Rapid Acquisition Research Articles

Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.

Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues. We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work. We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions. We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development. We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.

Inserting Omp22 into the flagellin protein, replacing its hypervariable region, results in stronger protection against lethal Acinetobacter baumannii infection.

Acinetobacter baumannii, a common nosocomial pathogen, is known for its rapid acquisition of antimicrobial resistance, underscoring the urgent need to develop an effective vaccine against this pathogen. Outer membrane protein 22 (Omp22) regulates the biogenesis of outer membrane vesicles to transport virulence-promoting factors into the host cells and facilitates the progression of A. baumannii infection. In this study, we used a mouse model to assess a vaccine's immunogenicity and protective efficacy using recombinant Omp22 protein within the hypervariable region of flagellin (FliC-Omp22). FliC-Omp22 demonstrated superior protection following challenge with a lethal dose of multidrug-resistant (MDR) A. baumannii strain 58ST compared to Omp22 alone. In addition, it elicited increased IgG1/IgG2a and IL-4/IFN-γ ratios, indicating a predominant Th2 immune response. Furthermore, the FliC-Omp22 vaccination elicited strong specific antibodies that inhibited the adhesion and invasion of A. baumannii 58ST and enhanced the opsonic killing activity against the pathogen. FliC-Omp22 immunization significantly reduced bacterial loads in infected mice's spleen, lungs, and liver, thereby improving their survival against the lethal infection caused by MDR A. baumannii 58ST. This study suggests that integrating Omp22 into the hypervariable domain of flagellin holds promise for developing an effective vaccine against A. baumannii infections.

Innovative computational model for optimal deep coal mining under large-scale crustal stress conditions.

Understanding and controlling the ground stress in deep coalfield mining is crucial for ensuring mine safety and efficiency. This study aims to address two unresolved scientific issues in ground stress research: the selection of optimal excavation plans and the large-scale rapid acquisition of ground stress data. A novel computational model is proposed for key rock layer control under different crustal stress conditions. The model considers the complexity and uncertainty of ground stress distribution by simplifying the key rock strata into a fixed-fixed beam model and calculating their safety through mechanical methods. Introducing an innovative approach for acquiring large-scale stress fields using GPS data. By inverting large-scale strain fields in the coalfield area, combined with World Stress Map (WSM) and measured data, accurate stress fields are obtained. The proposed optimal excavation plan can be applied to various coalfield locations worldwide, providing a scientific basis for safe and efficient mining operations. Additionally, the innovative use of GPS data in obtaining large-scale stress fields offers a valuable tool for assessing ground stress variation.

The LTAR Cropland Common Experiment at R. J. Cook Agronomy Farm.

Dryland agriculture in the Inland Pacific Northwest is challenged in part by rising input costs for seed, fertilizer, and agrichemicals; threats to water quality and soil health, including soil erosion, organic matter decline, acidification, compaction, and nutrient imbalances; lack of cropping system diversity; herbicide resistance; and air quality concerns from atmospheric emissions of particulate matter and greenhouse gases. Technological advances such as rapid data acquisition, artificial intelligence, cloud computing, and robotics have helped fuel innovation and discovery but have also further complicated agricultural decision-making and research. Meeting these challenges has promoted interest in (1) supporting long-term research that enables assessment of ecosystem service trade-offs and advances sustainable and regenerative approaches to agriculture, and (2) developing coproduction research approaches that actively engage decision-makers and accelerate innovation. The R. J. Cook Agronomy Farm (CAF) Long-Term Agroecosystem Research (LTAR) site established a cropping systems experiment in 2017 that contrasts prevailing (PRV) and alternative (ALT) practices at field scales over a proposed 30-year time frame. The experimental site is on the Washington State University CAF near Pullman, WA. Cropping practices include a wheat-based cropping system with wheat (Triticum aestivum L.), canola (Brassica napus, variety napus), chickpea (Cicer arietinum), and winter pea (Pisum sativum), with winter wheat produced every third year under the ALT practices of continuous no-tillage and precision applied N, compared to the PRV practice of reduced tillage (RT) and uniformly applied agrichemicals. Biophysical measurements are made at georeferenced locations that capture field-scale spatial variability at temporal intervals that follow approved methods for each agronomic and environmental metric. Research to date is assessing spatial and temporal variations in cropping system performance (e.g., crop yield, soil health, and water and air quality) for ALT versus PRV and associated tradeoffs. Future research will explore a coproduction approach with the intent of advancing discovery, innovation, and impact through collaborative stakeholder-researcher partnerships that direct and implement research priorities.

Enhanced TomoSAR imaging method with complex least squares for 3D building reconstruction

ABSTRACT Synthetic aperture radar tomography (TomoSAR) has emerged as an advanced technology for the rapid acquisition of three-dimensional information, with model solutions dependent on a specific complex least squares adjustment criterion. We design two TomoSAR imaging algorithms based on two adjustment criterions minimizing the square sum of the real and imaginary components of observation vector residual, and minimizing the square sum of the L2-norm of observation vector residual. Additionally, we propose a strong scatterer recognition method that combines amplitude thresholding and density-based spatial clustering of applications with noise to alleviate the low efficiency and excessive noise in real images. The simulated and practical experiments are conducted to evaluate the performance of algorithms. Results suggested that two algorithms perform similarly in parameter estimation accuracy and noise immunity for buildings with simple structures and single scatterer. However, for buildings with multiple scatterers, the algorithm derived by minimizing the square sum of the L2-norm from the observation vector residual exhibits more robustly than the other algorithm, accurately distinguishing multiple scatterers within pixels and reconstructing 3D scenes mirroring the actual ones. The results verify the effectiveness of the proposed strong scatterer recognition method and the importance of the adjustment criterion in TomoSAR imaging.

Rapid chromosome evolution and acquisition of thermosensitive stochastic sex determination in nematode androdioecious hermaphrodites

The factors contributing to evolution of androdioecy, the coexistence of hermaphrodites and males such as in Caenorhabditis elegans, remains poorly known. However, nematodes exhibit androdioecy in at last 13 genera with the predatory genus Pristionchus having seven independent transitions towards androdioecy. Nonetheless, associated genomic architecture and sex determination mechanisms are largely known from Caenorhabditis. Here, studying 47 Pristionchus species, we observed repeated chromosome evolution which abolished the ancestral XX/XO sex chromosome system. Two phylogenetically unrelated androdioecious Pristionchus species have no genomic differences between sexes and mating hermaphrodites with males resulted in hermaphroditic offspring only. We demonstrate that stochastic sex determination is influenced by temperature in P. mayeri and P. entomophagus, and CRISPR engineering indicated a conserved role of the transcription factor TRA-1 in P. mayeri. Chromosome-level genome assemblies and subsequent genomic analysis of related Pristionchus species revealed stochastic sex determination to be derived from XY sex chromosome systems through sex chromosome-autosome fusions. Thus, rapid karyotype evolution, sex chromosome evolution and evolvable sex determination mechanisms are general features of this genus, and represent a dynamic background against which androdioecy has evolved recurrently. Future studies might indicate that stochastic sex determination is more common than currently appreciated.

Potential Therapeutic Targets for Combination Antibody Therapy Against Staphylococcus aureus Infections

Despite the significant advances in antibiotic treatments and therapeutics, Staphylococcus aureus (S. aureus) remains a formidable pathogen, primarily due to its rapid acquisition of antibiotic resistance. Known for its array of virulence factors, including surface proteins that promote adhesion to host tissues, enzymes that break down host barriers, and toxins that contribute to immune evasion and tissue destruction, S. aureus poses a serious health threat. Both the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) classify S. aureus as an ESKAPE pathogen, recognizing it as a critical threat to global health. The increasing prevalence of drug-resistant S. aureus underscores the need for new therapeutic strategies. This review discusses a promising approach that combines monoclonal antibodies targeting multiple S. aureus epitopes, offering synergistic efficacy in treating infections. Such strategies aim to reduce the capacity of the pathogen to develop resistance, presenting a potent adjunct or alternative to conventional antibiotic treatments.

Estimating coffee crop parameters through multispectral imaging and machine learning algorithms

Abstract. Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).

Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting.

In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

Pruned tree-structured temporal convolutional networks for quality variable prediction of industrial process

In real industrial processes, the rapid and accurate acquisition of quality variables is essential. Therefore, this paper proposes a pruned tree-structured temporal convolutional network (PT-TCN) for efficient and accurate variables prediction. First, a novel tree network is developed, utilizing dilated causal convolution blocks as nodes to avoid the loss of local information. Each node extracts distinct local information, and by concatenating all tree nodes, the network can capture a comprehensive range of temporal scales. Then, to avoid the increased complexity caused by the tree structure, we design an online two-stage pruning strategy to compress the tree network without introducing additional computations. During the training process, blocks are initially pruned based on the correlation assessment between quality variables and tree nodes. Subsequently, weight normalization layers are employed to evaluate the importance of output channels in blocks, thereby enabling intra-block channel pruning. The effectiveness of PT-TCN is verified on Tennessee Eastman benchmark process. In addition, experiments on the real zinc flotation process demonstrate that the proposed PT-TCN improves in R2 and MAE by 1.32% and 1.26% respectively in predicting quality variables, and it can reduce 91.8% parameters of the initial tree-structured TCN without sacrificing accuracy.

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

Water Volume and 3D Topographic Analysis of Hub Dam, Karachi, Using Remote Sensing and Global Bathymetry Data

Background: Karachi one of the largest and fastest-growing cities in the world, the city faces severe water scarcity and aggravated by climate change, excessive groundwater extraction in dense urban areas and inefficient water distribution systems. The Hub Dam, along with the Keenjhar Lake, is the source of water for Karachi's population as well as for the industrial and urban agricultural sectors. It is therefore important to have current information on the dam's storage capacity, its topographic features and the hydrological dynamics of its catchment area, which remains poorly understood to date. Objectives: This study's primary objectives are (1) to assess the hydrography of Hub Dam using global bathymetric and remote sensing data and (2) to create a detailed 3D model of the dam to facilitate analysis of its hydrological features. (3) Also generate data-driven insights to guide improvements in Karachi’s urban water management policies. Methods: The methodology integrates global bathymetric data with remote sensing and Google Earth Engine (GEE) for analysing Hub Dam’s hydrological and topographic characteristics. Data processing and visualization were conducted in Google Colab by python codes. This process enabling an efficient workflow for handling and analysing large datasets. Results: Hub Dam 3D model is developed. The approaches is enhancing visualization of the dam's water volume and terrain features to support detailed hydrological analysis. 3D volumetric analysis estimates with total water volume at approximately 79.954 · 106 m3. It was found that currently, the Hub Dam reservoir has enough water to fulfil Karachi needs for 503 days, a critical reserve for urban planning, resource management. Using a topographic analysis that includes surrounding slopes and watershed features, key elements affecting dam performance were identified. This result helps to adjust the filling of the dam with water. The Hub Dam's ability to store water was confirmed through a preliminary volume assessment. Conclusion: The study successfully achieved its objectives. The results obtained highlight the importance of adaptive well informed strategies for sustaining well managed water resources in response to environmental pressures. The current study further advance rational water management by integrating remote sensing and data-driven methodologies into this area of knowledge. That is, the effectiveness of Using Remote Sensing and Global Bathymetry Data has been repeatedly proven by other studies in different areas of knowledge. However, in the current study, this scientific approach has successfully demonstrated the efficient and rapid acquisition of accurate, up-to-date information on reservoir capacity and hydrological characteristics, which opens up new opportunities for efficient water management.

Orthogonal dispersion modeling of double-stage VIPA spectrometer in SBS system

Brillouin microscopy enables the assessment of the mechanical properties of biological tissues by mapping the Brillouin shift in three-dimensional (3D), all-optical, label-free, non-contact, and subcellular resolution. The virtually imaged phased array (VIPA) etalon is widely utilized for measuring Brillouin spectra owing to its superior light throughput, large angular dispersion, and rapid signal acquisition capabilities. The VIPA-based spectrometer plays a significant role in Brillouin microscopy, but it is highly sensitive to factors such as the tilt angle, beam radius, lens focal length, and so on. Here, we propose an orthogonal dispersion model based on paraxial wave theory. This model is employed to explore the output intensity distribution and dispersion characteristics of a double-stage VIPA spectrometer. To validate the proposed model, we develop a stimulated Brillouin scattering (SBS) system by leveraging the optimal spectrometer parameters to measure the Brillouin frequency shift in pure water. We demonstrate the capabilities of this model in mitigating spectral dispersion and enhancing spectral measurement accuracy by optimizing the spectrometer's system parameters. This work is pivotal for the future deployment of the double-stage VIPA spectrometer in SBS-based microscopy applications.

Spatial transcriptomics analysis identifies therapeutic targets in diffuse high-grade gliomas.

Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma. Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots. We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma. Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.

Stromal reprogramming overcomes resistance to RAS-MAPK inhibition to improve pancreas cancer responses to cytotoxic and immune therapy.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is often resistant to therapy. An immune suppressive tumor microenvironment (TME) and oncogenic mutations in KRAS have both been implicated as drivers of resistance to therapy. Mitogen-activated protein kinase (MAPK) inhibition has not yet shown clinical efficacy, likely because of rapid acquisition of tumor-intrinsic resistance. However, the unique PDAC TME may also be a driver of resistance. We found that long-term focal adhesion kinase (FAK) inhibitor treatment led to hyperactivation of the RAS/MAPK pathway in PDAC cells in mouse models and tissues from patients with PDAC. Concomitant inhibition of both FAK (with VS-4718) and rapidly accelerated fibrosarcoma and MAPK kinase (RAF-MEK) (with avutometinib) induced tumor growth inhibition and increased survival across multiple PDAC mouse models. In the TME, cancer-associated fibroblasts (CAFs) impaired the down-regulation of MYC by RAF-MEK inhibition in PDAC cells, resulting in resistance. By contrast, FAK inhibition reprogramed CAFs to suppress the production of FGF1, which can drive resistance to RAF-MEK inhibition. The addition of chemotherapy to combined FAK and RAF-MEK inhibition led to tumor regression, a decrease in liver metastasis, and improved survival in KRAS-driven PDAC mouse models. Combination of FAK and RAF-MEK inhibition alone improved antitumor immunity and priming of T cell responses in response to chemotherapy. These findings provided the rationale for an ongoing clinical trial evaluating the efficacy of avutometinib and defactinib in combination with gemcitabine and nab-paclitaxel in patients with PDAC and may suggest further paths for combined stromal and tumor-targeting therapies.

Differentiation of MS lesions through analysis of microvascular distribution

Abstract Conventional MRI is crucial for diagnosing multiple sclerosis (MS) but lacks precision, leading to the clinico-radiological paradox and misdiagnosis risk, especially when confronted with unspecific lesions not related to MS. Advancements in perfusion-weighted imaging (PWI) with an algorithm designed for diseases with anticipated contrast agent extravasation offer insight into microvascular impairment and flow heterogeneity. Our study aimed to assess these factors in MS patients and their association with clinically relevant white matter injury and disease course. We evaluated 60 adults with white matter lesions (WML), including 50 diagnosed with MS or MS syndromes and 10 non-diseased symptomatic controls (SC) with unspecific WML. MRI included conventional three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), 3D magnetization-prepared 2 rapid acquisition gradient-echo (MP2RAGE), post-contrast 3D T1-weighted (T1) images and Dynamic Susceptibility Contrast (DSC) PWI at 3T. WML masks of “unspecific T2-FLAIR lesions”, “MS T2-FLAIR lesions”, and “MS T1-lesions” were manually outlined and validated by a neuroradiologist. DSC-derived parameters were analyzed in WML masks and healthy-appearing tissue. MS T2-FLAIR lesions showed increased flow heterogeneity and vasodilation compared to unspecific T2-FLAIR lesions in SC, as well as compared to unspecific T2-FLAIR lesions within the MS group. MS T1-lesions exhibited more homogenized flow. Our findings suggest that DSC-PWI combined with lesion delineation, can provide clinically relevant differentiation of MS lesions from unspecific WML, highlighting potential microvascular pathology previously overlooked in MS.

Single pulse shaping for higher harmonic demodulation in terahertz time-domain spectroscopy

We present a simple, highly stable, low noise, and rapid detection scheme using higher harmonic demodulation applied to terahertz time domain spectroscopy (THz-TDS). The presence of higher harmonics in the detected periodic signal is because of the non-sinusoidal shape of a single pulse, which is controlled by an ultrafast current pre-amplifier. Instead of using external signal modulators, the use of the inherent repetition rate (frep) of the femtosecond laser and its harmonics as reference for the lock-in amplifier simplifies the setup, while allows rapid and low noise detection owing to the megahertz modulation frequencies. Unlike the signal detected at the fundamental frep, signals detected at higher harmonics have much lower offset and are unaffected by perturbations in the environment present during measurements, which is an essential characteristic for an analytical tool. Our proposed technique can be readily integrated to existing THz-TDS systems and is applicable to scans with rapid acquisition times and to scans that require long periods of time (e.g., hyperspectral imaging).

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.

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding.

To enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts. A displacement-encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center-out golden-angle stack-of-stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel-imaging and compressed-sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE-based MRE sequences via phantom and brain acquisitions. The proposed sequence achieved a 6-fold acceleration compared with GRE MRE. With the application of a conventional parallel-imaging and compressed-sensing algorithm, the scanning speed was further accelerated by 8-fold, matching the speed of EPI-based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast-to-noise ratio (1.38) than the two EPI-based sequences (0.61/0.76) and similar to GRE-based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance. A rapid DENSE-based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI-based sequences, without compromising SNR or introducing distortion artifacts.