Lorentzian Fitting Research Articles

Improving quantification accuracy of a nuclear Overhauser enhancement signal at −1.6 ppm at 4.7 T using a machine learning approach

Objective.A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at -1.6 ppm, potentially from choline phospholipids and termed NOE(-1.6), has been reported in biological tissues at high magnetic fields. This signal shows promise for detecting brain tumors and strokes. However, its proximity to the water peak and low signal-to-noise ratio makes accurate quantification challenging, especially at low fields, due to the difficulty in separating it from direct water saturation and other confounding signals. This study proposes using a machine learning (ML) method to address this challenge.Approach.The ML model was trained on a partially synthetic chemical exchange saturation transfer dataset with a curriculum learning denoising approach. The accuracy of our method in quantifying NOE(-1.6) was validated using tissue-mimicking data from Bloch simulations providing ground truth, with subsequent application to an animal tumor model at 4.7 T. The predictions from the proposed ML method were compared with outcomes from traditional Lorentzian fit and ML models trained on other data types, including measured and fully simulated data.Main results.Our tissue-mimicking validation suggests that our method offers superior accuracy compared to all other methods. The results from animal experiments show that our method, despite variations in training data size or simulation models, produces predictions within a narrower range than the ML method trained on other data types.Significance.The ML method proposed in this work significantly enhances the accuracy and robustness of quantifying NOE(-1.6), thereby expanding the potential for applications of this novel molecular imaging mechanism in low-field environments.

Amide proton transfer-weighted (APTw) imaging and derived quantitative metrics in evaluating gliomas: Improved performance compared to magnetization transfer ratio asymmetry (MTRasym).

Isocitrate dehydrogenase (IDH) status, glioma subtypes and tumor proliferation are important for glioma evaluation. We comprehensively compare the diagnostic performance of amide proton transfer-weighted (APTw) MRI and its related metrics in glioma diagnosis, in the context of the latest classification. Totally 110 patients with adult-type diffuse gliomas underwent APTw imaging. The magnetization transfer ratio asymmetry (MTRasym), magnetization transfer ratio normalized by reference signal (MTRnormref), and spillover-corrected magnetization transfer ratio yielding Rex (MTRRex), and metrics based on Lorentzian fitting (Fit-amide, Fit-MTRnormref, and Fit-MTRRex) were calculated. Group differences were compared between IDH genotypes, and among three glioma subtypes. The diagnostic performances were assessed using the receiver operating characteristic (ROC) analysis and compared. The correlations with Ki-67 expression were also analyzed. All APTw-related metrics exhibited significantly higher values in IDH-wildtype gliomas than in IDH-mutant gliomas (all p < 0.001). Fit-MTRnormref had the best area under the curve (AUC) of 0.858. All APTw-related metrics in glioblastomas were significantly higher than oligodendrogliomas (all p < 0.01) and astrocytomas (all p < 0.001). No metrics had significant difference between oligodendrogliomas and astrocytomas. The highest AUCs was 0.870 for Fit-MTRnormref in distinguishing astrocytomas from glioblastomas, and 0.867 for Fit-MTRRex in distinguishing oligodendrogliomas from glioblastomas. Besides, Fit-MTRnormref had the highest correlation coefficient with Ki-67 expression of 0.578. APTw-related metrics can effectively evaluate glioma IDH status, tumor subtypes and proliferation. The combination of Lorentzian fitting and the reference signal normalization could further improve the diagnostic performance, and perform better than MTRasym.

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder.

To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods. The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods.

Simultaneous monitoring of glycogen, creatine, and phosphocreatine in type II glycogen storage disease using saturation transfer MRI.

There is a need for non-invasive approaches to assess the progression of glycogen storage diseases (GSD). Here, we use saturation transfer (ST) MRI via relayed nuclear Overhauser effects (glycoNOE) to detect abnormal changes in muscle glycogen of a GSD II mouse model. In addition to glycogen, the energy metabolites phosphocreatine (PCr) and creatine (Cr) were studied to assess the muscle disease. Water saturation (Z-spectra) and 1H MRS were acquired at 9.4 T on the skeletal muscle of healthy control mice and homozygous acid -glucosidase (GAA) knock-out mice (ages of 2-48 weeks). The glycoNOE (-1 ppm), total creatine (tCr)* (+2 ppm, = a × [Cr] + b × [PCr]), and PCr (+2.5 ppm) from Z-spectra and the ratio between tCr and taurine signals (tCr/Tau) from 1H MRS spectra were quantified by using multi-pool Lorentzian fitting methods. The concentrations of the metabolites were also measured via tissue assays. The postnatal GSD II mice (age <12 weeks) showed a continued accumulation of muscle glycoNOE signal. GlycoNOE in adult GSD II mice (age ≥12 weeks) reached a plateau, at a level above 400% of that in normal mice. PCr, tCr*, and tCr/Tau gradually decreased in GSD II mice during the postnatal stage, then stabilized at levels comparable to adult control, yet PCr in adult GSD II mice was lower than that in controls. This study demonstrates that ST MRI of glycogen can provide in situ non-invasive biomarkers for GSD II disease progression, with the potential to study the progression and treatment response of GSDs.

Mitochondrial oxidative phosphorylation capacity in skeletal muscle measured by ultrafast Z-spectroscopy (UFZ) MRI at 3T.

To investigate the feasibility of rapid CEST MRI acquisition for evaluating oxidative phosphorylation (OXPHOS) in human skeletal muscle at 3T, utilizing ultrafast Z-spectroscopy (UFZ) combined with MRI and the Polynomial and Lorentzian line-shape Fitting (PLOF) technique. UFZ MRI on muscle was evaluated with turbo spin echo (TSE) and 3D EPI readouts. Five healthy subjects performed in-magnet plantar flexion exercise (PFE) and subsequent changes of amide, PCr, and partial PCr mixed Cr (Cr+) CEST dynamic signals post-exercise were enabled by PLOF fitting. PCr/Cr CEST signal was further refined through pH correction by using the ratios between PCr/Cr and amide signals, named PCAR/CAR, respectively. UFZ MRI with TSE readout significantly reduces acquisition time, achieving a temporal resolution of <50 s for collecting high-resolution Z-spectra. Following PFE, the recovery/decay times (τ) for both PCr and Cr in the gastrocnemius muscle of the calf were notably longer when determined using PCr/Cr CEST compared to those after pH correction with amideCEST, namely = 87.1 ± 15.8 s and = 98.1 ± 20.4 s versus = 32.9 ± 19.7 s and = 43.0 ± 13.0 s, respectively. obtained via 31P MRS ( = 50.3 ± 6.2 s) closely resemble those obtained from pH-corrected PCr/Cr CEST signals. The outcomes suggest potential of UFZ MRI as a robust tool for non-invasive assessment of mitochondrial function in skeletal muscles. pH correction is critical for the reliable OXPHOS measurement by CEST.

Temperature extraction from Brillouin sensing based on temporal convolutional networks

The Brillouin optical time-domain sensing system has become a hot spot of research due to its ability to seamlessly monitor the temperature and strain variations in optical fibers along the line. Given its current limitations of low accuracy and inadequate real-time performance in long-distance monitoring, the Brillouin gain extraction temperature method based on temporal convolutional networks is proposed. On this basis, we established a Brillouin optical time-domain experimental system where comprehensive simulations and tests were conducted to assess the temperature extraction performance under different conditions. Besides, a comparison was made between the system and traditional methods like Lorentz fitting method and extreme learning machine method. The results have suggested that the temporal convolutional network exhibits remarkable measurement accuracy, even in scenarios with low signal-to-noise ratios and large sweep frequency steps.

Multi-pool chemical exchange saturation transfer MRI in glioma grading, molecular subtyping and evaluating tumor proliferation.

To evaluate the performance of multi-pool Chemical exchange saturation transfer (CEST) MRI in prediction of glioma grade, isocitrate dehydrogenase (IDH) mutation, alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss and Ki-67 labeling index (LI), based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5). 95 patients with adult-type diffuse gliomas were analyzed. The amide, direct water saturation (DS), nuclear Overhauser enhancement (NOE), semi-solid magnetization transfer (MT) and amine signals were derived using Lorentzian fitting, and asymmetry-based amide proton transfer-weighted (APTwasym) signal was calculated. The mean value of tumor region was measured and intergroup differences were estimated using student-t test. The receiver operating curve (ROC) and area under the curve (AUC) analysis were used to evaluate the diagnostic performance of signals and their combinations. Spearman correlation analysis was performed to evaluate tumor proliferation. The amide and DS signals were significantly higher in high-grade gliomas compared to low-grade gliomas, as well as in IDH-wildtype gliomas compared to IDH-mutant gliomas (all p < 0.001). The DS, MT and amine signals showed significantly differences between ATRX loss and retention in grade 2/3 IDH-mutant gliomas (all p < 0.05). The combination of signals showed the highest AUC in prediction of grade (0.857), IDH mutation (0.814) and ATRX loss (0.769). Additionally, the amide and DS signals were positively correlated with Ki-67 LI (both p < 0.001). Multi-pool CEST MRI demonstrated good potential to predict glioma grade, IDH mutation, ATRX loss and Ki-67 LI.

Desarrollo de un método de clasificación de pastas cerámicas basado en el análisis de imágenes de radiografía digital

X-rays have motivated the application of imaging techniques in Heritage Sciences, providing a tool that produces global information about the object. The radiography analysis generates information with physical meaning relating the material properties with the photon attenuation. In this work, the characterization of a digital radiography system is presented, and the basis of an analysis method for ceramics sherds is described, based on the study of histograms, which represent the distribution of pixel intensities that made the image. Thus, this study aims to provide a tool, based on a mathematical analysis, encompassing a Lorentzian fit to each image histogram and a cluster analysis, that leads to establish a contrast between ceramic pastes. By applying this method, we could distinguish six paste groups from a set of 275 ceramic objects (archaeological and contemporary) from the Maya region.

An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing.

Development and Validation of Four Different Methods to Improve MRI-CEST Tumor pH Mapping in Presence of Fat.

CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. In this study, we investigated four post-processing approaches to overcome fat signal influences and enable correct CEST contrast calculations and tumor pH measurements using iopamidol. The proposed methods involve replacing the Z-spectrum region affected by fat peaks by (i) using a linear interpolation of the fat frequencies, (ii) applying water pool Lorentzian fitting, (iii) considering only the positive part of the Z-spectrum, or (iv) calculating a correction factor for the ratiometric value. In vitro and in vivo studies demonstrated the possibility of using these approaches to calculate CEST contrast and then to measure tumor pH, even in the presence of moderate to high fat fraction values. However, only the method based on the water pool Lorentzian fitting produced highly accurate results in terms of pH measurement in tumor-bearing mice with low and high fat contents.

Quick methylene blue dye elimination via SDS-Ag nanoparticles catalysts

Methylene blue dye, being toxic, carcinogenic and non-biodegradable, poses a serious threat for human health and environmental safety. The effective and time-saving removal of such industrial dye necessitates the use of innovative technologies such as silver nanoparticle-based catalysis. Utilizing a pulsed Nd:YAG laser operating at the second harmonic generation of 532 nm with 2.6 J energy per pulse and 10 ns pulse duration, Ag nanoparticles were synthesized via an eco-friendly method with sodium dodecyl sulphate (SDS) as a capping agent. Different exposure times (15, 30, and 45 min) resulted in varying nanoparticle sizes. Characterization was achieved through UV–Vis absorption spectroscopy, scanning electron microscopy (SEM) imaging, and energy dispersive X-ray (EDX). Lorentzian fitting was used to model nanoparticle size, aligning well with SEM results. Mie’s theory was applied to evaluate the absorption, scattering, and extinction cross-sectional area spectra. EDX revealed increasing Ag and carbon content with exposure time. The SDS-caped AgNPs nanoparticles were tested as catalyst for methylene blue degradation, achieving up to 92.5% removal in just 12 min with a rate constant of 0.2626 min−1, suggesting efficient and time-saving catalyst compared to previously reported Ag-based nanocatalysts.

A Denoising Convolutional Autoencoder for SNR Enhancement in Chemical Exchange Saturation Transfer imaging: (DCAE-CEST).

To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods. The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of 1D convolutions, nonlinearity applications and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in-vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirms the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared to other methods.

Whole-cerebrum guanidino and amide CEST mapping at 3 T by a 3D stack-of-spirals gradient echo acquisition.

To develop a 3D, high-sensitivity CEST mapping technique based on the 3D stack-of-spirals (SOS) gradient echo readout, the proposed approach was compared with conventional acquisition techniques and evaluated for its efficacy in concurrently mapping of guanidino (Guan) and amide CEST in human brain at 3 T, leveraging the polynomial Lorentzian line-shape fitting (PLOF) method. Saturation time and recovery delay were optimized to achieve maximum CEST time efficiency. The 3DSOS method was compared with segmented 3D EPI (3DEPI), turbo spin echo, and gradient- and spin-echo techniques. Image quality, temporal SNR (tSNR), and test-retest reliability were assessed. Maps of Guan and amide CEST derived from 3DSOS were demonstrated on a low-grade glioma patient. The optimized recovery delay/saturation time was determined to be 1.4/2 s for Guan and amide CEST. In addition to nearly doubling the slice number, the gradient echo techniques also outperformed spin echo sequences in tSNR: 3DEPI (193.8 ± 6.6), 3DSOS (173.9 ± 5.6), and GRASE (141.0 ± 2.7). 3DSOS, compared with 3DEPI, demonstrated comparable GuanCEST signal in gray matter (GM) (3DSOS: [2.14%-2.59%] vs. 3DEPI: [2.15%-2.61%]), and white matter (WM) (3DSOS: [1.49%-2.11%] vs. 3DEPI: [1.64%-2.09%]). 3DSOS also achieves significantly higher amideCEST in both GM (3DSOS: [2.29%-3.00%] vs. 3DEPI: [2.06%-2.92%]) and WM (3DSOS: [2.23%-2.66%] vs. 3DEPI: [1.95%-2.57%]). 3DSOS outperforms 3DEPI in terms of scan-rescan reliability (correlation coefficient: 3DSOS: 0.58-0.96 vs. 3DEPI: -0.02 to 0.75) and robustness to motion as well. The 3DSOS CEST technique shows promise for whole-cerebrum CEST imaging, offering uniform contrast and robustness against motion artifacts.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Nanocrystalline Iron Oxides with Various Average Crystallite Size Investigated Using Magnetic Resonance Method

A series of nanocrystalline iron oxide samples (M1–M5) which differ from each other in average crystallite size (from 26 to 37 nm) was studied. The raw material was nanocrystalline iron with an average crystallite size equal to 21 nm promoted with hardly reducible oxides: Al2O3, CaO, K2O (in total, max. 10 wt%). Nanocrystalline iron was subjected to oxidation with water vapor to achieve different oxidation degrees (α = 0.16–1.00). Metallic iron remaining in the samples after the oxidizing step was removed by etching. Magnetic resonance spectra of all samples were obtained at room temperature. All resonance lines were asymmetric and intense. These spectra were fitted by Lorentzian and Gaussian functions. All spectral parameters depend on the preparation method of the nanoparticles. We suppose that the Lorentz fit gives us a spectrum from larger agglomerated sizes whereas the Gaussian fit comes from much smaller magnetic centers. For the nanocrystalline samples with the largest size of iron oxide nanocrystallites, the highest value of total integrated intensity was obtained, indicating that at smaller sizes, they are more mobile in reorientation processes resulting in more settings of anti-parallel magnetic moments. The magnetic anisotropy should also increase with the increase in size of nanocrystallites.

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.

Narrow laser linewidth measurement with the optimal demodulated Lorentzian spectrum.

A method called the optimal demodulated Lorentzian spectrum is employed to precisely quantify the narrowness of a laser's linewidth. This technique relies on the coherent envelope demodulation of a spectrum obtained through short delayed self-heterodyne interferometry. Specifically, we exploit the periodic features within the coherence envelope spectrum to ascertain the delay time of the optical fiber. Furthermore, the disparity in contrast within the coherence envelope spectrum serves as a basis for estimating the laser's linewidth. By creating a plot of the coefficient of determination for the demodulated Lorentzian spectrum fitting in relation to the estimated linewidth values, we identify the existence of an optimal Lorentzian spectrum. The corresponding laser linewidth found closest to the true value is deemed optimal. This method holds particular significance for accurately measuring the linewidth of lasers characterized as narrow or ultranarrow.

PH Mapping of Gliomas Using Quantitative Chemical Exchange Saturation Transfer MRI: Quasi-Steady-State, Spillover-, and MT-Corrected Omega Plot Analysis.

Quantitative in-situ pH mapping of gliomas is important for therapeutic interventions, given its significant association with tumor progression, invasion, and metastasis. Although chemical exchange saturation transfer (CEST) offers a noninvasive way for pH imaging based on the pH-dependent exchange rate (ksw), the reliable quantification of ksw in glioma remains constrained due to technical challenges. To quantify the pH of gliomas by measuring the proton exchange rate through optimized omega plot analysis. Prospective. Creatine and murine brain lysates phantoms, six rats with glioma xenograft model, and three patients with World Health Organization grade 2-4 gliomas. 11.7 T, 7.0 T, CEST imaging, T2-weighted (T2W) imaging, and T1-mapping. Omega plot analysis, quasi-steady-state (QUASS) analysis, multi-pool Lorentzian fitting, amine and amide concentration-independent detection, pH enhanced method with the combination of amide and guanidyl (pHenh), and magnetization transfer ratio (MTR) were utilized for pH metric quantification. The clinical outcomes were determined through radiologic follow-up and histopathological analysis. Mann-Whitney U test was performed to compare glioma with normal tissue, and Pearson's correlation analysis was used to assess the relationship between ksw and other parameters. In vitro experiments reveal that the determined ksw at 2 ppm increases exponentially with pH (creatine phantoms: ksw = 106 + 0.147 × 10(pH-4.198); lysates: ksw = 185.1 + 0.101 × 10(pH-3.914)). Omega plot analysis exhibits a linear correlation between 1/MTRRex and 1/ω1 2 in the glioma xenografts (R2 > 0.98) and glioma patients (R2 > 0.99). The exchange rate in the rat glioma decreases compared to the contralateral normal tissue (349.46 ± 30.40 s-1 vs. 403.54 ± 51.01 s-1, P = 0.025), while keeping independence from changes in concentration (r = 0.5037, P = 0.095). Similar pattern was observed in human data. Utilizing QUASS-based, spillover-, and MT-corrected omega plot analysis for the measurement of exchange rates, offers a feasible method for quantifying pH within glioma. NA TECHNICAL EFFICACY: Stage 1.

Voigt Profile is Suitable for Impedance Spectroscopic Analysis of BLSF Ceramics

Abstract: In our earlier studies, samarium-modified bismuth layer-structured ferroelectrics (BLSF), namely SmxBi3-x TiNbO9 (SBTN); (x=0, 0.2, 0.4, 0.6. 0.8, and 1.0), prepared by solid-state reaction method, have shown broad dielectric relaxations. Among all the compositions, Bi2.6Sm0.4TiNbO9 (BST-0.4) has shown a lower tolerance factor, high remnant polarization (Pr) value, and low stretching factor. In addition, the compound has shown interesting broad dielectric relaxation and non-Debye nature. The specific reasons for the broad relaxation remain unclear. To gain more insights, we synthesized multi rare earth ion substituted compounds, namely, Bi2.6La0.2Sm0.2TiNbO9 (sample A) and Bi2.6La0.2Gd0.2TiNbO9 (sample B), using the conventional solid-state reaction method. A single impedance spectroscopic approach alone may not be sufficient to explain the asymmetric or non-Debye behavior, therefore Lorentzian, Gaussian, and Voigt fitting were adopted in the present investigation. From the analysis, the Voigt profile (Vf and Vʹf) is found to be a valuable alternative tool to understand the broad impedance non-Debye relaxations. Finally, results were corroborated by dc-conductivity data. Keywords: BLSF, Modulus spectroscopy, Relaxation, Defect-mechanism, Gaussian, Lorentz and Voigt fitting, Dc conductivity.