Reduce Data Acquisition Time Research Articles

Maximum a posteriori estimation of crystallographic phases in X-ray diffraction tomography.

A maximum a posteriori approach is proposed for X-ray diffraction tomography for reconstructing three-dimensional spatial distribution of crystallographic phases and orientations of polycrystalline materials. The approach maximizes the a posteriori density which includes a Poisson log-likelihood and an a priori term that reinforces expected solution properties such as smoothness or local continuity. The reconstruction method is validated with experimental data acquired from a section of the spinous process of a porcine vertebra collected at the 1-ID-C beamline of the Advanced Photon Source, at Argonne National Laboratory. The reconstruction results show significant improvement in the reduction of aliasing and streaking artefacts, and improved robustness to noise and undersampling compared to conventional analytical inversion approaches. The approach has the potential to reduce data acquisition times, and significantly improve beamtime efficiency.

1-A-D-18. A Study on improvement of character input performance for a P300 speller

A P300 speller, which is equipment for character input utilizing an event-related potential (ERP), was tested clinically in patients with amyotrophic lateral sclerosis (ALS). Improvement of character input speed and reduction of data acquisition time for pre-training were evaluated. The authors developed a stimulus presentation method named reliability based automatic repeat request (RB-ARQ), which dynamically discontinues stimulus presentation based on the reliability of discriminant function, an N-gram method which calculates this reliability based on the transition probabilities of Japanese letters, and an incremental learning method which uses on-line EEG data for updating the discriminant function on-line. The P300 speller incorporating the RB-ARQ and N-gram method was tested with four ALS patients on ventilator in sickrooms with plural beds in two hospitals. The utility, an index for character input speed, of the proposed method was 3.3 times higher than that of the conventional averaging method. It took approximately 10 s to input a character on average. The incremental learning method was tested in simulation using EEG data collected from two patients a half year ago and another patient a week ago for the pre-training. The proposed method achieved zero acquisition time at the start of re-use of the speller.

Characterising the rheology of non-Newtonian fluids using PFG-NMR and cumulant analysis

Conventional rheological characterisation using nuclear magnetic resonance (NMR) typically utilises spatially-resolved measurements of velocity. We propose a new approach to rheometry using pulsed field gradient (PFG) NMR which readily extends the application of MR rheometry to single-axis gradient hardware. The quantitative use of flow propagators in this application is challenging because of the introduction of artefacts during Fourier transform, which arise when realistic sampling strategies are limited by experimental and hardware constraints and when particular spatial and temporal resolution are required. The method outlined in this paper involves the cumulant analysis of the acquisition data directly, thereby preventing the introduction of artefacts and reducing data acquisition times. A model-dependent approach is developed to enable the pipe-flow characterisation of fluids demonstrating non-Newtonian power-law rheology, involving the use of an analytical expression describing the flow propagator in terms of the flow behaviour index. The sensitivity of this approach was investigated and found to be robust to the signal-to-noise ratio (SNR) and number of acquired data points, enabling an increase in temporal resolution defined by the SNR. Validation of the simulated results was provided by an experimental case study on shear-thinning aqueous xanthan gum solutions, whose rheology could be accurately characterised using a power-law model across the experimental shear rate range of 1–100s−1. The flow behaviour indices calculated using this approach were observed to be within 8% of those obtained using spatially-resolved velocity imaging and within 5% of conventional rheometry. Furthermore, it was shown that the number of points sampled could be reduced by a factor of 32, when compared to the acquisition of a volume-averaged flow propagator with 128 gradient increments, without negatively influencing the accuracy of the characterisation, reducing the acquisition time to only 3% of its original value.

Hyperspectral image reconstruction for x-ray fluorescence tomography.

A penalized maximum-likelihood estimation is proposed to perform hyperspectral (spatio-spectral) image reconstruction for X-ray fluorescence tomography. The approach minimizes a Poisson-based negative log-likelihood of the observed photon counts, and uses a penalty term that has the effect of encouraging local continuity of model parameter estimates in both spatial and spectral dimensions simultaneously. The performance of the reconstruction method is demonstrated with experimental data acquired from a seed of arabidopsis thaliana collected at the 13-ID-E microprobe beamline at the Advanced Photon Source. The resulting element distribution estimates with the proposed approach show significantly better reconstruction quality than the conventional analytical inversion approaches, and allows for a high data compression factor which can reduce data acquisition times remarkably. In particular, this technique provides the capability to tomographically reconstruct full energy dispersive spectra without compromising reconstruction artifacts that impact the interpretation of results.

REDOR solid-state NMR as a probe of the membrane locations of membrane-associated peptides and proteins

Rotational-echo double-resonance (REDOR) solid-state NMR is applied to probe the membrane locations of specific residues of membrane proteins. Couplings are measured between protein 13CO nuclei and membrane lipid or cholesterol 2H and 31P nuclei. Specific 13CO labeling is used to enable unambiguous assignment and 2H labeling covers a small region of the lipid or cholesterol molecule. The 13CO–31P and 13CO–2H REDOR respectively probe proximity to the membrane headgroup region and proximity to specific insertion depths within the membrane hydrocarbon core. One strength of the REDOR approach is use of chemically-native proteins and membrane components. The conventional REDOR pulse sequence with 100kHz 2H π pulses is robust with respect to the 2H quadrupolar anisotropy. The 2H T1’s are comparable to the longer dephasing times (τ’s) and this leads to exponential rather than sigmoidal REDOR buildups. The 13CO–2H buildups are well-fitted to A×(1−e−γτ) where A and γ are fitting parameters that are correlated as the fraction of molecules (A) with effective 13CO–2H coupling d=3γ/2. The REDOR approach is applied to probe the membrane locations of the “fusion peptide” regions of the HIV gp41 and influenza virus hemagglutinin proteins which both catalyze joining of the viral and host cell membranes during initial infection of the cell. The HIV fusion peptide forms an intermolecular antiparallel β sheet and the REDOR data support major deeply-inserted and minor shallowly-inserted molecular populations. A significant fraction of the influenza fusion peptide molecules form a tight hairpin with antiparallel N- and C-α helices and the REDOR data support a single peptide population with a deeply-inserted N-helix. The shared feature of deep insertion of the β and α fusion peptide structures may be relevant for fusion catalysis via the resultant local perturbation of the membrane bilayer. Future applications of the REDOR approach may include samples that contain cell membrane extracts and use of lower temperatures and dynamic nuclear polarization to reduce data acquisition times.

Fly-scan ptychography.

We report an experimental ptychography measurement performed in fly-scan mode. With a visible-light laser source, we demonstrate a 5-fold reduction of data acquisition time. By including multiple mutually incoherent modes into the incident illumination, high quality images were successfully reconstructed from blurry diffraction patterns. This approach significantly increases the throughput of ptychography, especially for three-dimensional applications and the visualization of dynamic systems.

High-throughput studies of protein shapes and interactions by synchrotron small-angle X-ray scattering.

Solution-based small angle X-ray scattering (SAXS) affords the opportunity to extract accurate structural parameters and global shape information from diverse biological macromolecular systems. SAXS is an ideal complementary technique to other structural and biophysical methods but it can also be applied alone to access structural information that is otherwise unobtainable using high-resolution methods. Macromolecular structures ranging from kilodaltons to gigadaltons can be analyzed, which encompasses the size of most proteins and functional cellular complexes. The SAXS analysis is performed using only a few microliters of solution containing microgram quantities of purified material in sample environments that can be tailored to mimic physiological conditions or altered to suit a particular question. High-brilliance synchrotron X-ray sources and parallel advances in hardware and computing have reduced data acquisition times to the millisecond range and the application of automated methods have allowed data processing and low resolution shape modelling to be completed within minutes. These developments have paved the way for high-throughput studies that generate significant quantities of structural information over a short period of time. Here, we briefly consider the basics of SAXS and describe major methods and protocols employed in high-throughput SAXS studies.

Advanced new relaxation filter-selective signal excitation methods for (13)C solid-state nuclear magnetic resonance.

We have developed new relaxation filter-selective signal excitation (RFS) methods for (13)C solid-state NMR, which enable extraction of the spectrum of a target component from a mixture of several components. These methods are based on the equalization of proton relaxation time in a single domain via rapid intraproton spin diffusion and the difference in proton relaxation time of individual components in the mixture. We recently reported two types of RFS methods using proton spin-lattice relaxation time in the rotating frame ((1)H T1rho) in (13)C solid-state nuclear magnetic resonance (NMR) spectroscopy. Here, to increase the availability of RFS methods, we focus on proton spin-lattice relaxation time ((1)H T1). Introduction of simple pulse sequences to one-dimensional experiments reduced data acquisition time and increased flexibility, and led to the development of two new types of RFS methods using (1)H T1. We then demonstrated these methods by selectively exciting the (13)C signals of target components in a commercially available drug and a number of physical mixtures, and we showed them to be applicable to the quantitative analysis of individual components in these solid mixtures with an experimental duration of 1.5 to about 10 h. The practicality and versatility of these four RFS methods were increased by combining two or more of them, or by using a flip-back pulse, which is an effective means of shortening experimental duration. These RFS methods are suitable for use in a broad range of fields.

Fitting approach to liquid–liquid displacement porosimetry based on the log-normal pore size distribution

Liquid–liquid displacement porosimetry (LLDP) has been used to characterize several UF membranes in a wide range of molecular weight cut-offs (MWCO). A new method to convert porosimetric data into pore size distributions and related information has been developed based on assuming log-normal pore size distributions. The results of this are in good agreement with those from the customary data conversion algorithm (as derived by Grabar and Nikitine). The proposed method can also be used when a reduced number of experimental data points is available, leading to a significant reduction of data acquisition time needed to complete a reliable analysis.

Rapid acquisition of auditory subcortical steady state responses using multichannel recordings

ObjectiveAuditory subcortical steady state responses (SSSRs), also known as frequency following responses (FFRs), provide a non-invasive measure of phase-locked neural responses to acoustic and cochlear-induced periodicities. SSSRs have been used both clinically and in basic neurophysiological investigation of auditory function. SSSR data acquisition typically involves thousands of presentations of each stimulus type, sometimes in two polarities, with acquisition times often exceeding an hour per subject. Here, we present a novel approach to reduce the data acquisition times significantly. MethodsBecause the sources of the SSSR are deep compared to the primary noise sources, namely background spontaneous cortical activity, the SSSR varies more smoothly over the scalp than the noise. We exploit this property and extract SSSRs efficiently, using multichannel recordings and an eigendecomposition of the complex cross-channel spectral density matrix. ResultsOur proposed method yields SNR improvement exceeding a factor of 3 compared to traditional single-channel methods. ConclusionsIt is possible to reduce data acquisition times for SSSRs significantly with our approach. SignificanceThe proposed method allows SSSRs to be recorded for several stimulus conditions within a single session and also makes it possible to acquire both SSSRs and cortical EEG responses without increasing the session length.

Non‐Cartesian parallel imaging reconstruction

Non-Cartesian parallel imaging has played an important role in reducing data acquisition time in MRI. The use of non-Cartesian trajectories can enable more efficient coverage of k-space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be used to reconstruct images from undersampled Cartesian data, non-Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the nonhomogeneous sensitivities of multi-coil phased arrays. This review will begin with an overview of non-Cartesian k-space trajectories and their sampling properties, followed by an in-depth discussion of several selected non-Cartesian parallel imaging algorithms. Three representative non-Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non-Cartesian generalized autocalibrating partially parallel acquisition (GRAPPA), and Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non-Cartesian parallel imaging will be covered.

Accuracy of compressed sensing for left ventricular mass and volumes

Background While SSFP CMR is the gold standard for assessing left ventricular (LV) function, it requires a regular cardiac rhythm and frequent breath-holds and not all patients with cardiovascular disease are able to achieve this. It is known that Compressed Sensing (CS) greatly reduces data acquisition time however its accuracy for LV volume and mass is currently unknown. This study compares ventricular function measurements by CS to those obtained from standard SSFP cines.

Inter-observer variability of LV mass and volumes with compressed sensing

Background Recently developed compressed sensing (CS) SSFP sequences significantly reduce data acquisition time thereby reducing patient breath-hold and scan time relative to standard techniques. The variability of ventricular functional (VF) analysis of CS data is not known so this study investigated the inter-observer variation of left ventricular (LV) mass and volume measurements compared to standard SSFP acquisitions.

Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator

Compressed sensing MRI (CS-MRI) has shown great potential in reducing data acquisition time in MRI. Sparsity or compressibility plays an important role to reduce the image reconstruction error. Conventional CS-MRI typically uses a pre-defined sparsifying transform such as wavelet or finite difference, which sometimes does not lead to a sufficient sparse representation for the image to be reconstructed. In this paper, we design a patch-based nonlocal operator (PANO) to sparsify magnetic resonance images by making use of the similarity of image patches. The definition of PANO results in sparse representation for similar patches and allows us to establish a general formulation to trade the sparsity of these patches with the data consistency. It also provides feasibility to incorporate prior information learnt from undersampled data or another contrast image, which leads to optimized sparse representation of images to be reconstructed. Simulation results on in vivo data demonstrate that the proposed method achieves lower reconstruction error and higher visual quality than conventional CS-MRI methods.

Focus on analytical equipment and diagnostics

Focus on analytical equipment and diagnostics

Smoothly Clipped Absolute Deviation (SCAD) regularization for compressed sensing MRI Using an augmented Lagrangian scheme

PurposeCompressed sensing (CS) provides a promising framework for MR image reconstruction from highly undersampled data, thus reducing data acquisition time. In this context, sparsity-promoting regularization techniques exploit the prior knowledge that MR images are sparse or compressible in a given transform domain. In this work, a new regularization technique was introduced by iterative linearization of the non-convex smoothly clipped absolute deviation (SCAD) norm with the aim of reducing the sampling rate even lower than it is required by the conventional l1 norm while approaching an l0 norm. Materials and MethodsThe CS-MR image reconstruction was formulated as an equality-constrained optimization problem using a variable splitting technique and solved using an augmented Lagrangian (AL) method developed to accelerate the optimization of constrained problems. The performance of the resulting SCAD-based algorithm was evaluated for discrete gradients and wavelet sparsifying transforms and compared with its l1-based counterpart using phantom and clinical studies. The k-spaces of the datasets were retrospectively undersampled using different sampling trajectories. In the AL framework, the CS-MRI problem was decomposed into two simpler sub-problems, wherein the linearization of the SCAD norm resulted in an adaptively weighted soft thresholding rule with a sparsity enhancing effect. ResultsIt was demonstrated that the proposed regularization technique adaptively assigns lower weights on the thresholding of gradient fields and wavelet coefficients, and as such, is more efficient in reducing aliasing artifacts arising from k-space undersampling, when compared to its l1-based counterpart. ConclusionThe SCAD regularization improves the performance of l1-based regularization technique, especially at reduced sampling rates, and thus might be a good candidate for some applications in CS-MRI.

Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT

Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.

Development of Qualitative and Quantitative Analysis Methods in Pharmaceutical Application with New Selective Signal Excitation Methods for 13C Solid-State Nuclear Magnetic Resonance Using 1H T1rho Relaxation Time

Most pharmaceutical drug substances and excipients in formulations exist in a crystalline or amorphous form, and an understanding of their state during manufacture and storage is critically important, particularly in formulated products. Carbon 13 solid-state nuclear magnetic resonance (NMR) spectroscopy is useful for studying the chemical and physical state of pharmaceutical solids in a formulated product. We developed two new selective signal excitation methods in (13) C solid-state NMR to extract the spectrum of a target component from such a mixture. These methods were based on equalization of the proton relaxation time in a single domain via rapid intraproton spin diffusion and the difference in proton spin-lattice relaxation time in the rotating frame ((1) H T1rho) of individual components in the mixture. Introduction of simple pulse sequences to one-dimensional experiments reduced data acquisition time and increased flexibility. We then demonstrated these methods in a commercially available drug and in a mixture of two saccharides, in which the (13) C signals of the target components were selectively excited, and showed them to be applicable to the quantitative analysis of individual components in solid mixtures, such as formulated products, polymorphic mixtures, or mixtures of crystalline and amorphous phases.

A new fast magnetic resonance imaging method based on variabledensity spiral data acquisition and Bregman iterative reconstruction

Data acquisition time is a bottle neck for increasing imaging speed of magnetic resonance imaging. To solve the problem, a new fast magnetic resonance imaging method based on variable-density spiral acquisition and Bregman iterative reconstruction is proposed in this paper, under the framework of compressed sensing. The proposed method increases the acquisition speed by data undersampling. The resulting undersampling aliasing artifact is removed by utilizing the intrinsic property of variable-density spiral and Bregman iterative recosntruction. The proposed method is validated by both phantom experiemnt and in vivo experiment. The experimental results demonstrate that the proposed method can effectively remove aliasing artifact from data undersampling, and achieve an image with well-preserved image structure information. Therefore this method can be used for reducing data acquisition time.

Sparse Tomographic Inverse Scattering Approach for Through-the-Wall Radar Imaging

We present a sparse tomographic inverse scattering approach for application in through-the-wall radar imaging, which enables reduction in data acquisition time and fast investigation of very large multi-targets domains, while maintaining a reasonable image quality. The imaging technique achieves fast 3-D scene reconstructions using a linear inverse scattering algorithm based on truncated singular value decomposition (SVD), combined with a 2-D sliced approach. The SVD of the integral equation relevant to the imaging system is used to guide the reduction in the number of data measurements. Performance of the proposed sparse scheme is evaluated using experimental data.