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

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

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

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

Dynamic hyperpolarized 13C MR spectroscopic imaging using SPICE in mouse kidney at 9.4 T

This study aims to investigate the feasibility of dynamic hyperpolarized ¹³C MR spectroscopic imaging (MRSI) using the SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) technique and an estimation of the spatially resolved conversion constant rate (k_pl ). An acquisition scheme comprising a single training dataset and several imaging datasets was proposed considering hyperpolarized ¹³C circumstances. The feasibility and advantage of the scheme were investigated in two parts: (a) consistency of spectral basis over time and (b) accuracy of the estimated k_pl . The simulations and in vivo experiments support accurate k_pl estimation with consistent spectral bases. The proposed method was implemented in an enzyme phantom and via in vivo experiments. In the enzyme phantom experiments, spatially resolved homogeneous k_pl maps were observed. In the in vivo experiments, normal diet (ND) mice and high‐fat diet (HFD) mice had k_pl (s^?1 ) values of medullar (ND: 0.0119 ± 0.0022, HFD: 0.0195 ± 0.0005) and cortical (ND: 0.0148 ± 0.0023, HFD: 0.0224 ± 0.0054) regions which were higher than vascular (ND: 0.0087 ± 0.0013, HFD: 0.0132 ± 0.0050) regions. In particular, the k_pl value in the medullar region exhibited a significant difference between the two diet groups. In summary, the feasibility of using modified SPICE for dynamic hyperpolarized ¹³C MRSI was demonstrated via simulations and in vivo experiments. The consistency of spectral bases over time and the accuracy of the estimated k_pl values validate the proposed acquisition scheme, which comprises only a single training dataset. The proposed method improved the spatial resolution of dynamic hyperpolarized ¹³C MRSI, which could be used for k_pl estimation using high signal‐to‐noise ratio spectral bases.     

Low SAR 31P (multi‐echo) spectroscopic imaging using an integrated whole‐body transmit coil at 7T

Phosphorus (³¹P) MRSI provides opportunities to monitor potential biomarkers. However, current applications of ³¹P MRS are generally restricted to relatively small volumes as small coils are used. Conventional surface coils require high energy adiabatic RF pulses to achieve flip angle homogeneity, leading to high specific absorption rates (SARs), and occupy space within the MRI bore. A birdcage coil behind the bore cover can potentially reduce the SAR constraints massively by use of conventional amplitude modulated pulses without sacrificing patient space. Here, we demonstrate that the integrated ³¹P birdcage coil setup with a high power RF amplifier at 7 T allows for low flip angle excitations with short repetition time (T_R) for fast 3D chemical shift imaging (CSI) and 3D T₁‐weighted CSI as well as high flip angle multi‐refocusing pulses, enabling multi‐echo CSI that can measure metabolite T₂, over a large field of view in the body. B₁⁺ calibration showed a variation of only 30% in maximum B₁ in four volunteers. High signal‐to‐noise ratio (SNR) MRSI was obtained in the gluteal muscle using two fast in vivo 3D spectroscopic imaging protocols, with low and high flip angles, and with multi‐echo MRSI without exceeding SAR levels. In addition, full liver MRSI was achieved within SAR constraints. The integrated ³¹P body coil allowed for fast spectroscopic imaging and successful implementation of the multi‐echo method in the body at 7 T. Moreover, no additional enclosing hardware was needed for ³¹P excitation, paving the way to include larger subjects and more space for receiver arrays. The increase in possible number of RF excitations per scan time, due to the improved B₁⁺ homogeneity and low SAR, allows SNR to be exchanged for spatial resolution in CSI and/or T₁ weighting by simply manipulating T_R and/or flip angle to detect and quantify ratios from different molecular species.     

Non‐uniformly under‐sampled multi‐dimensional spectroscopic imaging in vivo: maximum entropy versus compressed sensing reconstruction

The four‐dimensional (4D) echo‐planar correlated spectroscopic imaging (EP‐COSI) sequence allows for the simultaneous acquisition of two spatial (k_y, k_x) and two spectral (t₂, t₁) dimensions in vivo in a single recording. However, its scan time is directly proportional to the number of increments in the k_y and t₁ dimensions, and a single scan can take 20–40 min using typical parameters, which is too long to be used for a routine clinical protocol. The present work describes efforts to accelerate EP‐COSI data acquisition by application of non‐uniform under‐sampling (NUS) to the k_y–t₁ plane of simulated and in vivo EP‐COSI datasets then reconstructing missing samples using maximum entropy (MaxEnt) and compressed sensing (CS). Both reconstruction problems were solved using the Cambridge algorithm, which offers many workflow improvements over other l₁‐norm solvers. Reconstructions of retrospectively under‐sampled simulated data demonstrate that the MaxEnt and CS reconstructions successfully restore data fidelity at signal‐to‐noise ratios (SNRs) from 4 to 20 and 5× to 1.25× NUS. Retrospectively and prospectively 4× under‐sampled 4D EP‐COSI in vivo datasets show that both reconstruction methods successfully remove NUS artifacts; however, MaxEnt provides reconstructions equal to or better than CS. Our results show that NUS combined with iterative reconstruction can reduce 4D EP‐COSI scan times by 75% to a clinically viable 5 min in vivo, with MaxEnt being the preferred method. Copyright © 2013 John Wiley & Sons, Ltd.     

Accelerated echo planar J‐resolved spectroscopic imaging in prostate cancer: a pilot validation of non‐linear reconstruction using total variation and maximum entropy

The overlap of metabolites is a major limitation in one‐dimensional (1D) spectral‐based single‐voxel MRS and multivoxel‐based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two‐dimensional (2D) J‐resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four‐dimensional (4D) echo planar J‐resolved spectroscopic imaging (EP‐JRESI). The goal of the present work was to validate two different non‐linear reconstruction methods independently using compressed sensing‐based 4D EP‐JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty‐two patients with PCa with a mean age of 63.8 years (range, 46–79 years) were investigated in this study. A 4D non‐uniformly undersampled (NUS) EP‐JRESI sequence was implemented on a Siemens 3‐T MRI scanner. The NUS data were reconstructed using two non‐linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non‐cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo‐inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP‐JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites. Copyright © 2015 John Wiley & Sons, Ltd.     

A model‐based reconstruction for undersampled radial spin‐echo DTI with variational penalties on the diffusion tensor

Radial spin‐echo diffusion imaging allows motion‐robust imaging of tissues with very low T₂ values like articular cartilage with high spatial resolution and signal‐to‐noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin‐echo sequences and the less efficient k‐space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion‐tensor imaging (DTI). A model‐based reconstruction implicitly exploits redundancies in the diffusion‐weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model‐based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin‐echo diffusion‐tensor imaging without degrading parameter quantification and/or SNR. Copyright © 2015 John Wiley & Sons, Ltd.     

磁共振谱成像(MRSI)技术的研究进展

磁共振谱成像 (MRSI)在临床诊断中的作用越来越大。目前 ,最有效的快速谱成像法有回波平面法、螺旋轨迹法和阵列采集法。但是 ,这些 MRSI方法的数据采集时间仍然很长 ,速度有待于进一步提高。抑水抑脂脉冲序列基本定型 ,改进的余地不大。在定量的谱分析方面 ,已经实现分析自动化。谱参数估计方法基本完善 ,但在强基线信号时参数估计方法有待于深入研究。目前 ,磁共振谱的图像重建方法局限在 FFT或网格化后的 FFT,这些方法比较简单、快速 ,但也局限了采样脉冲序列 (采样轨迹 )的大胆设计。期望研究出速度更快的 MRSI数据采集脉冲序列。     

Combining parallel detection of proton echo planar spectroscopic imaging (PEPSI) measurements with a data‐consistency constraint improves SNR

One major challenge of MRSI is the poor signal‐to‐noise ratio (SNR), which can be improved by using a surface coil array. Here we propose to exploit the spatial sensitivity of different channels of a coil array to enforce the k‐space data consistency (DC) in order to suppress noise and consequently to improve MRSI SNR. MRSI data were collected using a proton echo planar spectroscopic imaging (PEPSI) sequence at 3 T using a 32‐channel coil array and were averaged with one, two and eight measurements (avg‐1, avg‐2 and avg‐8). The DC constraint was applied using a regularization parameter λ of 1, 2, 3, 5 or 10. Metabolite concentrations were quantified using LCModel. Our results show that the suppression of noise by applying the DC constraint to PEPSI reconstruction yields up to 32% and 27% SNR gain for avg‐1 and avg‐2 data with λ = 5, respectively. According to the reported Cramer–Rao lower bounds, the improvement in metabolic fitting was significant (p < 0.01) when the DC constraint was applied with λ ≥ 2. Using the DC constraint with λ = 3 or 5 can minimize both root‐mean‐square errors and spatial variation for all subjects using the avg‐8 data set as reference values. Our results suggest that MRSI reconstructed with a DC constraint can save around 70% of scanning time to obtain images and spectra with similar SNRs using λ = 5. Copyright © 2015 John Wiley & Sons, Ltd.     

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

Several different pathologies, including many neurodegenerative disorders, affect the energy metabolism of the brain. Glutamate, a neurotransmitter in the brain, can be used as a biomarker to monitor these metabolic processes. One method that is capable of quantifying glutamate concentration reliably in several regions of the brain is TE‐averaged ¹H spectroscopic imaging. However, this type of method requires the acquisition of multiple TE lines, resulting in long scan durations. The goal of this experiment was to use non‐uniform sampling, compressed sensing reconstruction and an echo planar readout gradient to reduce the scan time by a factor of eight to acquire TE‐averaged spectra in three spatial dimensions. Simulation of glutamate and glutamine showed that the 2.2–2.4 ppm spectral region contained 95% glutamate signal using the TE‐averaged method. Peak integration of this spectral range and home‐developed, prior‐knowledge‐based fitting were used for quantitation. Gray matter brain phantom measurements were acquired on a Siemens 3 T Trio scanner. Non‐uniform sampling was applied retrospectively to these phantom measurements and quantitative results of glutamate with respect to creatine 3.0 (Glu/Cr) ratios showed a coefficient of variance of 16% for peak integration and 9% for peak fitting using eight‐fold acceleration. In vivo scans of the human brain were acquired as well and five different brain regions were quantified using the prior‐knowledge‐based algorithm. Glu/Cr ratios from these regions agreed with previously reported results in the literature. The method described here, called accelerated TE‐averaged echo planar spectroscopic imaging (TEA‐EPSI), is a significant methodological advancement and may be a useful tool for categorizing glutamate changes in pathologies where affected brain regions are not known a priori. Copyright © 2016 John Wiley & Sons, Ltd.     

快速回波平面磁共振谱成像数据重建算法

传统的相位编码磁共振谱成像采集数据需要很长的时间,使得其在临床上的应用受阻。快速回波平面谱成像(Echoplanarspectroscopicimaging,EPSI)技术采用随时间变化的梯度对谱维和空间维同时进行编码,大大减少了数据采集时间。同时,改进EPSI的读出梯度形式还可以提高‘空间-谱’的分辨率。EPSI数据重建算法比较复杂在t方向先分别对奇偶回波数据进行快速傅立叶变换(FastFouriertranslation,FFT),再利用‘偏移’理论进行组合;在kx方向采用网格化算法将不等间隔采集的数据转换到等间隔的直线网格上,再利用FFT进行图像重建;ky方向是相位编码,不需要转换,直接进行FFT即可。     

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

Proton magnetic resonance spectroscopic imaging (¹H 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 ¹H 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 ¹H MRSI data from calf muscles of three healthy volunteers. We reconstructed spectral lineshapes of the ¹H 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 ¹H 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 ¹H 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%). Copyright © 2014 John Wiley & Sons, Ltd.     

Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging

A new fast and accurate tissue typing technique has recently been successfully applied to prostate MR spectroscopic imaging (MRSI) data. This technique is based on canonical correlation analysis (CCA), a statistical method able to simultaneously exploit the spectral and spatial information characterizing the MRSI data. Here, the performance of CCA is further investigated by using brain data obtained by two-dimensional turbo spectroscopic imaging (2DTSI) from patients affected by glioblastoma. The purpose of this study is to investigate the applicability of CCA when typing tissues of heterogeneous tumors. The performance of CCA is also compared with that of ordinary correlation analysis on simulated as well as in vivo data. The results show that CCA outperforms ordinary correlation analysis in terms of robustness and accuracy.     

Improvement of accuracy of diffusion MRI using real‐time self‐gated data acquisition

Diffusion‐weighted and diffusion tensor MR imaging (DWI, DTI) techniques are generally performed with signal averaging of multiple measurements to improve the signal‐to‐noise ratio (SNR) and the accuracy of the diffusion measurement. Any discrepancy in the images between different averages causes errors which reduce the accuracy of the diffusion MRI measurements. In this report, a motion artifact reduction scheme with a real‐time self‐gated (RTSG) data acquisition for diffusion MRI using two‐dimensional echo planar imaging (2D EPI) is described. A subject's translational and rotational motions during application of the diffusion gradients induce an additional phase term and a shift of the echo‐peak position in the k‐space, respectively. These motions also reduce the magnitude of the echo‐peak. Based on these properties, we present a new scheme which monitors the position and the magnitude of the largest echo‐peak in the k‐space. The position and the magnitude of each average is compared to those of early averaging shot to determine if the differences are within or beyond the given threshold values. Motion corrupted data are reacquired in real time. Our preliminary results using RTSG indicate an improvement of both SNR and the accuracy of diffusion MRI measurements. Copyright © 2009 John Wiley & Sons, Ltd.     

Spatio‐spectral regularization to improve magnetic resonance spectroscopic imaging quantification

Magnetic resonance spectroscopic imaging (MRSI) is a non‐invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow‐up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal‐to‐noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio‐spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single‐voxel methods due to their lower concentrations. Copyright © 2016 John Wiley & Sons, Ltd.     

Frequency correction method for improved spatial correlation of hyperpolarized 13C metabolites and anatomy

Blip‐reversed echo‐planar imaging (EPI) is investigated as a method for measuring and correcting the spatial shifts that occur due to bulk frequency offsets in ¹³C metabolic imaging in vivo. By reversing the k‐space trajectory for every other time point, the direction of the spatial shift for a given frequency is reversed. Here, mutual information is used to find the ‘best’ alignment between images and thereby measure the frequency offset. Time‐resolved 3D images of pyruvate/lactate/urea were acquired with 5 s temporal resolution over a 1 min duration in rats (N = 6). For each rat, a second injection was performed with the demodulation frequency purposely mis‐set by +35 Hz, to test the correction for erroneous shifts in the images. Overall, the shift induced by the 35 Hz frequency offset was 5.9 ± 0.6 mm (mean ± standard deviation). This agrees well with the expected 5.7 mm shift based on the 2.02 ms delay between k‐space lines (giving 30.9 Hz per pixel). The 0.6 mm standard deviation in the correction corresponds to a frequency‐detection accuracy of 4 Hz. A method was presented for ensuring the spatial registration between ¹³C metabolic images and conventional anatomical images when long echo‐planar readouts are used. The frequency correction method was shown to have an accuracy of 4 Hz. Summing the spatially corrected frames gave a signal‐to‐noise ratio (SNR) improvement factor of 2 or greater, compared with the highest single frame. Copyright © 2013 John Wiley & Sons, Ltd.     

Whole-brain high-resolution metabolite mapping with 3D compressed-sensing SENSE low-rank 1H FID-MRSI

There is a growing interest in the neuroscience community to map the distribution of brain metabolites in vivo. Magnetic resonance spectroscopic imaging (MRSI) is often limited by either a poor spatial resolution and/or a long acquisition time, which severely restricts its applications for clinical and research purposes. Building on a recently developed technique of acquisition-reconstruction for 2D MRSI, we combined a fast Cartesian ¹H-FID-MRSI acquisition sequence, compressed-sensing acceleration, and low-rank total-generalized-variation constrained reconstruction to produce 3D high-resolution whole-brain MRSI with a significant acquisition time reduction. We first evaluated the acceleration performance using retrospective undersampling of a fully sampled dataset. Second, a 20 min accelerated MRSI acquisition was performed on three healthy volunteers, resulting in metabolite maps with 5 mm isotropic resolution. The metabolite maps exhibited the detailed neurochemical composition of all brain regions and revealed parts of the underlying brain anatomy. The latter assessment used previous reported knowledge and a atlas-based analysis to show consistency of the concentration contrasts and ratio across all brain regions. These results acquired on a clinical 3 T MRI scanner successfully combined 3D ¹H-FID-MRSI with a constrained reconstruction to produce detailed mapping of metabolite concentrations at high resolution over the whole brain, with an acquisition time suitable for clinical or research settings.     

Snapshot‐CEST: Optimizing spiral‐centric‐reordered gradient echo acquisition for fast and robust 3D CEST MRI at 9.4 T

Gradient echo (GRE)‐based acquisition provides a robust readout method for chemical exchange saturation transfer (CEST) at ultrahigh field (UHF). To develop a snapshot‐CEST approach, the transient GRE signal and point spread function were investigated in detail, leading to optimized measurement parameters and reordering schemes for fast and robust volumetric CEST imaging. Simulation of the transient GRE signal was used to determine the optimal sequence parameters and the maximum feasible number of k‐space lines. Point spread function analysis provided an insight into the induced k‐space filtering and the performance of different rectangular reordering schemes in terms of blurring, signal‐to‐noise ratio (SNR) and relaxation dependence. Simulation results were confirmed in magnetic resonance imaging (MRI) measurements of healthy subjects. Minimal repetition time (TR) is beneficial for snapshot‐GRE readout. At 9.4 T, for TR = 4 ms and optimal flip angle close to the Ernst angle, a maximum of 562 k‐space lines can be acquired after a single presaturation, providing decent SNR with high image quality. For spiral‐centric reordered k‐space acquisition, the image quality can be further improved using a rectangular spiral reordering scheme adjusted to the field of view. Application of the derived snapshot‐CEST sequence for fast imaging acquisition in the human brain at 9.4 T shows excellent image quality in amide and nuclear Overhauser enhancement (NOE), and enables guanidyl CEST detection. The proposed snapshot‐CEST establishes a fast and robust volumetric CEST approach ready for the imaging of known and novel exchange‐weighted contrasts at UHF.     

Slice‐selective FID acquisition,localized by outer volume suppression (FIDLOVS) for 1H‐MRSI of the human brain at 7 T with minimal signal loss

In comparison to 1.5 and 3 T, MR spectroscopic imaging at 7 T benefits from signal‐to‐noise ratio (SNR) gain and increased spectral resolution and should enable mapping of a large number of metabolites at high spatial resolutions. However, to take full advantage of the ultra‐high field strength, severe technical challenges, e.g. related to very short T₂ relaxation times and strict limitations on the maximum achievable B₁ field strength, have to be resolved. The latter results in a considerable decrease in bandwidth for conventional amplitude modulated radio frequency pulses (RF‐pulses) and thus to an undesirably large chemical‐shift displacement artefact. Frequency‐modulated RF‐pulses can overcome this problem; but to achieve a sufficient bandwidth, long pulse durations are required that lead to undesirably long echo‐times in the presence of short T₂ relaxation times. In this work, a new magnetic resonance spectroscopic imaging (MRSI) localization scheme (free induction decay acquisition localized by outer volume suppression, FIDLOVS) is introduced that enables MRSI data acquisition with minimal SNR loss due to T₂ relaxation and thus for the first time mapping of an extended neurochemical profile in the human brain at 7 T. To overcome the contradictory problems of short T₂ relaxation times and long pulse durations, the free induction decay (FID) is directly acquired after slice‐selective excitation. Localization in the second and third dimension and skull lipid suppression are based on a T₁‐ and B₁‐insensitive outer volume suppression (OVS) sequence. Broadband frequency‐modulated excitation and saturation pulses enable a minimization of the chemical‐shift displacement artefact in the presence of strict limits on the maximum B₁ field strength. The variable power RF pulses with optimized relaxation delays (VAPOR) water suppression scheme, which is interleaved with OVS pulses, eliminates modulation side bands and strong baseline distortions. Third order shimming is based on the accelerated projection‐based automatic shimming routine (FASTERMAP) algorithm. The striking SNR and spectral resolution enable unambiguous quantification and mapping of 12 metabolites including glutamate (Glu), glutamine (Gln), N‐acetyl‐aspartatyl‐glutamate (NAAG), γ‐aminobutyric acid (GABA) and glutathione (GSH). The high SNR is also the basis for highly spatially resolved metabolite mapping. Copyright © 2009 John Wiley & Sons, Ltd.