Spectral resolution amelioration by deconvolution (SPREAD) in MR spectroscopic imaging

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.

Effect of linewidth on estimation of metabolic concentration when using water lineshape spectral model fitting for single voxel proton spectroscopy at 7 T

Good B₀ field homogeneity is considered an essential requirement to obtain high-quality MRS data. Many commonly used spectral fitting methods assume that all metabolite signals have Lorentzian or Gaussian shapes. However, B₀ inhomogeneity can both broaden the linewidth and modify the lineshape. In this study, it is hypothesized that a realistic metabolite fitting model, which accounts for B₀ homogeneity on the basis of the water lineshape, will improve the accuracy of estimation of metabolite concentrations. In-vivo water suppressed/unsuppressed single voxel spectroscopy signals were acquired under three different B₀ field homogeneity regimes. Individual realistic basis sets were created for each acquisition. Frequency-domain spectral fitting with LCModel was used to quantify the metabolite concentrations with fitting uncertainties given in terms of the Cramer-Rao lower bound. The quantification results obtained using the water lineshape basis set yielded similar concentrations independent of linewidth and showed a larger fitting error as the linewidth increased. The conventional approach, however quantifies metabolite concentrations with greater variations despite showing a supposedly improved fitting quality. The water lineshape basis set achieved single voxel spectroscopy accuracy that is less sensitive to the linewidth compared to the conventional spectral fitting method for the range of linewidths used in this study, but the precision deteriorated with worsening B₀ field inhomogeneity. The beneficial effect was ascribed to a reduction in the number of degrees of freedom when using the water lineshape to generate the basis set.

B0-adjusted and sensitivity-encoded spectral localization by imaging (BASE-SLIM) in the human brain in vivo

Spectral localization by imaging (SLIM) based magnetic resonance spectroscopy (MRS) provides a framework that overcomes major limitations of current MRS techniques, which allow only rectangular voxel shapes that do not conform to the shapes of brain structures or lesions. However, the restrictive assumption of compartmental homogeneity in SLIM can lead to localization errors, thus its applications have been very limited to date. SLIM-based localization is subject to errors due to inhomogeneous B0 and B1 fields, particularly in organs with complex compartmental geometry including the human brain. The limitations of SLIM were overcome through the development and implementation of B0-adjusted and sensitivity-encoded SLIM (BASE-SLIM) that includes corrections for inhomogeneities of both B0 and B1 fields throughout the volume of interest. In this study, we demonstrate significantly improved localization accuracy in compartments with arbitrary shapes and reliable quantification of metabolite concentrations in gray and white matter of the human brain using the BASE-SLIM technique.

Spectral fitting using basis set modified by measured B0 field distribution

This study sought to demonstrate and evaluate a novel spectral fitting method to improve quantification accuracy in the presence of large magnetic field distortion, especially with high fields. MRS experiments were performed using a point-resolved spectroscopy (PRESS)-type sequence at 7 T. A double-echo gradient echo (GRE) sequence was used to acquire B0 maps following MRS experiments. The basis set was modified based on the measured B0 distribution within the MRS voxel. Quantification results were obtained after fitting the measured MRS data using the modified basis set. The proposed method was validated using numerical Monte Carlo simulations, phantom measurements, and comparison of occipital lobe MRS measurements under homogeneous and inhomogeneous magnetic field conditions. In vivo results acquired from voxels placed in thalamus and prefrontal cortex regions close to the frontal sinus agreed well with published values. Instead of noise-amplifying complex division, the proposed method treats field variations as part of the signal model, thereby avoiding inherent statistical bias associated with regularization. Simulations and experiments showed that the proposed approach reliably quantified results in the presence of relatively large magnetic field distortion. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Improved Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data Corrupted by Noise and B₀ Field Inhomogeneity

GOAL

To improve the signal-to-noise ratio (SNR) of magnetic resonance spectroscopic imaging (MRSI) data.

METHODS

A low-rank filtering method recently proposed for denoising MRSI data is extended by: 1) incorporating tissue boundary constraints to enable local low-rank filtering, and 2) integrating B0 field inhomogeneity correction by rank-minimization to make the low-rank model more effective.

RESULTS

The proposed method was validated using both simulated and in vivo MRSI data. Its denoising performance is also compared with an upper bound based on the constrained Cramér-Rao lower bound for low-rank filtering.

CONCLUSION

Low-rank filtering can effectively improve the SNR of MRSI data corrupted by both noise and B0 field inhomogeneity.

SIGNIFICANCE

The proposed low-rank filtering method will enhance the practical utility of high-resolution MRSI, where SNR has been a limiting factor.

Proton MRS and MRSI of the brain without water suppression

Water suppression (WS) techniques have played a vital role in the commencement and development of in vivo proton magnetic resonance spectroscopy (MRS, including spectroscopic imaging - MRSI). WS not only made in vivo proton MRS functionally available but also made its applications conveniently accessible, and it has become an indispensable tool in most of the routine applications of in vivo proton MR spectroscopy. On the other hand, WS brought forth some challenges. Therefore, various techniques of proton MRS without WS have been developed since the pioneering work in the late 1990s. After more than one and a half decades of advances in both hardware and software, non-water-suppressed proton MRS is coming to the stage of maturity and seeing increasing application in biomedical research and clinical diagnosis. In this article, we will review progress in the technical development and applications of proton MRS without WS.

Improving the spectral resolution and spectral fitting of (1) H MRSI data from human calf muscle by the SPREAD technique

Proton magnetic resonance spectroscopic imaging ((1) H MRSI) has been used for the in vivo measurement of intramyocellular lipids (IMCLs) in human calf muscle for almost two decades, but the low spectral resolution between extramyocellular lipids (EMCLs) and IMCLs, partially caused by the magnetic field inhomogeneity, has hindered the accuracy of spectral fitting. The purpose of this paper was to enhance the spectral resolution of (1) H MRSI data from human calf muscle using the SPREAD (spectral resolution amelioration by deconvolution) technique and to assess the influence of improved spectral resolution on the accuracy of spectral fitting and on in vivo measurement of IMCLs. We acquired MRI and (1) H MRSI data from calf muscles of three healthy volunteers. We reconstructed spectral lineshapes of the (1) H MRSI data based on field maps and used the lineshapes to deconvolve the measured MRS spectra, thereby eliminating the line broadening caused by field inhomogeneities and improving the spectral resolution of the (1) H MRSI data. We employed Monte Carlo (MC) simulations with 200 noise realizations to measure the variations of spectral fitting parameters and used an F-test to evaluate the significance of the differences of the variations between the spectra before SPREAD and after SPREAD. We also used Cramer-Rao lower bounds (CRLBs) to assess the improvements of spectral fitting after SPREAD. The use of SPREAD enhanced the separation between EMCL and IMCL peaks in (1) H MRSI spectra from human calf muscle. MC simulations and F-tests showed that the use of SPREAD significantly reduced the standard deviations of the estimated IMCL peak areas (p < 10(-8) ), and the CRLBs were strongly reduced (by ~37%).

N-acetyl-aspartyl-glutamate detection in the human brain at 7 Tesla by echo time optimization and improved Wiener filtering

PURPOSE

To report enhanced signal detection for measuring N-acetyl-aspartyl-glutamate (NAAG) in the human brain at 7 Tesla by echo time (TE) -optimized point-resolved spectroscopy (PRESS) and improved Wiener filtering.

METHODS

Using a highly efficient in-house developed numerical simulation program, a PRESS sequence with (TE1 , TE2 ) = (26, 72) ms was found to maximize the NAAG signals relative to the overlapping Glu signals. A new Wiener filtering water reference deconvolution method was developed to reduce broadening and distortions of metabolite peaks caused by B0 inhomogeneity and eddy currents.

RESULTS

Monte Carlo simulation results demonstrated that the new Wiener filtering method offered higher spectral resolution, reduced spectral artifacts, and higher accuracy in NAAG quantification compared with the original Wiener filtering method. In vivo spectra and point spread functions of signal distortion confirmed that the new Wiener filtering method lead to improved spectral resolution and reduced spectral artifacts.

CONCLUSION

TE-optimized PRESS in combination with a new Wiener filtering method made it possible to fully use both the NAAG singlet signal at 2.05 ppm and the NAAG multiplet signal at 2.18 ppm in the quantification of NAAG. A more accurate characterization of lineshape distortion for Wiener filtering needs B0 field maps and segmented anatomical images to exclude contribution from cerebral spinal fluid.

Correction of field inhomogeneity effects on limited k-space MRSI data using anatomical constraints

Magnetic field inhomogeneity is a long-standing problem in magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI). Specifically, in MRSI, field inhomogeneity, if not corrected, can cause frequency shifts, line broadening, and lineshape distortions in the spectral peaks. This paper addresses the problem of correcting the field inhomogeneity effects on limited k-space MRSI data. A penalized maximum-likelihood method is proposed, which enables the use of anatomical constraints for improving the correction performance with only limited k-space data. Simulation results are shown to demonstrate the effectiveness of the proposed method.