Reduce Data Acquisition Time Research Articles

Investigation to Understand the Role of Phase Variation in Red Emitting Eu3+-Doped Calcium Magnesium Silicate Phosphor for In Vitro Bioimaging.

Eu3+-doped silicate phosphors are gaining significant attention for bioimaging and scaffold development due to their narrow red emission, high color purity, quantum yield (QY), and large Stokes shift. These phosphors offer several advantages over conventional imaging techniques, such as good selectivity and sensitivity, simpler operation, reduced data acquisition time, cost-effectiveness, and nondestructive imaging. The luminescence properties of these phosphors can be enhanced by modifying synthesis methods, annealing conditions, and hosts and introducing multiple dopants. This study explores a novel approach for improving luminescence by modifying the crystal structures of Eu3+ doped calcium magnesium silicate (CMS:Eu3+) phosphors for in vitro bioimaging and potential scaffold development. The synthesized diopside (CaMgSi2O6:xEu3+; x = 10, 15, and 20 mol %), merwinite (Ca3MgSi2O8:15 mol % Eu3+), and akermanite (Ca2MgSi2O7:15 mol % Eu3+) phases of CMS:Eu3+ exhibit distinct coordination environments for Eu3+, leading to unique excitation wavelength tunability from ultraviolet (UV) to the visible region, high emission intensity, decay time, QY > 40%, and color purity >83%. A comparative analysis of their structural and photoluminescence properties reveals the impact of phase modifications on luminescence for in vitro bioimaging by optimizing the dopant concentration. The results indicate that CaMgSi2O6: 15 mol % Eu3+ is the most efficient phosphor for in vitro bioimaging, with the highest relative emission intensity in the red region, decay time ∼2 ms, QY ∼ 77%, and color purity ∼86%. The unique morphology of Ca3MgSi2O8:15 mol %Eu3+ and Ca2MgSi2O7:15 mol % Eu3+ also supports cell adhesion, suggesting their potential in scaffold development. In brief, the study highlights the potential of CMS:Eu3+ phosphors for in vitro bioimaging and scaffold development by modifying phases and dopant concentrations.

Optimized MR pulse sequence for high-resolution brain 3D-T1ρ mapping with weighted spin-lock acquisitions.

To implement and evaluate the feasibility of brain spin-lattice relaxation in the rotating frame (T1ρ) mapping using a novel optimized pulse sequence that incorporates weighted spin-lock acquisitions, enabling high-resolution three-dimensional (3D) mapping. The optimized variable flip-angle framework, previously proposed for knee T1ρ mapping, was enhanced by integrating weighted spin-lock acquisitions. This strategic combination significantly boosts signal-to-noise ratio (SNR) while reducing data acquisition time, facilitating high-resolution 3D-T1ρ mapping of the brain. The proposed sequence was compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo sequence snapshots (MAPSS). The newly developed pulse sequence, tested for brain 3D-T1ρ mapping for the first time, obtained maps in 4 min with quality comparable to a 20-min MAPSS sequence. Specifically, the voxel-wise median absolute percentage difference between these MR sequences at a resolution of 0.9 × 0.9 × 3 mm3 is 13.1%. If high resolution is desired, with a voxel size of 0.5 × 0.5 × 3 mm3, the new sequence can acquire T1ρ maps in 8 min, surpassing a 20-min (and resolution of 0.9 × 0.9 × 3 mm3) MAPSS in SNR. The weighted spin-lock acquisition combined with optimized variable flip angle improved the SNR over optimized variable flip angle alone by about 28%. Compared with the 20-min MAPSS sequence for brain T1ρ mapping, the proposed learned high-resolution 3D pulse sequence simultaneously achieved a 2.3-fold improvement in effective (3.2-fold nominal) spatial resolution, a 1.1-fold improvement in SNR, and a 2.5-fold reduction in scan time.

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging.

The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy. DiffDL was trained using data from the Human Connectome Project, including 300 training/validation subjects and 50 testing subjects. High-quality DTI and DKI metrics were generated using many DWIs and combined with subsets of DWIs to form training pairs. A UNet architecture was used for denoising, trained over 500 epochs with a linear noise schedule. Performance was evaluated against conventional DTI/DKI modeling and a reference UNet model using normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (PCC). DiffDL showed significant improvements in the quality and accuracy of fractional anisotropy (FA) and mean diffusivity (MD) maps compared to conventional methods and the baseline UNet model. For DKI metrics, DiffDL outperformed conventional DKI modeling and the UNet model across various acceleration scenarios. Quantitative analysis demonstrated superior NMAE, PSNR, and PCC values for DiffDL, capturing the full dynamic range of DTI and DKI metrics. The generative nature of DiffDL allowed for multiple predictions, enabling uncertainty quantification and enhancing performance. The DiffDL framework demonstrated the potential to significantly reduce data acquisition times in diffusion MRI while maintaining high metric quality. Future research should focus on optimizing computational demands and validating the model with clinical cohorts and standard MRI scanners.

A near-field SIMO millimeter-wave imaging system based on RMA

Abstract In the imaging of human security checks, high-resolution millimeter wave imaging systems are required. The millimeter wave imaging system has a large amount of data and requires a long time. So, in this design, in terms of data acquisition, the system uses the SIMO method, using one transmitting antenna and four receiving antennas to maintain imaging resolution while reducing data acquisition time by four times. In this system, MATLAB sends instructions to the rail control board through the serial port to control the movement trajectory of the radar. MATLAB implements collaborative radar acquisition through Lua scripts. The system scans 50 lines of data and finally obtains a raw data size of 400x200x256. Then, the RMA algorithm is used to calculate the imaging scene image. The imaging scheme of this system is simple to implement and has good imaging effects. It has great potential in millimeter wave high-resolution imaging direction and millimeter wave security inspection.

FCSSL: fusion enhanced contrastive self-supervised learning method for parallel MRI reconstruction

Objective. The implementation of deep learning in magnetic resonance imaging (MRI) has significantly advanced the reduction of data acquisition times. However, these techniques face substantial limitations in scenarios where acquiring fully sampled datasets is unfeasible or costly.Approach. To tackle this problem, we propose a fusion enhanced contrastive self-supervised learning (FCSSL) method for parallel MRI reconstruction, eliminating the need for fully sampledk-space training dataset and coil sensitivity maps. First, we introduce a strategy based on two pairs of re-undersampling masks within a contrastive learning framework, aimed at enhancing the representational capacity to achieve higher quality reconstruction. Subsequently, a novel adaptive fusion network, trained in a self-supervised learning manner, is designed to integrate the reconstruction results of the framework.Results. Experimental results on knee datasets under different sampling masks demonstrate that the proposed FCSSL achieves superior reconstruction performance compared to other self-supervised learning methods. Moreover,the performance of FCSSL approaches that of the supervised methods, especially under the 2DRU and RADU masks, but no need for fully sample data. The proposed FCSSL, trained under the 3× 1DRU and 2DRU masks, can effectively generalize to unseen 1D and 2D undersampling masks, respectively. For target domain data that exhibit significant differences from source domain data, the proposed model, fine-tuned with just a few dozen instances of undersampled data in the target domain, achieves reconstruction performance comparable to that achieved by the model trained with the entire set of undersampled data.Significance. The novel FCSSL model offers a viable solution for reconstructing high-quality MR images without needing fully sampled datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.

Advanced Compressive Sensing and Dynamic Sampling for 4D-STEM Imaging of Interfaces.

Interfaces in energy materials and devices often involve beam-sensitive materials such as fast ionic, soft, or liquid phases. 4D scanning transmission electron microscopy (4D-STEM) offers insights into local lattice, strain charge, and field distributions, but faces challenges in analyzing beam-sensitive interfaces at high spatial resolutions. Here, a 4D-STEM compressive sensing algorithm is introduced that significantly reduces data acquisition time and electron dose. This method autonomously allocates probe positions on interfaces and reconstructs missing information from datasets acquired via dynamic sampling. This algorithm allows for the integration of various scanning schemes and electron probe conditions to optimize data integrity. Its data reconstruction employs a neural network and an autoencoder to correlate diffraction pattern features with measured properties, significantly reducing training costs. The accuracy of the reconstructed 4D-STEM datasets is verified using a combination of explicitly and implicitly trained parameters from atomic resolution datasets. This method is broadly applicable for 4D-STEM imaging of any local features of interest and will be available on GitHub upon publication.

Mid‐Infrared Single‐Photon Compressive Spectroscopy

AbstractSensitive mid‐infrared (MIR) spectroscopy plays an indispensable role in various photon‐starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi‐pixel arrays in parallel spectral acquisition. Here, an ultra‐sensitive MIR single‐pixel spectrometer is devised and implemented, which relies on high‐fidelity spectral upconversion and wavelength‐encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped‐pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single‐element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 under an illumination flux down to 0.01 photons nm–1 pulse–1. Moreover, faithful reconstructions at sub‐Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single‐pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single‐photon level.

High-Throughput SFC-MS/MS Method to Measure EPSA and Predict Human Permeability.

Permeability is a key factor driving the absorption of orally administered drugs. In early discovery, the efficient evaluation of permeability, particularly for compounds violating Lipinski's Rule of 5, remains challenging. Addressing this, we established a high-throughput method to measure the experimental polar surface area (HT-EPSA) as an in vitro surrogate to measure permeability. Compared to earlier methods, HT-EPSA significantly reduces data acquisition time with enhanced sensitivity, selectivity, and data quality. In the effort of translating EPSA to human in vitro and in vivo passive permeability, we demonstrated the application of EPSA for predicting Caco-2 cell and human intestinal permeability, showing improvements over topological polar surface area and the parallel artificial membrane permeability assay for rank-ordering permeability in a proteolysis targeting chimera case study. The HT-EPSA method is expected to be highly beneficial in guiding early stage compound rank-ordering, faster decision-making, and in predicting in vitro and/or in vivo human intestinal permeability.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

A semi-supervised framework for computational fluid dynamics prediction

Data-driven deep learning approach heavily relies on the diversity and quantity of data. Acquiring data in the computational fluid dynamics (CFD) domain is a time and computationally intensive process. This paper proposes a semi-supervised learning method called discriminative regression fitters (DRF) for aerodynamic prediction of airfoils. DRF utilizes neural networks’ memory property to dynamically divide pseudo-labeled data into easy and difficult subsets using a model of Gaussian distribution. The method classifies unlabeled data based on loss and updates the pseudo-labeled data, improving the model’s generalization capability. Experiments on airfoil regression task datasets show that DRF achieves similar or better prediction accuracy than fully supervised approaches. It reduces data acquisition time by 70%. Ablation studies and qualitative results verify the effectiveness of DRF. The surrogate model obtained from DRF is extended to airfoil optimization, demonstrating its practicality. DRF provides a promising direction for improving the regression task while reducing the reliance on large amounts of CFD data.

High-resolution non-line-of-sight imaging based on liquid crystal planar optical elements.

Non-line-of-sight (NLOS) imaging aims at recovering hidden objects located beyond the traditional line of sight, with potential applications in areas such as security monitoring, search and rescue, and autonomous driving. Conventionally, NLOS imaging requires raster scanning of laser pulses and collecting the reflected photons from a relay wall. High-time-resolution detectors obtain the flight time of photons undergoing multiple scattering for image reconstruction. Expanding the scanning area while maintaining the sampling rate is an effective method to enhance the resolution of NLOS imaging, where an angle magnification system is commonly adopted. Compared to traditional optical components, planar optical elements such as liquid crystal, offer the advantages of high efficiency, lightweight, low cost, and ease of processing. By introducing liquid crystal with angle magnification capabilities into the NLOS imaging system, we successfully designed a large field-of-view high-resolution system for a wide scanning area and high-quality image reconstruction. Furthermore, in order to reduce the long data acquisition time, a sparse scanning method capitalizing on the correlation between measurement data to reduce the number of sampling points is thus proposed. Both the simulation and experiment results demonstrate a >20 % reduction in data acquisition time while maintaining the exact resolution.

ULM-MbCNRT: In vivo Ultrafast Ultrasound Localization Microscopy by Combining Multi-branch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascular to be rendered at sub-wavelength resolution. Nevertheless, to obtain such superior spatial resolution, it is necessary to spend tens of seconds gathering numerous ultrasound (US) frames to accumulate MB events required, resulting in ULM imaging still suffering from trade-offs between imaging quality, data acquisition time and data processing speed. In this paper, we present a new deep learning (DL) framework combining multi-branch CNN and recursive Transformer, termed as ULM-MbCNRT, that is capable of reconstructing a super-resolution image directly from a temporal mean low-resolution image generated by averaging much fewer raw US frames, i.e., implement an ultrafast ULM imaging. To evaluate the performance of ULM-MbCNRT, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-MbCNRT achieves high-quality ULM imaging with ~10-fold reduction in data acquisition time and ~130-fold reduction in computation time compared to the previous DL method (e.g., the modified sub-pixel convolutional neural network, ULM-mSPCN). For the in vivo experiments, when comparing to the ULM-mSPCN, ULM-MbCNRT allows ~37-fold reduction in data acquisition time (~0.8 s) and ~2134-fold reduction in computation time (~0.87 s) without sacrificing spatial resolution. It implies that ultrafast ULM imaging holds promise for observing rapid biological activity in vivo, potentially improving the diagnosis and monitoring of clinical conditions.

Scanning Electron Microscope Analysis of Polypropylene Filter Cartridges Used in Drinking Water Purification Systems

In recent years, more and more emphasis has been placed on the use of home filtration systems as a coarse pre-filtration step. The PP (polypropylene) filter cartridge is one of the most common of these systems, with the role of retaining solid suspensions from drinking water. However, few studies have focused on the fouling analysis of PP cartridges using EDS (Energy-Dispersive X-ray Spectroscopy) analysis methods. Through this study, a clear and in-depth view of the structures and morphology of PP filter cartridges as well as their impurity retention capacities and their impact is provided with the help of an SEM (Scanning Electron Microscope) analyzer. To achieve these goals, it was necessary to establish a specialized preparation methodology for this type of material in order to analyze it using the SEM and, at the same time, determine the optimal setting of the SEM parameters (improved resolution, reduced acceleration voltage, reduced data acquisition time, etc.) depending on the analysis performed for the visualization and detailed characterization of surfaces. Based on the SEM-EDS analysis and characterization, an uneven distribution of impurities on the surface of the PP fibers was identified. The number of impurities varied according to the depth of the cartridge due to the sieving effect that occurred owing to the varied sizes and shapes of the impurities, but also the structural differences and pore sizes of the filter material. So, the most common chemical elements identified were Al, Si, Na, Cl, Ca, Fe, and S, having a predominantly higher intensity from the inside to the outside of the PP filter cartridge due to pressure forces and the uneven flow of filtered water.

Characterising the Pelletron beam at the University of Melbourne

The Pelletron at the University of Melbourne is a DC particle accelerator that has been used for materials analysis and ion implantation since its installation in the 1970s. Today, it is primarily used to produce proton and helium beams up to 3.5 MeV and 1.5 MeV respectively, for three beamlines with a range of different experiments. The beam stability and fundamental beam parameters such as the transverse phase space distribution and emittance have not been previously investigated in detail. We have performed systematic measurements to determine the beam current variation, finding persistent fluctuations: our study determined that internal wire scanners used for monitoring the beam profile contribute to a periodic current drop. We also found that large terminal voltage oscillations reduce the average current. In addition, the phase space has been measured using a custom-built slit-grid apparatus. We found that the Courant–Snyder (Twiss) parameters are a function of the operational characteristics of the Pelletron, with the emittance more than doubling between measurements. To parametrise the beam despite the fluctuations, we introduce a ‘minimum bounding ellipse’ described by a set of Courant–Snyder parameters that is representative of the beam characteristics for all the measurements: for our data, this has β= 8.2(5)m, α=-5.2(3), and an emittance of 0.34(3)mmmrad. These parameters will enable future beamline design in Melbourne, and give an indication for the requirements at other Pelletron facilities. Our characterisation techniques can provide a low-cost method to probe beam parameters for improvements in the operation and efficiency of similar DC accelerators, allowing for more accurate measurements and reduced data acquisition times.

Spectroscopy of helium-tagged molecular ions-Development of a novel experimental setup.

In this contribution, we present an efficient and alternative method to the commonly used RF-multipole trap technique to produce He-tagged molecular ions at cryogenic temperatures, which are perfectly suitable for messenger spectroscopy. The seeding of dopant ions in multiply charged helium nanodroplets, in combination with a gentle extraction of the latter from the helium matrix, enables the efficient production of He-tagged ion species. With a quadrupole mass filter, a specific ion of interest is selected, merged with a laser beam, and the photoproducts are measured in a time-of-flight mass-spectrometer. The detection of the photofragment signal from a basically zero background is much more sensitive than the depletion of the same amount of signal from precursor ions, delivering high quality spectra at reduced data acquisition times. Proof-of-principle measurements of bare and He-tagged Ar-cluster ions, as well as of He-tagged C60 ions, are presented.

Rapid Scan Electron Paramagnetic Resonance Spectroscopy Is a Suitable Tool to Study Intermolecular Interactions of Intrinsically Disordered Protein.

Intrinsically disordered proteins (IDPs) are involved in most crucial cellular processes. However, they lack a well-defined fold hampering the investigation of their structural ensemble and interactions. Suitable biophysical methods able to manage their inherent flexibility and broad conformational ensemble are scarce. Here, we used rapid scan (RS) electron paramagnetic resonance (EPR) spectroscopy to study the intermolecular interactions of the IDP α-synuclein (aS). aS aggregation and fibril deposition is the hallmark of Parkinson's disease, and specific point mutations, among them A30P and A53T, were linked to the early onset of the disease. To understand the pathological processes, research intensively investigates aS aggregation kinetics, which was reported to be accelerated in the presence of ethanol. Conventional techniques fail to capture these fast processes due to their limited time resolution and, thus, lose kinetic information. We have demonstrated that RS EPR spectroscopy is suitable for studying aS aggregation by resolving underlying kinetics and highlighting differences in fibrillization behavior. RS EPR spectroscopy outperforms traditional EPR methods in terms of sensitivity by a factor of 5 in our case while significantly reducing data acquisition time. Thus, we were able to sample short time intervals capturing single events taking place during the aggregation process. Further studies will therefore be able to shed light on biological processes proceeding on fast time scales.

Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL)-based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data ( L frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging l -frame raw US images with l being significantly smaller than L . To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows ∼40 -fold reduction in data acquisition time and ∼61 -fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time ( ∼0.33 s) and ultrafast data processing speed ( ∼0.60 s) that makes it promising to observe rapid biological activities in vivo.

High-Sensitivity Visualization of Ultrafast Carrier Diffusion by Wide-Field Holographic Microscopy

Ultrafast transient microscopy is a key tool to study the photophysical properties of materials in space and time, but current implementations are limited to ≈1-μm fields of view, offering no statistical information for heterogeneous samples. Recently, we demonstrated wide-field transient imaging based on multiplexed off-axis holography. Here, we perform ultrafast microscopy in parallel around a hundred diffraction-limited excitation spots over a ≈60-μm field of view, which not only automatically samples the photophysical heterogeneity of the sample over a large area but can also be used to obtain a 10-fold increase in signal-to-noise ratio by computing an average spot. We apply our microscope to study the carrier diffusion processes in methylammonium lead bromide perovskites. We observe strong diffusion due to the presence of hot carriers during the first picosecond and slower diffusion afterward. We also describe how many-body kinetics can be misleadingly interpreted as strong diffusion at high excitation densities, while at weak excitation, real diffusion is observed. Therefore, the vast increase in sensitivity offered by this technique benefits the study of carrier transport not only by reducing data acquisition times but also by enabling the measurement of the much smaller signals generated at low carrier densities.

Sub-10 second fly-scan nano-tomography using machine learning

X-ray computed tomography is a versatile technique for 3D structure characterization. However, conventional reconstruction algorithms require that the sample not change throughout the scan, and the timescale of sample dynamics must be longer than the data acquisition time to fulfill the stable sample requirement. Meanwhile, concerns about X-ray-induced parasite reaction and sample damage have driven research efforts to reduce beam dosage. Here, we report a machine-learning-based image processing method that can significantly reduce data acquisition time and X-ray dose, outperforming conventional approaches like Filtered-Back Projection, maximum-likelihood, and model-based maximum-a-posteriori probability. Applying machine learning, we achieve ultrafast nano-tomography with sub-10 second data acquisition time and sub-50 nm pixel resolution in a transmission X-ray microscope. We apply our algorithm to study dynamic morphology changes in a lithium-ion battery cathode under a heating rate of 50 oC min−1, revealing crack self-healing during thermal annealing. The proposed method can be applied to various tomography modalities.

Simultaneous non-convex low rank regularization for fast magnetic resonance spectroscopy reconstruction

2D magnetic resonance spectroscopy (MRS) is extensively used to analyze the components, structures, and interactions of substances in chemistry and bioengineering. To reduce data acquisition time, the spatiotemporally encoded ultrafast (STEU) MRS employs a fast means to acquire data in the hybrid time and frequency (HTF) plane, but relies on non-uniform sampling (NUS) technique. After that, a proper reconstruction method is essential to recover a high quality 2D MRS from undersampled HTF data. In this work, each column and row of 2D MRS are converted into Hankel matrices, by which a simultaneous low rank regularization model is proposed to exploit the bivariate exponential structure of 2D MRS signal. Additionally, a non-convex surrogate function for rank is integrated in the proposed model to more precisely enforce the low rank property of Hankel matrices. To ensure computational accuracy and convergence, an efficient numerical algorithm is further deduced based on alternating direction method of multipliers (ADMM) iteration. The experimental results have shown that the proposed method significantly outperforms existing 2D MRS reconstruction methods, and presents a strong robustness to low sampling rates, various sampling patterns, and measurement noise with a favorable complexity.