3D spatially encoded and accelerated TE-averaged echo planar spectroscopic imaging in healthy human brain

Using a deep learning prior for accelerating hyperpolarized 13 C MRSI on synthetic cancer datasets

PURPOSE

We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.

METHODS

A two-site exchange model, derived from the Bloch equation of MR signal evolution, was firstly used in simulating training and testing data, that is, synthetic phantom datasets. Five singular maps generated from each simulated dataset were used to train a deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled MRI k-space data. The proposed method was assessed on synthetic human brain tumor images (N = 33), prostate cancer images (N = 72), and mouse tumor images (N = 58) for three undersampling factors and 2.5% additive Gaussian noise. Furthermore, varied levels of Gaussian noise with SDs of 2.5%, 5%, and 10% were added on synthetic prostate cancer data, and corresponding reconstruction results were evaluated.

RESULTS

For quantitative evaluation, peak SNRs were approximately 32 dB, and the accuracy was generally improved for 5 to 8 dB compared with those from compressed sensing with L1-norm regularization or total variation regularization. Reasonable normalized RMS error were obtained. Our method also worked robustly against noise, even on a data with noise SD of 10%.

CONCLUSION

The proposed singular value decomposition + iterative deep learning model could be considered as a general framework that extended the application of deep learning MRI reconstruction to metabolic imaging. The morphology of tumors and metabolic images could be measured robustly in six times acceleration using our method.

Optimized multi-voxel TE-averaged PRESS for glutamate detection in the human brain at 3T

PURPOSE

To optimize possible combinations of echo times (TE) for multi-voxel TE-averaged Point RESolved Spectroscopy (PRESS) while reducing the total number of TEs required to separate glutamate (Glu) and glutamine (Gln) within a clinically feasible scan time.

METHODS

General Approach to Magnetic resonance Mathematical Analysis (GAMMA) was used to implement 2D J-resolved PRESS technique, and the spectra of 14 individual brain metabolites were simulated at 64 different TEs. Monte Carlo simulations were used for selecting the best TE combinations to separate Glu and Gln using TE-averaged PRESS with a total number of two, three, four and five TEs. Single-voxel 1H-MRS data were acquired using 64 different TEs from a healthy volunteer on a clinical 3T MR scanner to validate the echo time combinations selected with simulations. Additionally, 2D 1H-MRSI data of eight healthy volunteers were acquired on a clinical 3T MR scanner using four different TEs that were determined by Monte Carlo simulations. Optimized TE-averaged PRESS spectra were created by averaging the spectra acquired at selected TEs. LCModel was used for spectral quantification. A Wilcoxon signed-rank test was used to detect statistically significant differences in Glu/Gln ratios between 35 ms PRESS and optimized TE-averaged PRESS data.

RESULTS

Glu could be clearly separated from Gln at 2.35 ppm, using optimized TE-averaged PRESS with only four TEs (35, 37, 40, and 42 ms) that were selected through Monte Carlo simulations. Glu/Gln ratios were significantly higher in the optimized TE-averaged PRESS data of healthy volunteers than in the 35 ms PRESS data (P = 0.008).

CONCLUSION

Optimized multi-voxel TE-averaged PRESS enabled faster and unobstructed quantification of Glu at multiple voxels in the human brain in vivo at 3T.

Developments in proton MR spectroscopic imaging of prostate cancer

In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.

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.

Water and lipid suppression techniques for advanced 1 H MRS and MRSI of the human brain: Experts' consensus recommendations

The neurochemical information provided by proton magnetic resonance spectroscopy (MRS) or MR spectroscopic imaging (MRSI) can be severely compromised if strong signals originating from brain water and extracranial lipids are not properly suppressed. The authors of this paper present an overview of advanced water/lipid-suppression techniques and describe their advantages and disadvantages. Moreover, they provide recommendations for choosing the most appropriate techniques for proper use. Methods of water signal handling are primarily focused on the VAPOR technique and on MRS without water suppression (metabolite cycling). The section on lipid-suppression methods in MRSI is divided into three parts. First, lipid-suppression techniques that can be implemented on most clinical MR scanners (volume preselection, outer-volume suppression, selective lipid suppression) are described. Second, lipid-suppression techniques utilizing the combination of k-space filtering, high spatial resolutions and lipid regularization are presented. Finally, three promising new lipid-suppression techniques, which require special hardware (a multi-channel transmit system for dynamic B₁⁺ shimming, a dedicated second-order gradient system or an outer volume crusher coil) are introduced.

Deep learning can accelerate and quantify simulated localized correlated spectroscopy

Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning

Magnetic resonance spectroscopic imaging (SI) is a unique imaging technique that provides biochemical information from in vivo tissues. The 1H spectra acquired from several spatial regions are quantified to yield metabolite concentrations reflective of tissue metabolism. However, since these metabolites are found in tissues at very low concentrations, SI is often acquired with limited spatial resolution. In this work, we test the hypothesis that deep learning is able to upscale low resolution SI, together with the T1-weighted (T1w) image, to reconstruct high resolution SI. We report on a novel densely connected UNet (D-UNet) architecture capable of producing super-resolution spectroscopic images. The inputs for the D-UNet are the T1w image and the low resolution SI image while the output is the high resolution SI. The results of the D-UNet are compared both qualitatively and quantitatively to simulated and in vivo high resolution SI. It is found that this deep learning approach can produce high quality spectroscopic images and reconstruct entire 1H spectra from low resolution acquisitions, which can greatly advance the current SI workflow.

Focus on the glycerophosphocholine pathway in choline phospholipid metabolism of cancer

Activated choline metabolism is a hallmark of carcinogenesis and tumor progression, which leads to elevated levels of phosphocholine and glycerophosphocholine in all types of cancer tested so far. Magnetic resonance spectroscopy applications have played a key role in detecting these elevated choline phospholipid metabolites. To date, the majority of cancer-related studies have focused on phosphocholine and the Kennedy pathway, which constitutes the biosynthesis pathway for membrane phosphatidylcholine. Fewer and more recent studies have reported on the importance of glycerophosphocholine in cancer. In this review article, we summarize the recent literature on glycerophosphocholine metabolism with respect to its cancer biology and its detection by magnetic resonance spectroscopy applications.

Density-weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

PURPOSE

Full-slice magnetic resonance spectroscopic imaging at ≥7 T is especially vulnerable to lipid contaminations arising from regions close to the skull. This contamination can be mitigated by improving the point spread function via higher spatial resolution sampling and k-space filtering, but this prolongs scan times and reduces the signal-to-noise ratio (SNR) efficiency. Currently applied parallel imaging methods accelerate magnetic resonance spectroscopic imaging scans at 7T, but increase lipid artifacts and lower SNR-efficiency further. In this study, we propose an SNR-efficient spatial-spectral sampling scheme using concentric circle echo planar trajectories (CONCEPT), which was adapted to intrinsically acquire a Hamming-weighted k-space, thus termed density-weighted-CONCEPT. This minimizes voxel bleeding, while preserving an optimal SNR.

THEORY AND METHODS

Trajectories were theoretically derived and verified in phantoms as well as in the human brain via measurements of five volunteers (single-slice, field-of-view 220 × 220 mm2 , matrix 64 × 64, scan time 6 min) with free induction decay magnetic resonance spectroscopic imaging. Density-weighted-CONCEPT was compared to (a) the originally proposed CONCEPT with equidistant circles (here termed e-CONCEPT), (b) elliptical phase-encoding, and (c) 5-fold Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase-encoding.

RESULTS

By intrinsically sampling a Hamming-weighted k-space, density-weighted-CONCEPT removed Gibbs-ringing artifacts and had in vivo +9.5%, +24.4%, and +39.7% higher SNR than e-CONCEPT, elliptical phase-encoding, and the Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase-encoding (all P < 0.05), respectively, which lead to improved metabolic maps.

CONCLUSION

Density-weighted-CONCEPT provides clinically attractive full-slice high-resolution magnetic resonance spectroscopic imaging with optimal SNR at 7T. Magn Reson Med 79:2874-2885, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T

PURPOSE

The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE ¹ H FID MRSI sequence at 9.4T.

METHODS

X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed ¹ H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared.

RESULTS

Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved.

CONCLUSION

By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min.

Covariance J-resolved spectroscopy: Theory and application in vivo

Magnetic resonance spectroscopy (MRS) is a powerful tool capable of investigating the metabolic status of several tissues in vivo. In particular, single-voxel-based ¹ H spectroscopy provides invaluable biochemical information from a volume of interest (VOI) and has therefore been used in a variety of studies. Unfortunately, typical one-dimensional MRS data suffer from severe signal overlap and thus important metabolites are difficult to distinguish. One method that is used to disentangle overlapping resonances is the two-dimensional J-resolved spectroscopy (JPRESS) experiment. Due to the long acquisition duration of the JPRESS experiment, a limited number of points are acquired in the indirect dimension, leading to poor spectral resolution along this dimension. Poor spectral resolution is problematic because proper peak assignment may be hindered, which is why the zero-filling method is often used to improve resolution as a post-processing step. However, zero-filling leads to spectral artifacts, which may affect visualization and quantitation of spectra. A novel method utilizing a covariance transformation, called covariance J-resolved spectroscopy (CovJ), was developed in order to improve spectral resolution along the indirect dimension (F₁ ). Comparison of simulated data demonstrates that peak structures remain qualitatively similar between JPRESS and the novel method along the diagonal region (F₁ = 0 Hz), whereas differences arise in the cross-peak (F₁ ≠0 Hz) regions. In addition, quantitative results of in vivo JPRESS data acquired on a 3T scanner show significant correlations (r² >0.86, p<0.001) when comparing the metabolite concentrations between the two methods. Finally, a quantitation algorithm, 'COVariance Spectral Evaluation of ¹ H Acquisitions using Representative prior knowledge' (Cov-SEHAR), was developed in order to quantify γ-aminobutyric acid and glutamate from the CovJ spectra. These preliminary findings indicate that the CovJ method may be used to improve spectral resolution without hindering metabolite quantitation for J-resolved spectra.

Prior-knowledge Fitting of Accelerated Five-dimensional Echo Planar J-resolved Spectroscopic Imaging: Effect of Nonlinear Reconstruction on Quantitation

¹H Magnetic Resonance Spectroscopic imaging (SI) is a powerful tool capable of investigating metabolism in vivo from mul- tiple regions. However, SI techniques are time consuming, and are therefore difficult to implement clinically. By applying non-uniform sampling (NUS) and compressed sensing (CS) reconstruction, it is possible to accelerate these scans while re- taining key spectral information. One recently developed method that utilizes this type of acceleration is the five-dimensional echo planar J-resolved spectroscopic imaging (5D EP-JRESI) sequence, which is capable of obtaining two-dimensional (2D) spectra from three spatial dimensions. The prior-knowledge fitting (ProFit) algorithm is typically used to quantify 2D spectra in vivo, however the effects of NUS and CS reconstruction on the quantitation results are unknown. This study utilized a simulated brain phantom to investigate the errors introduced through the acceleration methods. Errors (normalized root mean square error >15%) were found between metabolite concentrations after twelve-fold acceleration for several low concentra- tion (<2 mM) metabolites. The Cramér Rao lower bound% (CRLB%) values, which are typically used for quality control, were not reflective of the increased quantitation error arising from acceleration. Finally, occipital white (OWM) and gray (OGM) human brain matter were quantified in vivo using the 5D EP-JRESI sequence with eight-fold acceleration.

Pilot Assessment of Brain Metabolism in Perinatally HIV-Infected Youths Using Accelerated 5D Echo Planar J-Resolved Spectroscopic Imaging

Purpose

To measure cerebral metabolite levels in perinatally HIV-infected youths and healthy controls using the accelerated five dimensional (5D) echo planar J-resolved spectroscopic imaging (EP-JRESI) sequence, which is capable of obtaining two dimensional (2D) J-resolved spectra from three spatial dimensions (3D).

Materials and Methods

After acquisition and reconstruction of the 5D EP-JRESI data, T₁-weighted MRIs were used to classify brain regions of interest for HIV patients and healthy controls: right frontal white (FW), medial frontal gray (FG), right basal ganglia (BG), right occipital white (OW), and medial occipital gray (OG). From these locations, respective J-resolved and TE-averaged spectra were extracted and fit using two different quantitation methods. The J-resolved spectra were fit using prior knowledge fitting (ProFit) while the TE-averaged spectra were fit using the advanced method for accurate robust and efficient spectral fitting (AMARES).

Results

Quantitation of the 5D EP-JRESI data using the ProFit algorithm yielded significant metabolic differences in two spatial locations of the perinatally HIV-infected youths compared to controls: elevated NAA/(Cr+Ch) in the FW and elevated Asp/(Cr+Ch) in the BG. Using the TE-averaged data quantified by AMARES, an increase of Glu/(Cr+Ch) was shown in the FW region. A strong negative correlation (r < -0.6) was shown between tCh/(Cr+Ch) quantified using ProFit in the FW and CD4 counts. Also, strong positive correlations (r > 0.6) were shown between Asp/(Cr+Ch) and CD4 counts in the FG and BG.

Conclusion

The complimentary results using ProFit fitting of J-resolved spectra and AMARES fitting of TE-averaged spectra, which are a subset of the 5D EP-JRESI acquisition, demonstrate an abnormal energy metabolism in the brains of perinatally HIV-infected youths. This may be a result of the HIV pathology and long-term combinational anti-retroviral therapy (cART). Further studies of larger perinatally HIV-infected cohorts are necessary to confirm these findings.