Noise-reduction techniques for 1H-FID-MRSI at 14.1 T: Monte Carlo validation and in vivo application.

Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites.

Diffusion of brain metabolites highlights altered brain microstructure in type C hepatic encephalopathy: a 9.4 T preliminary study.

Introduction: Type C hepatic encephalopathy (HE) is a decompensating event of chronic liver disease leading to severe motor and cognitive impairment. The progression of type C HE is associated with changes in brain metabolite concentrations measured by 1H magnetic resonance spectroscopy (MRS), most noticeably a strong increase in glutamine to detoxify brain ammonia. In addition, alterations of brain cellular architecture have been measured ex vivo by histology in a rat model of type C HE. The aim of this study was to assess the potential of diffusion-weighted MRS (dMRS) for probing these cellular shape alterations in vivo by monitoring the diffusion properties of the major brain metabolites. Methods: The bile duct-ligated (BDL) rat model of type C HE was used. Five animals were scanned before surgery and 6- to 7-week post-BDL surgery, with each animal being used as its own control. 1H-MRS was performed in the hippocampus (SPECIAL, TE = 2.8 ms) and dMRS in a voxel encompassing the entire brain (DW-STEAM, TE = 15 ms, diffusion time = 120 ms, maximum b-value = 25 ms/µm2) on a 9.4 T scanner. The in vivo MRS acquisitions were further validated with histological measures (immunohistochemistry, Golgi-Cox, electron microscopy). Results: The characteristic 1H-MRS pattern of type C HE, i.e., a gradual increase of brain glutamine and a decrease of the main organic osmolytes, was observed in the hippocampus of BDL rats. Overall increased metabolite diffusivities (apparent diffusion coefficient and intra-stick diffusivity-Callaghan's model, significant for glutamine, myo-inositol, and taurine) and decreased kurtosis coefficients were observed in BDL rats compared to control, highlighting the presence of osmotic stress and possibly of astrocytic and neuronal alterations. These results were consistent with the microstructure depicted by histology and represented by a decline in dendritic spines density in neurons, a shortening and decreased number of astrocytic processes, and extracellular edema. Discussion: dMRS enables non-invasive and longitudinal monitoring of the diffusion behavior of brain metabolites, reflecting in the present study the globally altered brain microstructure in BDL rats, as confirmed ex vivo by histology. These findings give new insights into metabolic and microstructural abnormalities associated with high brain glutamine and its consequences in type C HE.

Lessons on brain edema in HE: from cellular to animal models and clinical studies.

Brain edema is considered as a common feature associated with hepatic encephalopathy (HE). However, its central role as cause or consequence of HE and its implication in the development of the neurological alterations linked to HE are still under debate. It is now well accepted that type A and type C HE are biologically and clinically different, leading to different manifestations of brain edema. As a result, the findings on brain edema/swelling in type C HE are variable and sometimes controversial. In the light of the changing natural history of liver disease, better description of the clinical trajectory of cirrhosis and understanding of molecular mechanisms of HE, and the role of brain edema as a central component in the pathogenesis of HE is revisited in the current review. Furthermore, this review highlights the main techniques to measure brain edema and their advantages/disadvantages together with an in-depth description of the main ex-vivo/in-vivo findings using cell cultures, animal models and humans with HE. These findings are instrumental in elucidating the role of brain edema in HE and also in designing new multimodal studies by performing in-vivo combined with ex-vivo experiments for a better characterization of brain edema longitudinally and of its role in HE, especially in type C HE where water content changes are small.

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling.

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

MP-PCA denoising for diffusion MRS data: promises and pitfalls.

Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.

PET CMRglc mapping and 1H-MRS show altered glucose uptake and neurometabolic profiles in BDL rats.

Type C hepatic encephalopathy (HE) is a complex neuropsychiatric disorder occurring as a consequence of chronic liver disease. Alterations in energy metabolism have been suggested in type C HE, but in vivo studies on this matter remain sparse and have reported conflicting results. Here, we propose a novel preclinical 18F-FDG PET methodology to compute quantitative 3D maps of the regional cerebral metabolic rate of glucose (CMRglc) from a labelling steady-state PET image of the brain and an image-derived input function. This quantitative approach shows its strength when comparing groups of animals with divergent physiology, such as HE animals. PET CMRglc maps were registered to an atlas and the mean CMRglc from the hippocampus and the cerebellum were associated to the corresponding localized 1H MR spectroscopy acquisitions. This study provides for the first time local and quantitative information on both brain glucose uptake and neurometabolic profile alterations in a rat model of type C HE. A 2-fold lower brain glucose uptake, concomitant with an increase in brain glutamine and a decrease in the main osmolytes, was observed in the hippocampus and in the cerebellum. These novel findings are an important step towards new insights into energy metabolism in the pathophysiology of HE.

Lean regional muscle volume estimates using explanatory bioelectrical models in healthy subjects and patients with muscle wasting.

BACKGROUND: The availability of non-invasive, accessible, and reliable methods for estimating regional skeletal muscle volume is paramount in conditions involving primary and/or secondary muscle wasting. This work aimed at (i) optimizing serial bioelectrical impedance analysis (SBIA ) by computing a conductivity constant based on quantitative magnetic resonance imaging (MRI) data and (ii) investigating the potential of SBIA for estimating lean regional thigh muscle volume in patients with severe muscle disorders. METHODS: Twenty healthy participants with variable body mass index and 20 patients with idiopathic inflammatory myopathies underwent quantitative MRI. Anatomical images and fat fraction maps were acquired in thighs. After manual muscle segmentation, lean thigh muscle volume (lVMRI ) was computed. Subsequently, multifrequency (50 to 350 kHz) serial resistance profiles were acquired between current skin electrodes (i.e. ankle and hand) and voltage electrodes placed on the anterior thigh. In vivo values of the muscle electrical conductivity constant were computed using data from SBIA and MRI gathered in the right thigh of 10 healthy participants. Lean muscle volume (lVBIA ) was derived from SBIA measurements using this newly computed constant. Between-day reproducibility of lVBIA was studied in six healthy participants. RESULTS: Electrical conductivity constant values ranged from 0.82 S/m at 50 kHz to 1.16 S/m at 350 kHz. The absolute percentage difference between lVBIA and lVMRI was greater at frequencies >270 kHz (P < 0.0001). The standard error of measurement and the intra-class correlation coefficient for lVBIA computed from measurements performed at 155 kHz (i.e. frequency with minimal difference) against lVMRI were 6.1% and 0.95 in healthy participants and 9.4% and 0.93 in patients, respectively. Between-day reproducibility of lVBIA was as follows: standard error of measurement = 4.6% (95% confidence interval [3.2, 7.8] %), intra-class correlation coefficient = 0.98 (95% confidence interval [0.95, 0.99]). CONCLUSIONS: These findings demonstrate a strong agreement of lean muscle volume estimated using SBIA against quantitative MRI in humans, including in patients with severe muscle wasting and fatty degeneration. SBIA shows promises for non-invasive, fast, and accessible estimation and follow-up of lean regional skeletal muscle volume for transversal and longitudinal studies.