we propose a novel method to remove the artefacts by recovering the missing k-space or spectral data. The method consists of four steps: (a) estimating the truncated k-space from the images containing truncations artefacts, (b) computing the parameters of the sparse representation of the difference image of an image from the estimated truncated k-space, (c) recovering the missing spectral data using the parameters computed in (b), and (d) obtaining the artefact-removed image through inverse Fourier transform of the estimated and the recovered spectral data. Experiments on both simulated and real MR images have shown that the proposed method effectively removes truncation artefacts while preserving image quality and outperforms both the conventional Hamming window method and the popular TV method.

Lipid suppression in CSI with spatial priors and highly undersampled peripheral k-space

Mapping 1H brain metabolites using chemical shift imaging is hampered by the presence of subcutaneous lipid signals, which contaminate the metabolites by ringing due to limited spatial resolution. Even though chemical shift imaging at spatial resolution high enough to mitigate the lipid artifacts is infeasible due to signal-to-noise constraints on the metabolites, the lipid signals have orders of magnitude of higher concentration, which enables the collection of high-resolution lipid maps with adequate signal-to-noise. The previously proposed dual-density approach exploits this high signal-to-noise property of the lipid layer to suppress truncation artifacts using high-resolution lipid maps. Another recent approach for lipid suppression makes use of the fact that metabolite and lipid spectra are approximately orthogonal, and seeks sparse metabolite spectra when projected onto lipid-basis functions. This work combines and extends the dual-density approach and the lipid-basis penalty, while estimating the high-resolution lipid image from 2-average k-space data to incur minimal increase on the scan time. Further, we exploit the spectral-spatial sparsity of the lipid ring and propose to estimate it from substantially undersampled (acceleration R=10 in the peripheral k-space) 2-average in vivo data using compressed sensing and still obtain improved lipid suppression relative to using dual-density or lipid-basis penalty alone.

MR spectroscopy and spectroscopic imaging of the brain

Magnetic resonance spectroscopy (MRS) and the related technique of magnetic resonance spectroscopic imaging (MRSI) are widely used in both clinical and preclinical research for the non-invasive evaluation of brain metabolism. They are also used in medical practice, although their ultimate clinical value continues to be a source of discussion. This chapter reviews the general information content of brain spectra and commonly used protocols for both MRS and MRSI and also touches on data analysis methods and quantitation. The main focus is on proton MRS for application in humans, but many of the methods are also applicable to other nuclei and studies of animal models as well.

GA-BASED OPTIMIZATION OF TAPERING WINDOWS FOR ARTIFACT REDUCTION IN FOURIER ELECTRON MAGNETIC RESONANCE IMAGES

Optimization of tapering windows for artifact reduction in two-dimensional (2D) Fourier electron magnetic resonance (EMR) tomography using genetic algorithm (GA) is presented. EMR imaging (EMRI) is a fast emerging functional imaging modality for studying free radicals in biological systems. EMRI by single point imaging (SPI) modality is a Fourier imaging technique. The bioclearance of the imaging agent as well as the need to minimize the radio frequency power deposition on the live animals, dictate reduced k-space sampling. This leads to ringing (Gibbs) artifacts in both directions of the 2D image, because, unlike the conventional MRI, SPI is phase encoding in both directions. To dampen the high-frequency components, data tapering windows are multiplicatively applied to provide tolerable blurred resultant image with reduced Gibbs ringing. To find a compromise between blur and ringing artifact, in this paper a method of optimizing the window functions by using GA is proposed. Our experiments suggest GA-based Kaiser window shows better performance by visual as well as quantitative evaluation.

BSLIM: spectral localization by imaging with explicit B0 field inhomogeneity compensation

Magnetic resonance spectroscopy imaging (MRSI) is an attractive tool for medical imaging. However, its practical use is often limited by the intrinsic low spatial resolution and long acquisition time. Spectral localization by imaging (SLIM) has been proposed as a non-Fourier reconstruction algorithm that incorporates spatial a priori information about spectroscopically uniform compartments. Unfortunately, the influence of the magnetic field inhomogeneity--in particular, the susceptibility effects at tissues' boundaries--undermines the validity of the compartmental model. Therefore, we propose BSLIM as an extension of SLIM with field inhomogeneity compensation. A B0-field inhomogeneity map, which can be acquired rapidly and at high resolution, is used by the new algorithm as additional a priori information. We show that the proposed method is distinct from the generalized SLIM (GSLIM) framework. Experimental results of a two-compartment phantom demonstrate the feasibility of the method and the importance of inhomogeneity compensation.

PSF-choice: a novel MRI method for shaping point-spread functions in phase-encoding dimensions

An imaging method to obtain arbitrary point-spread functions (PSFs) in phase-encoding dimensions is described. This method, called PSF-Choice, is particularly relevant for applications, such as spectroscopic imaging, in which only a very few phase encodes are acquired and ringing artifact can be a serious problem. PSF-Choice uses partial 2D RF excitations to produce aliased excitations that are encoded using standard phase-encoding gradients. Theoretically, the PSF of the reconstructed result depends only on the RF excitation profile. Simulations demonstrate that a Gaussian-like PSF can be achieved, eliminating the side lobes that are associated with ringing artifact. It is further shown that neither the spatial resolution (as represented by the width of the PSF) nor the signal-to-noise ratio (SNR) of the method is adversely affected when compared to standard phase encoding. In the sense that the same number of encodes are required as with standard phase encoding, temporal resolution is also maintained. Phantom experiments demonstrate the initial feasibility of the method to eliminate ringing artifact.