Reduce Data Acquisition Time Research Articles

Fast Data Acquisition in Heavy Ion CT Using Intensifying Screen—EMCCD Camera System With Beam Intensity Monitor

We investigated the feasibility of fast data acquisition in heavy ion CT (IonCT) technique with an X-ray intensifying screen-charged coupled device (CCD) camera system. This technique is based on measuring the residual range distribution of heavy ions after passing through an object. We took a large number of images with a CCD camera for one projection by changing the range shifter (RS) thickness to obtain a characteristic curve similar to a Bragg curve and then to estimate the relative residual range. We used a high quality Electron Multiplying CCD (EMCCD) camera, which drastically reduced data acquisition time. We also used a parallel-plate ionization chamber upstream of an object to monitor the time variation in heavy ion beam intensity from a synchrotron accelerator and to perform beam intensity correction for all EMCCD images. Experiments were conducted using a broad beam of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> C, which was generated by spreading out the pencil beam accelerated up to 400 MeV/u by the Heavy Ion Medical Accelerator, in Chiba (HIMAC) at the National Institute of Radiological Sciences, with a scatterer. We demonstrated that a fast CT data acquisition, 14 min for 256 projections, is possible for an electron density phantom, consisting of six rods with a relative electron density resolution of 0.017, using the proposed technique with HIMAC.

Giant amplification of spin dependent recombination at heterojunctions through a gate controlled bipolar effect

We demonstrate a method for spin dependent recombination (SDR) in metal-oxide-semiconductor-field-effect-transistors which (i) greatly amplifies the spin dependent fraction of the investigated transistor current and (ii) concentrates the sensitivity of the measurement exclusively to the most technologically relevant defects, those at the semiconductor/oxide interface. We demonstrate a gain in sensitivity well in excess of an order of magnitude and a much better resolved spectrum. The boost in sensitivity reduces data acquisition time by orders of magnitude and significantly enhances the analytical power of SDR. The very large amplification effect may also be of interest for magnetic resonance controlled spintronic transistors.

The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) model for reconstruction and observing statistical properties of reconstruction operators

The acquisition of sub-sampled data from an array of receiver coils has become a common means of reducing data acquisition time in MRI. Of the various techniques used in parallel MRI, SENSitivity Encoding (SENSE) is one of the most common, making use of a complex-valued weighted least squares estimation to unfold the aliased images. It was recently shown in Bruce et al. [Magn. Reson. Imag. 29(2011):1267-1287] that when the SENSE model is represented in terms of a real-valued isomorphism,it assumes a skew-symmetric covariance between receiver coils, as well as an identity covariance structure between voxels. In this manuscript, we show that not only is the skew-symmetric coil covariance unlike that of real data, but the estimated covariance structure between voxels over a time series of experimental data is not an identity matrix. As such, a new model, entitled SENSE-ITIVE, is described with both revised coil and voxel covariance structures. Both the SENSE and SENSE-ITIVE models are represented in terms of real-valued isomorphisms, allowing for a statistical analysis of reconstructed voxel means, variances, and correlations resulting from the use of different coil and voxel covariance structures used in the reconstruction processes to be conducted. It is shown through both theoretical and experimental illustrations that the miss-specification of the coil and voxel covariance structures in the SENSE model results in a lower standard deviation in each voxel of the reconstructed images, and thus an artificial increase in SNR, compared to the standard deviation and SNR of the SENSE-ITIVE model where both the coil and voxel covariances are appropriately accounted for. It is also shown that there are differences in the correlations induced by the reconstruction operations of both models, and consequently there are differences in the correlations estimated throughout the course of reconstructed time series. These differences in correlations could result in meaningful differences in interpretation of results.

Undersampled MRI reconstruction with patch-based directional wavelets

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.

Using learned under-sampling pattern for increasing speed of cardiac cine MRI based on compressive sensing principles

Abstract This article presents a compressive sensing approach for reducing data acquisition time in cardiac cine magnetic resonance imaging (MRI). In cardiac cine MRI, several images are acquired throughout the cardiac cycle, each of which is reconstructed from the raw data acquired in the Fourier transform domain, traditionally called k-space. In the proposed approach, a majority, e.g., 62.5%, of the k-space lines (trajectories) are acquired at the odd time points and a minority, e.g., 37.5%, of the k-space lines are acquired at the even time points of the cardiac cycle. Optimal data acquisition at the even time points is learned from the data acquired at the odd time points. To this end, statistical features of the k-space data at the odd time points are clustered by fuzzy c-means and the results are considered as the states of Markov chains. The resulting data is used to train hidden Markov models and find their transition matrices. Then, the trajectories corresponding to transition matrices far from an identity matrix are selected for data acquisition. At the end, an iterative thresholding algorithm is used to reconstruct the images from the under-sampled k-space datasets. The proposed approaches for selecting the k-space trajectories and reconstructing the images generate more accurate images compared to alternative methods. The proposed under-sampling approach achieves an acceleration factor of 2 for cardiac cine MRI.

Increasing the Sensitivity of Magnetic Resonance Spectroscopy and Imaging

Nuclear magnetic resonance and magnetic resonance imaging are two of the most important techniques in analytical chemistry and noninvasive medical imaging, respectively. They share a common physical basis, one aspect of which is a low intrinsic sensitivity relative to complementary techniques. Encouragingly, recent advances in physics, chemistry, engineering, and data processing have enabled significant increases in sensitivity, as measured by both increased signal-to-noise and reduced data acquisition times, allowing previously unattainable data to be acquired and also new types of experiments to be designed.

A statistical examination of SENSE image reconstruction via an isomorphism representation

In magnetic resonance imaging, the parallel acquisition of subsampled spatial frequencies from an array of multiple receiver coils has become a common means of reducing data acquisition time. SENSitivity Encoding (SENSE) is a popular parallel image reconstruction model that uses a complex-valued least squares estimation process to unfold aliased images. In this article, the linear mathematical framework derived in Rowe et al. [ J Neurosci Meth 159 (2007) 361–369] is built upon to perform image reconstruction with subsampled data acquired from multiple receiver coils, where the SENSE model is represented as a real-valued isomorphism. A statistical analysis is performed of the various image reconstruction operators utilized in the SENSE model, with an emphasis placed on the effects of each operator on voxel means, variances and correlations. It is shown that, despite the attractiveness of models that unfold the aliased images from subsampled data, there is an artificial correlation induced between reconstructed voxels from the different folds of aliased images. As such, the mathematical framework outlined in this manuscript could be further developed to provide a means of accounting for this unavoidable correlation induced by image reconstruction operators.

Chemical vapor deposition and composition–depth profiling of a graded coating by Auger electron spectroscopy used in conjunction with taper polishing

A graded TiCxN1−x coating with x varying parabolically in the range 0–1 was grown by chemical vapor deposition in the TiCL4–CH4–N2–H2 system by quasi-continuously changing process parameters (growth time, CH4 mole fraction) at 1400K and at 10.7kPa. Taper polishing of the graded coating was carried out by a simple apparatus converting a depth dimension into a lateral dimension. Auger electron spectroscopy line scan was then performed on the taper polished surface. Composition profile of the graded coating was determined to be in agreement with the designed one using surface profilometer traces and AES signals from CKLL transition at 272eV. AES/taper polishing approach facilitates composition–depth analysis with a minimal degree of sputtering and a substantial reduction of data acquisition time. Taper polishing could also be combined with other chemical analysis techniques such as X-ray microdiffraction.

Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing

We present a method for accelerating the acquisition of phase-encoded velocity images by the use of compressed sensing (CS), a technique that exploits the observation that an under-sampled signal can be accurately reconstructed by utilising the prior knowledge that it is sparse or compressible. We present results of both simulated and experimental measurements of liquid flow through a packed bed of spherical glass beads. For this system, the best image reconstruction used a spatial finite-differences transform. The reconstruction was further improved by utilising prior knowledge of the liquid distribution within the image. Using this approach, we demonstrate that for a sampling fraction of ∼30% of the full k-space data set, the velocity can be recovered with a relative error of 11%, which is below the visually detectable limit. Furthermore, the error in the total flow measured using the CS reconstruction is <3% for sampling fractions ⩾30%. Thus, quantitative velocity images were obtained in a third of the acquisition time required using conventional imaging. The reduction in data acquisition time can also be exploited in acquiring images at a higher spatial resolution, which increases the accuracy of the measurements by reducing errors arising from partial volume effects. To illustrate this, the CS algorithm was used to reconstruct gas-phase velocity images at a spatial resolution of 230μm×230μm. Images at this spatial resolution are prohibitively time-consuming to acquire using full k-space sampling techniques.

13C solid-state NMR chromatography by magic angle spinning 1H T1 relaxation ordered spectroscopy

An efficient method to separate the (13)C NMR spectra of solid mixtures is introduced. The (1)H longitudinal (T(1)) relaxation time is used to separate the overlapping (13)C chemical shift spectra of solid mixtures via an inverse Laplace transform (ILT) of the relaxation dimension. The resulting 2D spectrum of the mixture contains separate (13)C spectra for each component of the mixture that are identical to (13)C spectra of the isolated materials. The separation is based on the equalization of (1)H T(1) values in a single domain by rapid (1)H spin diffusion and on the (1)H T(1) value differences between different domains. The introduction of a general ILT scheme enables efficient and reduced data acquisition time. The method is demonstrated on a mixture of two disaccharides and on a commercial drug containing several compounds.

EPR oximetry in three spatial dimensions using sparse spin distribution

A method is presented to use continuous wave electron paramagnetic resonance imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Information encoding location and spectral linewidth is collected by varying the spatial orientation and strength of an applied magnetic gradient field. Data processing exploits the spatial sparseness of spins to detect voxels with nonzero spin and to estimate the spectral linewidth for those voxels. The parsimonious representation of spin locations and linewidths permits an order of magnitude reduction in data acquisition time, compared to four-dimensional tomographic reconstruction using traditional spectral-spatial imaging. The proposed oximetry method is experimentally demonstrated for a lithium octa- n-butoxy naphthalocyanine (LiNc–BuO) probe using an L-band EPR spectrometer.

Radiofrequency coils for magnetic resonance microscopy

Given the several orders of magnitude fewer spins per voxel for MR microscopy than for conventional MRI, efficient coil design is important to obtain sufficient signal-to-noise within reasonable data acquisition times. As MR microscopy is typically performed using very high magnetic fields, coil design must also incorporate the effects of increased component losses and skin-depth-dependent resistance, as well as radiation losses and phase effects for coils when conductor dimensions constitute a substantial fraction of the electromagnetic wavelength. For samples much less than 1 mm in size, wire solenoids or microfabricated planar coils are used. For samples with diameters of several millimeters, saddle, birdcage, Alderman-Grant or millipede coils become the preferred choice. Recent advances in multiple-coil probes and phased arrays have been used to reduce data acquisition time and/or increase sample throughput, and small superconducting coils have shown significant improvements in signal-to-noise over equivalently sized room-temperature coils.

Beam Multiplexing Using the Phased-Array Weather Radar

Abstract The recently installed S-band phased-array radar (PAR) at the National Weather Radar Testbed (NWRT) offers fast and flexible beam steering through electronic beam forming. This capability allows the implementation of a novel scanning strategy termed beam multiplexing (BMX), with the goal of providing fast updates of weather information with high statistical accuracy. For conventional weather radar the data acquisition time for a sector scan or a volume coverage pattern (VCP) can be reduced by increasing the antenna’s rotation rate to the extent that the pedestal allows. However, statistical errors of the spectral moment estimates will increase due to the fewer samples that are available for the estimation. BMX is developed to exploit the idea of collecting independent samples and maximizing the usage of radar resources. An improvement factor is introduced to quantify the BMX performance, which is defined by the reduction in data acquisition time using BMX when the same data accuracy obtained by a conventional scanning strategy is maintained. It is shown theoretically that a fast update without compromising data quality can be achieved using BMX at small spectrum widths and a high signal-to-noise ratio (SNR). Applications of BMX to weather observations are demonstrated using the PAR, and the results indicate that an average improvement factor of 2–4 can be obtained for SNR higher than 10 dB.

Rapid quantification of avian eggshell microstructure and crystallographic-texture using two-dimensional X-ray diffraction

1. It is important to quantify eggshell microstructure because it may influence eggshell mechanical properties and could be of importance for eggshell quality assessment. It also provides information about changes in eggshell associated with different hen physiological conditions. 2. This paper describes an alternative methodology which, based on 2D X-ray diffraction, allows eggshell microstructure quantification (for example, crystal size and orientation) much more efficiently than using traditional techniques (optical or scanning electron microscopy and conventional X-ray diffraction). 3. For such analyses, an X-ray diffractometer equipped with an area detector is used. Such equipment collects the diffraction pattern of the sample in one single exposure (taking a few tens of seconds), thus considerably reducing data acquisition time. 4. The two-dimensional diffraction patterns obtained contain detailed information about size and crystallographic orientation of crystals forming the eggshell. This information can be automatically extracted using specially designed software. 5. Access to the software is available on a website, the address of which is given in the Materials and Methods section. 6. In this paper, specific examples of how these analyses are applied are set out and recommendations for obtaining different types of microstructure information are given. In addition, the limitations of the technique are discussed.

Prediction of hydration status using multi-frequency bioelectrical impedance analysis during exercise and recovery in horses

The present study tested the hypothesis that multi-frequency bioelectrical impedance analysis (MFBIA) can be used to provide reasonable estimates of body mass, total body water (TBW), extracellular fluid volume (ECFV) and plasma volume (PV) at rest, during exercise-induced dehydration and subsequent recovery. Seven exerciseconditioned horses were administered indicators for measurement of resting TBW, ECFV and PV. MFBIA measurements at 24 frequencies between 5 and 280 kHz were obtained at rest, during prolonged submaximal exercise and for up to 13 h of recovery with food and water provided. Impedance ‐frequency response curves were described by a double-exponential decay equation from which coefficients were used, together with height and length, to generate predictive equations for estimating body mass, TBW, ECFV and PV. Predictive equations for body mass, ECFV and PV provided reasonable estimates of the parameter at rest and during exercise and recovery that were within 6% of absolute values determined using indicators. Despite the inherent error in estimating absolute volumes, the technique allowed accurate (within 1%) determination of the change in compartment volumes within individual horses over time. The number of frequencies at which impedance was measured could be reduced to seven without sacrificing the accuracy of the impedance‐frequency relationships or the predictive equations ‐ this enabled a 70% reduction in data-acquisition time (to , 35 s) for each MFBIA measurement series. It is concluded that MFBIA can be used in individual horses to track changes in compartmental hydration status resulting from dehydration and rehydration.

Object dependent sweep width reduction with spectral–spatial EPR imaging

For spectral–spatial EPR imaging, prior knowledge about the spatial support of an imaged object can be exploited in two ways. We can shrink the spatial field of view (FOV) to closely wrap the object in a sphere or reduce the sweep width in a projection dependent fashion. Use of a smaller spatial FOV with the same number of samples enhances spatial resolution by reducing voxel volume at the expense of signal-to-noise and a consequent degraded line-width resolution. We have developed another approach to define sweep width that prunes away the portions of the projection sweep with no signal. This reduces data acquisition time for the continuous wave (CW) EPR image proportional to the sweep width reduction. This method also avoids voxel volume reduction. Using the reduced-sweep method, we decreased the data acquisition time by 20% maintaining spatial and linewidth resolution.

Spatially uniform sampling in 4-D EPR spectral-spatial imaging

Four-dimensional EPR imaging involves a computationally intensive inversion of the sampled Radon transform. Conventionally, N-dimensional reconstructions have been carried out with N−1 stages of 2-D backprojection to exploit a dimension-dependent reduction in execution time. The huge data size of 4-D EPR imaging demands the use of a 3-stage reconstruction each consisting of 2-D backprojections. This gives three orders of magnitude reduction in computation relative to a single stage 4-D filtered backprojection. The multi-stage reconstruction, however, requires a uniform angular sampling that yields an inefficient distribution of gradient directions. We introduce a solution that involves acquisition of projections uniformly distributed in solid angle and reconstructs in three 2-D stages with the spatial uniform solid angle data set converted to uniform linear angular projections using 2-D interpolation. Images were taken from the two sampling schemes to compare the spatial resolution and the line width resolution. The degradation in the image quality due to the additional interpolation was small, and we achieved ∼30% reduction in data acquisition time.

Microfluidic Flow-Flash: Method for Investigating Protein Dynamics

We report a new method, microfluidic flow-flash, for measuring protein reaction kinetics. The method couples a microscope imaging detection system with a microfluidic flow cell to reduce data acquisition times and sample consumption. This combination allows for the simultaneous collection of spectral and temporal information. The microfluidic flow cell design utilizes three-dimensional sheath flow to reduce sample dispersion and minimize sample consumption. The ability to alter the flow rates in the microfluidic flow cells allows a variety of time scales to be studied with submillisecond time resolution. The imaging detection system can be coupled with several spectroscopic probes including fluorescence and UV/visible absorbance spectroscopy. Here, we utilize the microfluidic flow-flash method to probe the kinetics of CO recombination or O2 binding to myoglobin after the laser-induced photolysis of CO from myoglobin by UV/visible absorbance spectral imaging.

Spectral reconstruction methods in fast NMR: Reduced dimensionality, random sampling and maximum entropy

The need to reduce data acquisition times of multidimensional NMR experiments has fostered considerable interest in novel data acquisition schemes. A recurring theme is that of reduced dimensionality experiments, in which time evolutions in the indirect dimensions are incremented together, rather than independently. Spectral analysis of such data is carried out using methods such as filtered back-projection, GFT, or parametric signal modeling. By using Maximum Entropy reconstruction of reduced-dimensionality data, we show that the artifacts that arise in reduced dimensionality experiments are intrinsic to the data sampling, and are not, in general, the result of the methods used to compute spectra. Our results illustrate that reduced dimensionality is a special case of non-uniform sampling in the time domain. We show that MaxEnt reconstruction yields more accurate spectra for reduced dimensionality data than back-projection reconstruction and that randomly choosing time increments based on an exponentially weighted distribution is more efficient, with fewer artifacts, than the systematic coupling of time increments used in most reduced dimensionality approaches.

Fast spectroscopic imaging using online optimal sparse k‐space acquisition and projections onto convex sets reconstruction

Long acquisition times, low resolution, and voxel contamination are major difficulties in the application of magnetic resonance spectroscopic imaging (MRSI). To overcome these difficulties, an online-optimized acquisition of k-space, termed sequential forward array selection (SFAS), was developed to reduce acquisition time without sacrificing spatial resolution. A 2D proton MRSI region of interest (ROI) was defined from a scout image and used to create a region of support (ROS) image. The ROS was then used to optimize and obtain a subset of k-space (i.e., a subset of nonuniform phase encodings) and hence reduce the acquisition time for MRSI. Reconstruction and processing software was developed in-house to process and reconstruct MRSI using the projections onto convex sets method. Phantom and in vivo studies showed that good-quality MRS images are obtainable with an approximately 80% reduction of data acquisition time. The reduction of the acquisition time depends on the area ratio of ROS to FOV (i.e., the smaller the ratio, the greater the time reduction). It is also possible to obtain higher-resolution MRS images within a reasonable time using this approach. MRSI with a resolution of 64 x 64 is possible with the acquisition time of the same as 24 x 24 using the traditional full k-space method.