Reduce Data Acquisition Time Research Articles

Super high-quality SEM/FIB imaging of dentine structures without collagen fiber loss through a metal staining process

Scanning Electron Microscope/Focused Ion Beam (SEM/FIB) system has become valuable and popular tool for the analysis of biological materials such as dentine structures. According to physiological and anatomical studies, dentine structures are a complicated system containing collagen fibers, nanocrystalline hydroxyapatite, and numerous networks of tubular pores. During a routine FIB milling process, collagen fibers and other organic structures are vaporized, which increases the number of pores on the milled surface of the dentine. This causes the final cross-section to be more porous than the pristine sample. Unfortunately, little attention has been paid to the collagen fiber loss and how to preserve them during a FIB milling process. In this work, we present a novel and simple approach to preserve the organic portions of the dentine structure through metal staining. By using this method, the porosity of the dentine structure after the FIB milling process is significantly reduced similar to the pristine sample. This indicates that the organic portion of the dentine structure is well protected by the metal staining. This approach enables the SEM/FIB system to generate super-high quality SEM images with less ion beam damage; and the SEM images can better reflect the original condition of the dentine structure. Further, serial energy-dispersive X-ray spectroscopy (EDS) mapping of the stained dentine structure is achieved without an additional metal coating; and three-dimensional (3-D) elemental mapping of an occluded dentine is achieved with a significantly reduced data acquisition time.

Lock-in detector for accelerated nonlinear imaging.

We show that accelerated nonlinear imaging, such as stimulated Raman scattering and pump-probe imaging, is enabled by an order of magnitude reduction of data acquisition time when replacing the exponentially-weighted-moving-average low-pass filter in a lock-in amplifier with a simple-moving-average filter. We show that this simple-moving-average (box) lock-in yields a superior signal-to-noise ratio and suppression of extraneous modulations with short pixel dwell times, if one condition for the relation between the lock-in time constant and modulation frequencies is met. Our results, both theoretical and experimental, indicate that for nonlinear imaging applications, the box lock-in significantly outperforms conventional lock-in detection. These results facilitate the application of ultrafast and nonlinear imaging as a new standard for material characterization.

Measuring dimensionality and purity of high-dimensional entangled states

High-dimensional entangled states are promising candidates for increasing the security and encoding capacity of quantum systems. While it is possible to witness and set bounds for the entanglement, precisely quantifying the dimensionality and purity in a fast and accurate manner remains an open challenge. Here, we report an approach that simultaneously returns the dimensionality and purity of high-dimensional entangled states by simple projective measurements. We show that the outcome of a conditional measurement returns a visibility that scales monotonically with state dimensionality and purity, allowing for quantitative measurements for general photonic quantum systems. We illustrate our method using two separate bases, the orbital angular momentum and pixels bases, and quantify the state dimensionality by a variety of definitions over a wide range of noise levels, highlighting its usefulness in practical situations. Importantly, the number of measurements needed in our approach scale linearly with dimensions, reducing data acquisition time significantly. Our technique provides a simple, fast and direct measurement approach.

Fluorescence based rapid optical volume screening system (OVSS) for interrogating multicellular organisms

Continuous monitoring of large specimens for long durations requires fast volume imaging. This is essential for understanding the processes occurring during the developmental stages of multicellular organisms. One of the key obstacles of fluorescence based prolonged monitoring and data collection is photobleaching. To capture the biological processes and simultaneously overcome the effect of bleaching, we developed single- and multi-color lightsheet based OVSS imaging technique that enables rapid screening of multiple tissues in an organism. Our approach based on OVSS imaging employs quantized step rotation of the specimen to record 2D angular data that reduces data acquisition time when compared to the existing light sheet imaging system (SPIM). A co-planar multicolor light sheet PSF is introduced to illuminate the tissues labelled with spectrally-separated fluorescent probes. The detection is carried out using a dual-channel sub-system that can simultaneously record spectrally separate volume stacks of the target organ. Arduino-based control systems were employed to automatize and control the volume data acquisition process. To illustrate the advantages of our approach, we have noninvasively imaged the Drosophila larvae and Zebrafish embryo. Dynamic studies of multiple organs (muscle and yolk-sac) in Zebrafish for a prolonged duration (5 days) were carried out to understand muscle structuring (Dystrophin, microfibers), primitive Macrophages (in yolk-sac) and inter-dependent lipid and protein-based metabolism. The volume-based study, intensity line-plots and inter-dependence ratio analysis allowed us to understand the transition from lipid-based metabolism to protein-based metabolism during early development (Pharyngula period with a critical transition time, tau _c = 50 h post-fertilization) in Zebrafish. The advantage of multicolor lightsheet illumination, fast volume scanning, simultaneous visualization of multiple organs and an order-less photobleaching makes OVSS imaging the system of choice for rapid monitoring and real-time assessment of macroscopic biological organisms with microscopic resolution.

Chaotic Compressive Spectrum Sensing Based on Chebyshev Map for Cognitive Radio Networks

Recently, the chaotic compressive sensing paradigm has been widely used in many areas, due to its ability to reduce data acquisition time with high security. For cognitive radio networks (CRNs), this mechanism aims at detecting the spectrum holes based on few measurements taken from the original sparse signal. To ensure a high performance of the acquisition and recovery process, the choice of a suitable sensing matrix and the appropriate recovery algorithm should be done carefully. In this paper, a new chaotic compressive spectrum sensing (CSS) solution is proposed for cooperative CRNs based on the Chebyshev sensing matrix and the Bayesian recovery via Laplace prior. The chaotic sensing matrix is used first to acquire and compress the high-dimensional signal, which can be an interesting topic to be published in symmetry journal, especially in the data-compression subsection. Moreover, this type of matrix provides reliable and secure spectrum detection as opposed to random sensing matrix, since any small change in the initial parameters generates a different sensing matrix. For the recovery process, unlike the convex and greedy algorithms, Bayesian models are fast, require less measurement, and deal with uncertainty. Numerical simulations prove that the proposed combination is highly efficient, since the Bayesian algorithm with the Chebyshev sensing matrix provides superior performances, with compressive measurements. Technically, this number can be reduced to 20% of the length and still provides a substantial performance.

Accelerating Ultrafast Spectroscopy with Compressive Sensing

Ultrafast spectroscopy is an important tool for studying photoinduced dynamical processes in atoms, molecules, and nanostructures. Typically, the time to perform these experiments ranges from several minutes to hours depending on the choice of spectroscopic method. It is desirable to reduce this time overhead to not only to shorten time and laboratory resources, but also to make it possible to examine fragile specimens which quickly degrade during long experiments. In this article, we motivate using compressive sensing to significantly shorten data acquisition time by reducing the total number of measurements in ultrafast spectroscopy. We apply this technique to experimental data from ultrafast transient absorption spectroscopy and ultrafast terahertz spectroscopy and show that good estimates can be obtained with as low as 15% of the total measurements, implying a 6-fold reduction in data acquisition time.

Fast Diffusion Kurtosis Mapping of Human Brain at 7 Tesla With Hybrid Principal Component Analyses

Diffusion kurtosis has become an important magnetic resonance imaging (MRI) modality for non-invasively mapping the microstructural variations in living tissues. Theoretically, the spatial resolution of diffusion kurtosis imaging (DKI) can be significantly improved by acquiring data at ultra-high magnetic fields (UHF, ≤ 7 Tesla) because of the increased signal-to-noise ratios. However, issues such as increased susceptibility artefacts and rapid signal attenuation inherent in UHF-MRI have impeded the adoption of DKI in research and clinics. In this paper, we developed a new image reconstruction algorithm for fast DKI at UHF. By integrating the one-dimensional and two-dimensional principal component analysis and compressed sensing technologies, the new algorithm can reconstruct kurtosis maps from highly undersampled data. The technique was validated using randomly undersampled brain images with a control database of fully sampled DKI acquisitions from healthy human participants. We compared the technique with zero-filling Fourier transform and similar compressed sensing algorithms by evaluating the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between images and assessing the reproducibility of results using the Bland-Altman method. We found that our methods can achieve at least a five-fold reduction in data acquisition time at UHF with high image quality. Moreover, the PSNR and SSIM of five diffusion metrics generated by our methods were superior to the other algorithms when the undersampling rate is high and the echo time is short. The proposed method can be valuable for fast functional and dynamic-contrast imaging techniques at 7 Tesla or higher.

Simultaneous δ2 H and δ18 O analyses of water inclusions in halite with off-axis integrated cavity output spectroscopy.

Stable isotope compositions of ancient halite fluid inclusions have been recognized to be valuable tools for reconstructing past environments. Nevertheless, in order to better understand the genesis of halite deposits, it could be of great interest to combine both δ2 H and δ18 O measurements of the water trapped as inclusions in the defects of the mineral lattice. We developed a method combining off-axis integrated cavity output spectroscopy (OA-ICOS) connected on line with a modified elemental analyzer (EA-OA-ICOS) to perform those measurements. The first step was to test the method with synthetic halite crystals precipitated in the laboratory from isotopically calibrated waters. Water isotopic signatures have been measured with conventional techniques, equilibration for δ18 O and chromium reduction for δ2 H. Then, we modified and optimized a conventional EA to connect it online with an OA-ICOS instrument for H2 O measurements. The technique is first evaluated for calibrated free water samples. The technique is also evaluated for salt matrix effect, accuracy, and linearity for both isotopic signatures. Then, the technique is used to measure simultaneously δ2 H and δ18 O values of halite water inclusions precipitated from the evaporation experiments. Data generated with this new technique appeared to be comparable with those inferred from prior off-line technique studies. The advantages offered by the OA-ICOS technique are the simultaneous acquisition of both isotopic ratios and the substantial reduction of data acquisition time and sample aliquot size. Natural halite samples have been analyzed with this method. Natural halite samples as old as Precambrian have also been analyzed with this method.

Fast compressive lens-free tomography for 3D biological cell culture imaging.

We present a compressive lens-free technique that performs tomographic imaging across a cubic millimeter-scale volume from highly sparse data. Compared with existing lens-free 3D microscopy systems, our method requires an order of magnitude fewer multi-angle illuminations for tomographic reconstruction, leading to a compact, cost-effective and scanning-free setup with a reduced data acquisition time to enable high-throughput 3D imaging of dynamic biological processes. We apply a fast proximal gradient algorithm with composite regularization to address the ill-posed tomographic inverse problem. Using simulated data, we show that the proposed method can achieve a reconstruction speed ∼10× faster than the state-of-the-art inverse problem approach in 3D lens-free microscopy. We experimentally validate the effectiveness of our method by imaging a resolution test chart and polystyrene beads, demonstrating its capability to resolve micron-size features in both lateral and axial directions. Furthermore, tomographic reconstruction results of neuronspheres and intestinal organoids reveal the potential of this 3D imaging technique for high-resolution and high-throughput biological applications.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI

Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.

X-ray scattering tensor tomography with circular gratings

Three dimensional (3D) information of the microstructure organization of various relevant materials in industry and nature is fundamental to master the understanding of their macroscopic properties. X-ray scattering tensor tomography provides 3D directional information on unresolved microstructures in large volumes, facilitating the investigation of the microstructural organization in statistically large enough sample portions. However, established acquisition protocols such as scanning small angle x-ray scattering and x-ray grating interferometry require long measurement time. In this Letter, a rapid x-ray scattering tensor tomography acquisition method is proposed. It is based on circular gratings, which provide single-shot 2D-omnidirectional information of the sample scattering properties. The main advantage of the presented method is the reduced data acquisition time compared to the existing protocols for x-ray scattering tensor tomography, paving the way toward rapid time-resolved studies.

Application of metasurface-based low-pass filters for improving THz-TDS characteristics

We propose an approach to improve the technical characteristics of terahertz time-domain spectrometers at low-frequency measurements. The approach is based on applying low-pass THz filters to narrow the frequency band of the THz signal that allows increasing the sampling interval in accordance with the Nyquist–Shannon theorem. This concept was verified by studying the transmission spectra of low-frequency band-pass THz filters centered at 156 and 376 GHz. We confirm that the high-quality low-pass filters can improve accuracy of THz measurements and significantly reduce data acquisition time. The reduction up to 12 times was experimentally demonstrated in our case.

Submillimeter MR fingerprinting using deep learning-based tissue quantification.

To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach. A rapid and high-resolution MR fingerprinting technique was developed for brain T1 and T2 quantification. The 2D acquisition was performed using a FISP-based MR fingerprinting sequence and a spiral trajectory with 0.8-mm in-plane resolution. A deep learning-based method was used to replace the standard template matching method for improved tissue characterization. A novel network architecture (i.e., residual channel attention U-Net) was proposed to improve high-resolution details in the estimated tissue maps. Quantitative brain imaging was performed with 5 adults and 2 pediatric subjects, and the performance of the proposed approach was compared with several existing methods in the literature. In vivo measurements with both adult and pediatric subjects show that high-quality T1 and T2 mapping with 0.8-mm in-plane resolution can be achieved in 7.5 seconds per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared with the standard U-Net, high-resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. Experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside reduced data acquisition time, a 5-fold acceleration in tissue property mapping was also achieved with the proposed method. A rapid and high-resolution MR fingerprinting technique was developed, which enables high-quality T1 and T2 quantification with 0.8-mm in-plane resolution in 7.5 seconds per slice.

A Wireless Geophone Network Architecture Using IEEE 802.11af With Power Saving Schemes

Seismic surveys are conducted by oil exploration companies to keep up with the global demand for oil and gas. Although seismic cables offer fast and reliable data transfer, they account for a majority of the equipment weight, logistics, and labor costs. A novel wireless geophone network architecture, compliant with the IEEE 802.11af standard, is described. Operation in television white spaces can achieve long transmission ranges, allowing for scalable coverage of large seismic survey areas. Two methods for data collection are proposed: a Geophone-Polling (GP) scheme and an Adaptive Geophone Time Division Multiple Access Scheduling (AGTS) scheme. The two schemes are analyzed and evaluated in terms of the total time taken for data acquisition and the average power consumption, in comparison to the default IEEE 802.11 channel access schemes. The problem of hexagonal clustering for orthogonal deployment of geophones is also tackled, and the impact of co-channel interference is considered. The proposed schemes not only reduce data acquisition time, but also the average power consumption of the geophones, thereby improving the lifetime of wireless seismic surveys.

Методы анализа данных объёмной компьютерной томографии турбинных лопаток

The use of computed tomography slices covering the entire volume of a component has been critical to the advancement of innovative aerospace technologies. Inspection times for these components were often measured in hours, as data were acquired one single computed tomography slice at a time. 
 Recent advancements in computed tomography data acquisition technology have moved from the use of linear X-ray detectors to area X-ray detectors that can collect computed tomography data from a component in a single acquisition. The use of area X-ray detectors to collect computed tomography data is often referred to as volumetric computed tomography (VCT) and has reduced data acquisition times for an entire component from hours to minutes. The reduced inspection time provided by VCT has made it practicable to use this technique for the production inspection of advanced components. In order to realize the use of VCT in a production environment, the data produced by the inspection must be analyzed within a time comparable to the time taken by the data acquisition. This paper describes a technique for rapidly analyzing VCT inspection data and the application of that technique to the inspection of turbine airfoils.

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR

The coupling of atomic force microscopy with infrared spectroscopy (AFM-IR) offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution. However, in order to fully utilize the measurement capability of AFM-IR, a three-dimensional dataset (2D map with a spectroscopic dimension) needs to be acquired, which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan. In this paper, we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm. This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image, greatly reducing data acquisition time. As a result, we are able to generate high-resolution maps of component distribution, produce chemical maps at any wavenumber available in the spectral range, and perform correlative analysis of the physical and chemical properties of the samples. We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition. We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.

Protein and RNA dynamical fingerprinting

Protein structural vibrations impact biology by steering the structure to functional intermediate states; enhancing tunneling events; and optimizing energy transfer. Strong water absorption and a broad continuous vibrational density of states have prevented optical identification of these vibrations. Recently spectroscopic signatures that change with functional state were measured using anisotropic terahertz microscopy. The technique however has complex sample positioning requirements and long measurement times, limiting access for the biomolecular community. Here we demonstrate that a simplified system increases spectroscopic structure to dynamically fingerprint biomacromolecules with a factor of 6 reduction in data acquisition time. Using this technique, polarization varying anisotropy terahertz microscopy, we show sensitivity to inhibitor binding and unique vibrational spectra for several proteins and an RNA G-quadruplex. The technique’s sensitivity to anisotropic absorbance and birefringence provides rapid assessment of macromolecular dynamics that impact biology.

Technical Note: Single-shot phase retrieval method for synchrotron-based high-energy x-ray grating interferometry.

X-ray grating interferometry (XGI) provides substantially increased contrast over conventional absorption-based imaging methods and therefore shows great potential for future biomedical applications. In this work, we propose a single-shot phase retrieval method for synchrotron-based high-energy x-ray grating interferometry. Contrary to existing retrieval methods, the presented novel approach enables direct retrieval of the object's phase map quantitatively from a single projection image, thus significantly simplifying the experimental procedure and reducing data acquisition times. The phase retrieval method is analytically derived, based on the phase-attenuation duality of soft tissues when being imaged with high-energy x rays. The sensitivity of the retrieved phase map, quantified by the standard deviation, is evaluated as a function of the photon number. Numerical experiments are performed to validate the proposed method and provide some quantitative insight. The numerical results show that the method can provide high-quality phase images, where the well-known streak artifacts are significantly suppressed. Moreover, the retrieved phase maps confirm that the method is highly stable with respect to statistical noise. Thanks to simplified experimental procedure and reduced acquisition time and dose deposition to the sample, we believe that this new method can find its potential in biomedical imaging and invivo studies. Future work will focus on the adaptation of the method to polychromatic x ray from tube source and to computed tomography.

Partial fourier shells trajectory for non-cartesian MRI

Non-Cartesian MRI acquisition has demonstrated various advantages in many clinical applications. The shells trajectory is a 3D non-Cartesian MRI acquisition technique that samples the k-space using a series of concentric shells to achieve efficient 3D isotropic acquisition. Partial Fourier acquisition is an acceleration technique that is widely used in Cartesian MRI. It exploits the conjugate symmetry of k-space measurement to reduce the number of k-space samples compared to full-k-space acquisition, without loss of spatial resolution. For a Cartesian MRI acquisition, the direction of partial Fourier acceleration is aligned either with the phase encoded or frequency encoded direction. In those cases, the underlying image matrix can be reconstructed from the undersampled k-space data using a non-iterative, homodyne reconstruction framework. However, designing a non-Cartesian acquisition trajectory that is compatible with non-iterative homodyne reconstruction is not nearly as straightforward as in the Cartesian case. One reason is the non-iterative homodyne reconstruction requires (slightly over) half of the k-space to be fully sampled. Since the direction of partial Fourier acceleration varies throughout the acquisition in the non-Cartesian trajectory, directly applying the same partial Fourier acquisition pattern (as in Cartesian acquisitions) to a non-Cartesian trajectory does not necessarily yield a continuous, physically-achievable trajectory. In this work, we develop an asymmetric shells trajectory with fully-automated trajectory and gradient waveform design to achieve partial Fourier acquisition for the shells trajectory. We then demonstrate a non-iterative image reconstruction framework for the proposed trajectory. Phantom and in vivo brain scans based on spoiled gradient echo (SPGR) shells and magnetization-prepared shells (MP-shells) were performed to test the proposed trajectory design and reconstruction method. Our phantom and in vivo results demonstrate that the proposed partial Fourier shells trajectory maintains the desirable image contrast and high sampling efficiency from the fully sampled shells, while further reducing data acquisition time.

Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities.

This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences. We previously demonstrated that the Bayesian formalism has many advantages, amongst which are compensation of false coincidences, no overestimation of pump-only contributions, significantly increased signal-to-noise ratio, and applicability to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, our approach allows running experiments at higher ionization rates, resulting in an appreciable reduction of data acquisition times. In addition to our previous paper, we include fluctuating laser intensities, of which the straightforward implementation highlights yet another advantage of the Bayesian formalism. Our method is thoroughly scrutinized by challenging mock data, where we find a minor impact of laser fluctuations on false coincidences, yet a noteworthy influence on background subtraction. We apply our algorithm to data obtained in experiments and discuss the impact of laser fluctuations on the data analysis.