Single Voxel Research Articles

CHARACTERISATION OF THE RESPONSE OF PRE-CLINICAL MODELS OF GLIOMA TO METABOLIC THERAPIES USING IN VIVO MAGNETIC RESONANCE SPECTROSCOPY

Abstract AIMS Magnetic resonance imaging (MRI) is routinely used to diagnose and monitor brain tumours. Magnetic resonance spectroscopy (MRS) allows us to record the metabolic profile of tumours in situ. This provides a non-invasive method to monitor the response of tumours to metabolic therapies. We aimed to characterise the response of pre-clinical models of glioma to metabolic therapies such as arginine deprivation and the ketogenic diet (KD) by MRS, to better understand what metabolic changes are occurring in the tumour. METHOD We performed orthotopic injection of three different cell lines, including the syngeneic models GL261 and CT2A, as well as a primary GBM cell line, into the striatum of C57BL/6 and athymic nude mice respectively. Mice were randomised to either arginine deprivation treatment using pegylated arginine deiminase (ADI), or KD, plus controls. In each animal, we collected single voxel MRS data using STEAM on both the tumour itself, and healthy brain. MRS data was exported in jMRUI format and analysed in R using the spant package, followed by PCA and OPLS-DA. RESULTS PCA showed that there is clear distinction between healthy and tumour spectra across all models. OPLS-DA indicated that the lipid and choline spectral regions were significantly associated with tumour, whereas the NAA and glutamine/glutamate regions were significantly associated with healthy brain. Integration of specific peaks associated with these metabolites confirmed these results. Subset analysis indicated distinct metabolic signatures between the syngeneic models and the primary GBM model. Furthermore, there is evidence to suggest that tumours in animals treated with arginine deprivation have a unique metabolic signature. CONCLUSION This data indicates that the tumour specific response to metabolic therapies can be measured by routine MRS screening. This would allow a non-invasive method by which clinicians could monitor the effectiveness of metabolic therapies in patients with gliomas.

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

The relationship between visual acuity loss and GABAergic inhibition in amblyopia

Abstract Early childhood experience alters visual development, a process exemplified by amblyopia, a common neurodevelopmental condition resulting in cortically reduced vision in one eye. Visual deficits in amblyopia may be a consequence of abnormal suppressive interactions in the primary visual cortex by inhibitory neurotransmitter γ-aminobutyric acid (GABA). We examined the relationship between visual acuity loss and GABA+ in adult human participants with amblyopia. Single-voxel proton magnetic resonance spectroscopy (MRS) data were collected from the early visual cortex (EVC) and posterior cingulate cortex (control region) of 28 male and female adults with current or past amblyopia while they viewed flashing checkerboards monocularly, binocularly, or while they had their eyes closed. First, we compared GABA+ concentrations between conditions to evaluate suppressive binocular interactions. Then, we correlated the degree of visual acuity loss with GABA+ levels to test whether GABAergic inhibition could explain visual acuity deficits. Visual cortex GABA+ was not modulated by viewing condition, and we found weak evidence for a negative correlation between visual acuity deficits and GABA+. These findings suggest that reduced vision in one eye due to amblyopia is not strongly linked to GABAergic inhibition in the visual cortex. We advanced our understanding of early experience dependent plasticity in the human brain by testing the association between visual acuity deficits and visual cortex GABA in amblyopes of the most common subtypes. Our study shows that the relationship was not as clear as expected and provides avenues for future investigation.

Generalizing feature preservation in iso-surface extraction from triple dexel models

We present a method to resolve visual artifacts of a state-of-the-art iso-surface extraction algorithm by generating feature-preserving surface patches for isolated arbitrarily complex, single voxels without the need for further adaptive subdivision. In the literature, iso-surface extraction from a 3D voxel grid is limited to a single sharp feature per minimal unit, even for algorithms such as Cubical Marching Squares that produce feature-preserving surface reconstructions. In practice though, multiple sharp features can meet in a single voxel. This is reflected in the triple dexel model, which is used in simulation of CNC manufacturing processes. Our approach generalizes the use of normal information to perfectly preserve multiple sharp features for a single voxel, thus avoiding visual artifacts caused by state-of-the-art procedures.

Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models.

To develop a new method to generate synthetic MR spectroscopic imaging (MRSI) data for training machine learning models. This study targeted routine MRI examination protocols with single voxel spectroscopy (SVS). A novel model derived from pix2pix generative adversarial networks was proposed to generate synthetic MRSI data using MRI and SVS data as inputs. T1- and T2-weighted, SVS, and reference MRSI data were acquired from healthy brains with clinically available sequences. The proposed model was trained to generate synthetic MRSI data. Quantitative evaluation involved the calculation of the mean squared error (MSE) against the reference and metabolite ratio value. The effect of the location of and the number of the SVS data on the quality of the synthetic MRSI data was investigated using the MSE. The synthetic MRSI data generated from the proposed model were visually closer to the reference. The 95% confidence interval (CI) of the metabolite ratio value of synthetic MRSI data overlapped with the reference for seven of eight metabolite ratios. The MSEs tended to be lower in the same location than in different locations. The MSEs among groups of numbers of SVS data were not significantly different. A new method was developed to generate MRSI data by integrating MRI and SVS data. Our method can potentially increase the volume of MRSI data training for other machine learning models by adding SVS acquisition to routine MRI examinations.

Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

To develop a practical method to enable 3D T1 mapping of brain metabolites. Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

R2* Impact on Hepatic Fat Quantification With a Commercial Single Voxel Technique at 1.5 and 3.0 T.

Rationale and Objectives: Fat quantification accuracy using a commercial single-voxel high speed T2-corrected multi-echo (HISTO) technique and its robustness to R2* variations at 3.0 T, such as those introduced by iron in liver, has not been fully established. This study evaluated HISTO at 3.0 T and sought to reproduce results at 1.5 T. Methods: Phantoms were prepared with a range of fat content and R2*. Data were acquired at 1.5 T and 3.0 T, using HISTO and a Dixon technique. Fat quantification accuracy was evaluated as a function of R2*. The patient study included 239 consecutive patients. Data were acquired at 1.5 T or 3.0 T, using HISTO and Dixon techniques. The techniques were compared using Bland-Altman plots. Bias significance was evaluated using a one-sample t-test. Results: In phantoms, HISTO was accurate within 10% up to a R2* of 100 s-1 at both field strengths, while Dixon was accurate within 10% where R2* was accurately quantified (up to 350 s-1 at 1.5 T, and 550 s-1 at 3.0 T). In patients, where R2* was <100 s-1, fat quantification from both techniques agreed at 1.5 T (P = .71), but not at 3.0 T (P = .007), with a bias <1%. Conclusion: Results suggest that HISTO is reliable when R2* is <100 s-1, corresponding to patients with at most mild liver iron overload, and that it should be used with caution when R2* is >100 s-1. Dixon should be preferred for hepatic fat quantification due to its robustness to R2* variations.

Investigation of Creatine Levels in Glial Tumors Using MR Spectroscopy

Purpose: The present study examines the efficacy of Creatine (Cr) levels in the determination of tumor grade on single-voxel (SV) and multi-voxel (MV) magnetic resonance spectroscopy (MRS) in patients with glial tumors. Material and Methods: A retrospective review was made of 20 SV and 20 MV MRS PDF images of patients with a pathological diagnosis of glial tumor. Cr values and metabolite ratios were measured in the tumor and compared with those in the healthy symmetrical brain parenchyma of the contralateral hemisphere. Results: A significant difference was noted in the (Cho+Cr)/NAA and Cho/NAA ratios on SV MRS between the high-grade tumors and the healthy contralateral hemisphere. On MV MRS, Cho/NAA ratios within the minimum and maximum Cr(h) voxels (Cho/NAAmin-Cr(h), Cho/NAAmax-Cr(h), respectively) were higher in high-grade tumors compared to healthy tissue. ROC analysis showed that the max-Cr(h) metabolite on MV MRS was successful in distinguishing low-grade tumors from high-grade tumors. Conclusion: Using the minimum and maximum values of Cr as a reference can improve the overall diagnostic accuracy in the diagnosis and grading of glial tumors. Our findings showed that Cr tended to be low in high-grade tumors and further that the max-Cr(h) metabolite may help in the differentiation of glial tumors on MV MRS.

Imbalance of thalamic metabolites in an experimental model of hypertension: role of bergamot polyphenols

Abstract Funding Acknowledgements None. Hypertension is emerging as a pathogenic factor in vascular-based cognitive impairment, altering the structure and function of cerebral vasculature, and representing the main risk factor for cerebral small vessel diseases and neuronal loss. Some studies correlated a reduction in regional cerebral blood flow in the thalamus of elderly hypertensive brains with the decrease in certain cerebral metabolites, because of hypoperfusion. Patients with severe hypertension show a reduced oxygen consumption in regions with reduced cerebral blood flow suggesting that, the vascular dysfunction and the reduced cerebral blood flow could be related to reduced metabolic demands due to brain dysfunction and damage. Chronic cerebral hypoperfusion is associated with increased inflammatory state and oxidative stress. Herein, we hypothesized to prevent or reduce the onset of oxidative stress using natural occurring antioxidant compounds could be of particular importance for the management of hypertension-induced cerebral complications. Hypertension was induced on Wistar male rats by left renal artery ligation (RAL). After complete recovery from surgery, the animals were treated with 20 mg/kg deoxycorticosterone acetate twice a week for 4 weeks, and 1% sodium chloride (NaCl) water. A subgroup of hypertensive rats received Bergamot Polyphenolic Fraction (BPF) by gavage. A control group was treated with sub-cutaneous injection of saline. At baseline (T0), after 2 weeks (T1) and at the end of the experimental period (T2), the thalamic neurochemical profile were evaluated using proton magnetic resonance spectroscopy (1H-MRS), performed with a Bruker Pharmascan 70/16 7T. VAPOR water-suppression module was used and optimized to reduce residual water, then thalamic spectra were acquired using a short echo time single voxel point-resolved spectroscopy technique (voxel size 3x3x3 mm). The absolute quantification of metabolite concentrations has been performed using TARQUIN (v.4.3.10). A reduction tending to significance of the tNAA/tCr ratio, emerged in the thalamus of RAL DOCA-Salt rats compared to control rats at T2. A significant reduction of the tNAA/tCr ratio was observed in the group of RAL DOCA-Salt rats at the T2 compared to the same animals at the T1. In addition, a significantly decrease of the tCho/tCr ratio was highlighted in the RAL DOCA-Salt animals compared to the CTRL rats starting from the T1. The administration of BPF ameliorates the tNAA/tCr ratios in the thalamus of RAL DOCA-Salt rats. Such evidence of the influence of hypertension on tNAA/tCr and tCho/tCr ratios could better explain the relationship between brain structure and modulation of some metabolites, providing new insights into hypertension-induced brain complications and the contribution that the use of natural compounds could give to their management.

Sequence building block for magnetic resonance spectroscopy on Siemens VE-series scanners.

We present a sequence building block (SBB) that embeds magnetic resonance spectroscopy (MRS) into another sequence on the Siemens VE platform without any custom hardware. This enables dynamic studies such as functional MRS (fMRS), dynamic shimming and frequency correction, and acquisition of navigator images for motion correction. The SBB supports nonlocalised spectroscopy (free induction decay), STimulated Echo Acquisition Mode single voxel spectroscopy, and 1D, 2D and 3D phase-encoded chemical shift imaging. It can embed 1H or X-nuclear MRS into a 1H sequence; and 1H-MRS into an X-nuclear sequence. We demonstrate integration into the vendor's gradient-recalled echo sequence. We acquire test data in phantoms with three coils (31P/1H, 13C/1H and 2H/1H) and in two volunteers on a 7-T Terra MRI scanner. Fifteen lines of code are required to insert the SBB into a sequence. Spectra and images are acquired successfully in all cases in phantoms, and in human abdomen and calf muscle. Phantom comparison of signal-to-noise ratio and linewidth showed that the SBB has negligible effects on image and spectral quality, except that it sometimes produces a nuclear Overhauser effect (NOE) signal enhancement for multinuclear applications in line with conventional 1H NOE pulses. Our new SBB embeds MRS into a host imaging or spectroscopy sequence in 15 lines of code. It allows homonuclear and heteronuclear interleaving. The package is available through the standard C2P procedure. We hope this will lower the barrier for entry to studies applying dynamic fMRS and for online motion correction and B0-shim updating.

Voxel Design of Grayscale DLP 3D-Printed Soft Robots.

Grayscale digital light processing (DLP) printing is a simple yet effective way to realize the variation of material properties by tuning the grayscale value. However, there is a lack of available design methods for grayscale DLP 3D-printed structures due to the complexities arising from the voxel-level grayscale distribution, nonlinear material properties, and intricate structures. Inspired by the dexterous motions of natural organisms, a design and fabrication framework for grayscale DLP-printed soft robots is developed by combining a grayscale-dependent hyperelastic constitutive model and a voxel-based finite-element model. The constitutive model establishes the relationship between the projected grayscale value and the nonlinear mechanical properties, while the voxel-based finite-element model enables fast and efficient calculation of the mechanical performances with arbitrarily distributed material properties. A multiphysics modeling and experimental method is developed to validate the homogenization assumption of the degree of conversion(DoC) variation in a single voxel. The design framework is used to design structures with reduced stress concentration and programmable multimodal motions. This work paves the way for integrated design and fabrication of functional structures using grayscale DLP 3Dprinting.

Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracers

We propose a path-guiding algorithm to be incorporated into the wavefront style of path tracers (WFPTs). As WFPTs are primarily implemented on graphics processing units (GPUs), the proposed method aims to leverage the capabilities of the GPUs and reduce the hierarchical data structure and memory usage typically required for such techniques. To achieve this, our algorithm only stores the radiant exitance on a single global sparse voxel octree (SVO) data structure. Probability density functions required to guide the rays are generated on-the-fly using this data structure. The proposed approach reduces the scene-related persistent memory requirements compared to other path-guiding techniques while producing similar or better results depending on scene characteristics. To our knowledge, our algorithm is the first one that incorporates path guiding into a WFPT.

Differentiation of hemispheric white matter lesions in migraine and multiple sclerosis with similar radiological features using advanced MRI.

White matter hyperintensities (WMHs), presented on T2-weighted or fluid-attenuated inversion recovery magnetic resonance imaging (MRI) sequences, are lesions in the human brain that can be observed in both migraine and multiple sclerosis (MS). Seventeen migraine patients and 15 patients with relapsing-remitting multiple sclerosis with WMHs, and 17 healthy subjects age-and sex-matched to the migraine group were prospectively enrolled and underwent conventional and advanced MRI studies with diffusion-and perfusion-weighted imaging and single voxel proton magnetic resonance spectroscopy. In both disease groups, elevated T2 relaxation time, apparent diffusion coefficient (ADC) values, and decreased N-acetyl-aspartate levels were found in the intralesional white matter compared to the contralateral normal-appearing white matter (NAWM), while there was no difference between the hemispheres of the control subjects. Migraine patients had the lowest intralesional creatine + phosphocreatine and myo-inositol (mI) values among the three groups, while patients with MS showed the highest intralesional T1 and T2 relaxation times, ADC, and mI values. In the contralateral NAWM, the same trend with mI changes was observed in migraineurs and MS patients. No differences in perfusion variables were observed in any groups. Our multimodal study showed that tissue damage is detectable in both diseases. Despite the differences in various advanced MRI measures, with more severe injury detected in MS lesions, we could not clearly differentiate the two white matter lesion types.

Blood brain barrier disruption and glutamatergic excitotoxicity in post-acute sequelae of SARS COV-2 infection cognitive impairment: potential biomarkers and a window into pathogenesis.

To investigate the association between blood-brain barrier permeability, brain metabolites, microstructural integrity of the white matter, and cognitive impairment (CI) in post-acute sequelae of SARS-COV-2 infection (PASC). In this multimodal longitudinal MRI study 14 PASC participants with CI and 10 healthy controls were enrolled. All completed investigations at 3 months following acute infection (3 months ± 2 weeks SD), and 10 PASC participants completed at 12 months ± 2.22 SD weeks. The assessments included a standard neurological assessment, a cognitive screen using the brief CogState battery and multi-modal MRI derived metrics from Dynamic contrast enhanced (DCE) perfusion Imaging, Diffusion Tensor Imaging (DTI), and single voxel proton Magnetic Resonance Spectroscopy. These measures were compared between patients and controls and correlated with cognitive scores. At baseline, and relative to controls, PASC participants had higher K-Trans and Myo-inositol, and lower levels of Glutamate/Glutamine in the frontal white matter (FWM) (p < 0.01) as well as in brain stem (p < 0.05), and higher FA and lower MD in the FWM (p < 0.05). In PASC participants, FA and MD decreased in the FWM at 12 months compared to baseline (p < 0.05). K-Trans and metabolite concentrations did not change significantly over time. Neurocognitive scores did not correlation with the increased permeability (K trans). PASC with CI is associated with BBB impairment, loss of WM integrity, and inflammation at 3 months which significantly but not uniformly improved at 12 months. The loss of WM integrity is possibly mediated by BBB impairment and associated glutamatergic excitotoxicity.

Programmable Shape-Shifting Soft Robotic Structure Using Liquid Metal Electromagnetic Actuators.

Constant development of soft robots, stretchable electronics, or flexible medical devices forces the research to look for new flexible structures that can change their shapes under external physical stimuli. This study presents a soft robotic structure that can change its shape into different three-dimensional (3D) configurations in response to electric current flown through the embedded liquid-metal conductors enabling electromagnetic actuation. The proposed structure is composed of volumetric pixels (voxels) connected in series where each can be independently controlled by the inputs of electrical current and vacuum pressure. A single voxel is made up of a granular core (GC) with an outer shell made of silicone rubber. The shell has embedded channels filled with liquid metal. The structure changes its shape under the Lorentz force produced by the liquid metal channel under applied electrical current. The GC allows the structure to maintain its shape after deformation even when the current is shut off. This is possible due to the granular jamming effect. In this study, we show the concept, the results of multiphysics simulation, and experimental characterization, including among other techniques, such as 3D digital image correlation or 3D magnetic field scanning, to study the different properties of the structure. We prove that the proposed structure can morph into many different shapes with the amplitude higher than 10 mm, and this process can be both fully reversible and repeatable.

Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis.

This paper explores different machine learning (ML) algorithms for analyzing diffusion nuclear magnetic resonance imaging (dMRI) models when analytical fitting shows restrictions. It reviews various ML techniques for dMRI analysis and evaluates their performance on different b-values range datasets, comparing them with analytical methods. After standard fitting for reference, four sets of diffusion-weighted nuclear magnetic resonance images were used to train/test various ML algorithms for prediction of diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and kurtosis (K). ML classification algorithms, including extra-tree classifier (ETC), logistic regression, C-support vector, extra-gradient boost, and multilayer perceptron (MLP), were used to determine the existence of diffusion parameters (D, D*, f, and K) within single voxels. Regression algorithms, including linear regression, polynomial regression, ridge, lasso, random forest (RF), elastic-net, and support-vector machines, were used to estimate the value of the diffusion parameters. Performance was evaluated using accuracy (ACC), area under the curve (AUC) tests, and cross-validation root mean square error (RMSECV). Computational timing was also assessed. ETC and MLP were the best classifiers, with 94.1% and 91.7%, respectively, for the ACC test and 98.7% and 96.3% for the AUC test. For parameter estimation, RF algorithm yielded the most accurate results The RMSECV percentages were: 8.39% for D, 3.57% for D*, 4.52% for f, and 3.53% for K. After the training phase, the ML methods demonstrated a substantial decrease in computational time, being approximately 232 times faster than the conventional methods. The findings suggest that ML algorithms can enhance the efficiency of dMRI model analysis and offer new perspectives on the microstructural and functional organization of biological tissues. This paper also discusses the limitations and future directions of ML-based dMRI analysis.

Evaluation of Whole Brain Intravoxel Incoherent Motion (IVIM) Imaging.

Intravoxel Incoherent Motion (IVIM) imaging provides non-invasive perfusion measurements, eliminating the need for contrast agents. This work explores the feasibility of IVIM imaging in whole brain perfusion studies, where an isotropic 1 mm voxel is widely accepted as a standard. This study follows the validity of a time-limited, precise, segmentation-ready whole-brain IVIM protocol suitable for clinical reality. To assess the influence of SNR on the estimation of S0, f, D*, and D IVIM parameters, a series of measurements and simulations were performed in MATLAB for the following three estimation techniques: segmented grid search, segmented curve fitting, and one-step curve fitting, utilizing known "ground truth" and noised data. Scanner-specific SNR was estimated based on a healthy subject IVIM MRI study in a 3T scanner. Measurements were conducted for 25.6 × 25.6 × 14.4 cm FOV with a 256 × 256 in-plane resolution and 72 slices, resulting in 1 × 1 × 2 mm voxel size. Simulations were performed for 36 SNR levels around the measured SNR value. For a single voxel grid, the search algorithm mean relative error Ŝ0, f^, D^*, and D^ of at the expected SNR level were 5.00%, 81.91%, 76.31%, and 18.34%, respectively. Analysis has shown that high-resolution IVIM imaging is possible, although there is significant variation in both accuracy and precision, depending on SNR and the chosen estimation method.

Estimating the synaptic density deficit in Alzheimer's disease using multi-contrast CEST imaging.

In vivo noninvasive imaging of neurometabolites is crucial to improve our understanding of the underlying pathophysiological mechanism in neurodegenerative diseases. Abnormal changes in synaptic organization leading to synaptic degradation and neuronal loss is considered as one of the primary factors driving Alzheimer's disease pathology. Magnetic resonance based molecular imaging techniques such as chemical exchange saturation transfer (CEST) and magnetic resonance spectroscopy (MRS) can provide neurometabolite specific information which may relate to underlying pathological and compensatory mechanisms. In this study, CEST and short echo time single voxel MRS was performed to evaluate the sensitivity of cerebral metabolites to beta-amyloid (Aβ) induced synaptic deficit in the hippocampus of a mouse model of Alzheimer's disease. The CEST based spectra (Z-spectra) were acquired on a 9.4 Tesla small animal MR imaging system with two radiofrequency (RF) saturation amplitudes (1.47 μT and 5.9 μT) to obtain creatine-weighted and glutamate-weighted CEST contrasts, respectively. Multi-pool Lorentzian fitting and quantitative T1 longitudinal relaxation maps were used to obtain metabolic specific apparent exchange-dependent relaxation (AREX) maps. Short echo time (TE = 12 ms) single voxel MRS was acquired to quantify multiple neurometabolites from the right hippocampus region. AREX contrasts and MRS based metabolite concentration levels were examined in the ARTE10 animal model for Alzheimer's disease and their wild type (WT) littermate counterparts (age = 10 months). Using MRS voxel as a region of interest, group-wise analysis showed significant reduction in Glu-AREX and Cr-AREX in ARTE10, compared to WT animals. The MRS based results in the ARTE10 mice showed significant decrease in glutamate (Glu) and glutamate-total creatine (Glu/tCr) ratio, compared to WT animals. The MRS results also showed significant increase in total creatine (tCr), phosphocreatine (PCr) and glutathione (GSH) concentration levels in ARTE10, compared to WT animals. In the same ROI, Glu-AREX and Cr-AREX demonstrated positive associations with Glu/tCr ratio. These results indicate the involvement of neurotransmitter metabolites and energy metabolism in Aβ-mediated synaptic degradation in the hippocampus region. The study also highlights the feasibility of CEST and MRS to identify and track multiple competing and compensatory mechanisms involved in heterogeneous pathophysiology of Alzheimer's disease in vivo.

Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

The microstructure is a critical factor governing the functionality of ceramic materials. Meanwhile, microstructural analysis of electron microscopy images of polycrystalline ceramics, which are geometrically complex and composed of countless crystal grains with porosity and secondary phases, has generally been performed manually by human experts. Objective pixel-based analysis (semantic segmentation) with high accuracy is a simple but critical step for quantifying microstructures. In this study, we apply neural network-based semantic segmentation to secondary electron images of polycrystalline ceramics obtained by three-dimensional (3D) imaging. The deep-learning-based models (e.g., fully convolutional network and U-Net) by employing a dataset based on a 3D scanning electron microscopy with a focused ion beam is found to be able to recognize defect structures characteristic of polycrystalline materials in some cases due to artifacts in electron microscopy imaging. Owing to the training images with improved depth accuracy, the accuracy evaluation function, intersection over union (IoU) values, reaches 94.6% for U-Net. These IoU values are among the highest for complex ceramics, where the 3D spatial distribution of phases is difficult to locate from a 2D image. Moreover, we employ the learned model to successfully reconstruct a 3D microstructure consisting of giga-scale voxel data in a few minutes. The resolution of a single voxel is 20 nm, which is higher than that obtained using a typical X-ray computed tomography. These results suggest that deep learning with datasets that learn depth information is essential in 3D microstructural quantifying polycrystalline ceramic materials. Additionally, developing improved segmentation models and datasets will pave the way for data assimilation into operando analysis and numerical simulations of in situ microstructures obtained experimentally and for application to process informatics.

Getting the phase consistent: The importance of phase description in balanced steady-state free precession MRI of multi-compartment systems.

Determine the correct mathematical phase description for balanced steady-state free precession (bSSFP) signals in multi-compartment systems. Based on published bSSFP signal models, different phase descriptions can be formulated: one predicting the presence and the other predicting the absence of destructive interference effects in multi-compartment systems. Numerical simulations of bSSFP signals of water and acetone were performed to evaluate the predictions of these different phase descriptions. For experimental validation, bSSFP profiles were measured at 3T using phase-cycled bSSFP acquisitions performed in a phantom containing mixtures of water and acetone, which replicates a system with two signal components. Localized single voxel MRS was performed at 7T to determine the relative chemical shift of the acetone-water mixtures. Based on the choice of phase description, the simulated bSSFP profiles of water-acetone mixtures varied significantly, either displaying or lacking destructive interference effects, as predicted theoretically. In phantom experiments, destructive interference was consistently observed in the measured bSSFP profiles of water-acetone mixtures, supporting the theoretical description that predicts such interference effects. The connection between the choice of phase description and predicted observation enables unambiguous experimental identification of the correct phase description for multi-compartment bSSFP profiles, which is consistent with the Bloch equations. The study emphasizes that consistent phase descriptions are crucial for accurately describing multi-compartment bSSFP signals, as incorrect phase descriptions result in erroneous predictions.