Spectral Bins Research Articles

Detection of acute periodontal pain from physiological signals

Objective: To investigate the feasibility of the detection of brief orofacial pain sensations from easily recordable physiological signals by means of machine learning techniques. Approach: A total of 47 subjects underwent periodontal probing and indicated each instance of pain perception by means of a push button. Simultaneously, physiological signals were recorded and, subsequently, autonomic indices were computed. By using the autonomic indices as input features of a classifier, a pain indicator based on fusion of the various autonomic mechanisms was achieved. Seven patients were randomly chosen for the test set. The rest of the data were utilized for the validation of several classifiers and feature combinations by applying leave-one-out-cross-validation. Main results: During the validation process the random forest classifier, using frequency spectral bins of the ECG, wavelet level energies of the ECG and PPG, PPG amplitude, and SPI as features, turned out to be the best pain detection algorithm. The final test of this algorithm on the independent test dataset yielded a sensitivity and specificity of 71% and 70%, respectively. Significance: Based on these results, fusion of autonomic indices by applying machine learning techniques is a promising option for the detection of very brief instances of pain perception, that are not covered by the established indicators.

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km2) in Southwest Ohio and Taylorsville Lake (11.88 km2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earth’s orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

Optimization of wavelength selection for multispectral image acquisition: a case study of atrial ablation lesions

In vivo autofluorescence hyperspectral imaging of moving objects can be challenging due to motion artifacts and to the limited amount of acquired photons. To address both limitations, we selectively reduced the number of spectral bands while maintaining accurate target identification. Several downsampling approaches were applied to data obtained from the atrial tissue of adult pigs with sites of radiofrequency ablation lesions. Standard image qualifiers such as the mean square error, the peak signal-to-noise ratio, the structural similarity index map, and an accuracy index of lesion component images were used to quantify the effects of spectral binning, an increased spectral distance between individual bands, as well as random combinations of spectral bands. Results point to several quantitative strategies for deriving combinations of a small number of spectral bands that can successfully detect target tissue. Insights from our studies can be applied to a wide range of applications.

Frequency estimators with high resistance to interference from image frequency components

This paper presents an improved frequency estimation algorithm based on the interpolated discrete Fourier transform. High-accurate frequency estimation can be achieved by taking the geometric mean of two independent estimates, which are derived from the real parts of the two largest spectral bins and the imaginary parts, respectively. In situations where only a small number of sine wave cycles are observed, the ability of the algorithm to cancel interference from image frequency components results in improvements in accuracy. The residual errors of the proposed algorithm have been theoretically analyzed with maximum side-lobe decaying windows, since the windows have simple and uniform analytical expression of interpolation algorithm. The performance of the proposed algorithm was investigated using both Hanning and three-term maximum side-lobe decaying windows. A comparative analysis of systematic errors and noise sensitivity was performed between the new algorithm and traditional algorithms. Both the root mean squared error and the probability density of the errors were investigated under noisy conditions. Simulation results demonstrated that the new algorithm is not only highly resistant to interference from image components but is also resistant to the effects of random noise. The results presented in the paper are useful for identifying the best choice of algorithm in practical engineering applications.

Computational reconfigurable imaging spectrometer.

We demonstrate a novel hyperspectral imaging spectrometer based on computational imaging that enables sensitive measurements from smaller, noisier, and less-expensive components (e.g. uncooled microbolometers), making it useful for applications such as small space and air platforms with strict size, weight, and power requirements. The computational reconfigurable imaging spectrometer (CRISP) system exploits platform motion and a spectrally coded focal-plane mask to temporally modulate the optical spectrum, enabling simultaneous measurement of multiple spectral bins. Demodulation of this coded pattern returns an optical spectrum in each pixel.

Spectral binning for energy production calculations and multijunction solar cell design

AbstractCurrently, most solar cells are designed for and evaluated under standard spectra intended to represent typical spectral conditions. However, no single spectrum can capture the spectral variability needed for annual energy production (AEP) calculations, and this shortcoming becomes more significant for series‐connected multijunction cells as the number of junctions increases. For this reason, AEP calculations are often performed on very detailed yearlong sets of data, but these pose 2 inherent challenges: (1) These data sets comprise thousands of data points, which appear as a scattered cloud of data when plotted against typical parameters and are hence cumbersome to classify and compare, and (2) large sets of spectra bring with them a corresponding increase in computation or measurement time. Here, we show how a large spectral set can be reduced to just a few “proxy” spectra, which still retain the spectral variability information needed for AEP design and evaluation. The basic “spectral binning” methods should be extensible to a variety of multijunction device architectures. In this study, as a demonstration, the AEP of a 4‐junction device is computed for both a full set of spectra and a reduced proxy set, and the results show excellent agreement for as few as 3 proxy spectra. This enables much faster (and thereby more detailed) calculations and indoor measurements and provides a manageable way to parameterize a spectral set, essentially creating a “spectral fingerprint,” which should facilitate the understanding and comparison of different sites.

Balmer Filaments in Tycho’s Supernova Remnant: An Interplay between Cosmic-ray and Broad-neutral Precursors

Abstract We present Hα spectroscopic observations and detailed modeling of the Balmer filaments in the supernova remnant (SNR) Tycho (SN 1572). We used GH α FaS (Galaxy Hα Fabry–Pérot Spectrometer) on the William Herschel Telescope with a 3.′4 × 3.′4 field of view, 0.″2 pixel scale, and σ instr = 8.1 km s−1 resolution at 1″ seeing for ∼10 hr, resulting in 82 spatial−spectral bins that resolve the narrow Hα line in the entire SN 1572 northeastern rim. For the first time, we can therefore mitigate artificial line broadening from unresolved differential motion and probe Hα emission parameters in varying shock and ambient medium conditions. Broad Hα line remains unresolved within spectral coverage of 392 km s−1. We employed Bayesian inference to obtain reliable parameter confidence intervals and to quantify the evidence for models with multiple line components. The median Hα narrow-line (NL) FWHM of all bins and models is W NL = ( 54.8 ± 1.8 ) km s−1 at the 95% confidence level, varying within [35, 72] km s−1 between bins and clearly broadened compared to the intrinsic (thermal) ≈20 km s−1. Possible line splits are accounted for, significant in ≈ 18 % of the filament, and presumably due to remaining projection effects. We also find widespread evidence for intermediate-line emission of a broad-neutral precursor, with a median W IL = ( 180 ± 14 ) km s−1 (95% confidence). Finally, we present a measurement of the remnant’s systemic velocity, V LSR = − 34 km s−1, and map differential line-of-sight motions. Our results confirm the existence and interplay of shock precursors in Tycho’s remnant. In particular, we show that suprathermal NL emission is near-universal in SN 1572, and that, in the absence of an alternative explanation, collisionless SNR shocks constitute a viable acceleration source for Galactic TeV cosmic-ray protons.

Health State Monitoring of Bladed Machinery with Crack Growth Detection in BFG Power Plant Using an Active Frequency Shift Spectral Correction Method.

Power generation using waste-gas is an effective and green way to reduce the emission of the harmful blast furnace gas (BFG) in pig-iron producing industry. Condition monitoring of mechanical structures in the BFG power plant is of vital importance to guarantee their safety and efficient operations. In this paper, we describe the detection of crack growth of bladed machinery in the BFG power plant via vibration measurement combined with an enhanced spectral correction technique. This technique enables high-precision identification of amplitude, frequency, and phase information (the harmonic information) belonging to deterministic harmonic components within the vibration signals. Rather than deriving all harmonic information using neighboring spectral bins in the fast Fourier transform spectrum, this proposed active frequency shift spectral correction method makes use of some interpolated Fourier spectral bins and has a better noise-resisting capacity. We demonstrate that the identified harmonic information via the proposed method is of suppressed numerical error when the same level of noises is presented in the vibration signal, even in comparison with a Hanning-window-based correction method. With the proposed method, we investigated vibration signals collected from a centrifugal compressor. Spectral information of harmonic tones, related to the fundamental working frequency of the centrifugal compressor, is corrected. The extracted spectral information indicates the ongoing development of an impeller blade crack that occurred in the centrifugal compressor. This method proves to be a promising alternative to identify blade cracks at early stages.

Poster Presentations

Success of first breeding is a major concern for goat breeders, since failure to fertilize does increases unproductive time and breeding costs. Fertility rates after artificial insemination of young goats are highly variable and rather low. Breeders generally breed does that are older than 5 months and weight more than 32 kg. However, sexual precocity is highly variable between does. Up to now, there is no known biomarker for sexual precocity. A better characterization of the pubertal stage of maturity could help optimizing time for first breeding. Our objective was to analyze the serum metabolome of doe kids, just before the first breeding, in order to characterize the pubertal stage of maturity and identify biomarkers of sexual precocity. Weekly blood sampling was performed on twenty 6-to 7-month-old does born in February for 5 weeks before their first contact with bucks in September. Progesterone assays and metabolome analysis using 1H Nuclear Magnetic Resonance Spectroscopy were performed on the serum samples. No spontaneous ovulatory cycle was observed before breeding based on progesterone assays. All does had reached the pubertal stage of maturity at breeding since all got pregnant. Metabolome analysis allowed the identification of 109 spectral bins in sera. Between week 1 and 5 preceding buck introduction, 32 buckets showed significant variations (t-test, p < 0.05): i.e. inosine, formate, lactate and creatinine decreased, while threonine, tryptophan, isoleucine and trimethylamine oxide significantly increased. Metabolites with significant variations between the 5 considered weeks could be biomarkers of sexual precocity; studies are in progress to identify them.

Research on choices of spectral bins in energy-based frequency estimators under intensive noise

This paper presented the problem of wrong choice of spectral bins in the energy-based frequency estimation method under intensive noise, and studied its impact on the frequency estimation. Based on the theory of energy-based method, the causes for wrong location of spectral bins in the traditional estimation method were analyzed. In order to reduce wrong choice rate of spectral bins, two optimizational location strategies of spectral bins were introduced, and the effects of them were confirmed by computer simulation. A numerical test under intensive noise was carried out, in which estimation errors returned by optimizational spectral location strategies were compared. It was demonstrated that the estimation accuracy under intensive noise can be remarkably improved by using optimizational spectral location strategies. In particular, Macleod’s optimizational strategy is strongly suggested because of its prominent advantage in reducing occurrences of wrong location as well as its best performance in frequenc...

Travel mode estimation for multi-modal journey planner

For route planning and tracking, it is sometimes necessary to know if the user is walking or using some other mode of transport. In most cases, the GPS data can be acquired from the user device. It is possible to estimate user’s transportation mode based on a GPS trace at a sampling rate of once per minute. There has been little prior work on the selection of a set of features from a large number of proposed features, especially for sparse GPS data. This article considers characteristics of distribution, auto- and cross-correlations, and spectral features of speed and acceleration as possible features, and presents an approach to selecting the most significant, non-correlating features from among those. Both speed and acceleration are inferred from changes in location and time between data points. Using GPS traces of buses in the city of Tampere, and of walking, biking and driving from the OpenStreetMap and Microsoft GeoLife projects, spectral bins were found to be among the most significant non-correlating features for differentiating between walking, bicycle, bus and driving, and were used to train classifiers with a fair accuracy. Auto- and cross-correlations, kurtoses and skewnesses were found to be of no use in the classification task. Useful features were found to have a fairly large (>0.4) correlation with each other.

PCA-based radiative transfer: Improvements to aerosol scheme, vertical layering and spectral binning

The Principal Component Analysis (PCA)-based fast radiative transfer method has been shown to have at least an order of magnitude increase in computational speed while maintaining an overall accuracy of 0.01% (compared to line-by-line calculations) over narrow and broad spectral bands. In this paper, we describe several improvements made to the method and provide a discussion of the method’s performance over a diverse set of atmospheric profiles and land surface types. We also test the model over uniform pressure level profiles. The method is now capable of providing atmospheric spectra with residuals under 0.1%, calculated with respect to the continuum, throughout the shortwave region between 0.3−3μm at high resolution, which is substantial improvement over errors reported in earlier work. Future directions for applications and further optimization are examined.

Cross-correlation of weak lensing and gamma rays: implications for the nature of dark matter

We measure the cross-correlation between Fermi-LAT gamma-ray photons and over 1000 deg$^2$ of weak lensing data from the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), the Red Cluster Sequence Lensing Survey (RCSLenS), and the Kilo Degree Survey (KiDS). We present the first measurement of tomographic weak lensing cross-correlations and the first application of spectral binning to cross-correlations between gamma rays and weak lensing. The measurements are performed using an angular power spectrum estimator while the covariance is estimated using an analytical prescription. We verify the accuracy of our covariance estimate by comparing it to two internal covariance estimators. Based on the non-detection of a cross-correlation signal, we derive constraints on weakly interacting massive particle (WIMP) dark matter. We compute exclusion limits on the dark matter annihilation cross-section $\langle\sigma_\rm{ann} v \rangle$, decay rate $\Gamma_\rm{dec}$, and particle mass $m_\rm{DM}$. We find that in the absence of a cross-correlation signal, tomography does not significantly improve the constraining power of the analysis. Assuming a strong contribution to the gamma-ray flux due to small-scale clustering of dark matter and accounting for known astrophysical sources of gamma rays, we exclude the thermal relic cross-section for particle masses of $m_\rm{DM}\lesssim 20$ GeV.

ACCESS I. AN OPTICAL TRANSMISSION SPECTRUM OF GJ 1214b REVEALS A HETEROGENEOUS STELLAR PHOTOSPHERE

ABSTRACT GJ 1214b is the most studied sub-Neptune exoplanet to date. Recent measurements have shown its near-infrared transmission spectrum to be flat, pointing to a high-altitude opacity source in the exoplanet's atmosphere, either equilibrium condensate clouds or photochemical hazes. Many photometric observations have been reported in the optical by different groups, though simultaneous measurements spanning the entire optical regime are lacking. We present an optical transmission spectrum (4500–9260 Å) of GJ 1214b in 14 bins, measured with Magellan/IMACS repeatedly over three transits. We measure a mean planet-to-star radius ratio of R p / R s = 0.1146 ± 2 × 10 − 4 and mean uncertainty of σ ( R p / R s ) = 8.7 × 10 − 4 in the spectral bins. The optical transit depths are shallower on average than observed in the near-infrared. We present a model for jointly incorporating the effects of a composite photosphere and atmospheric transmission through the exoplanet's limb (the CPAT model), and use it to examine the cases of absorber and temperature heterogeneities in the stellar photosphere. We find the optical and near-infrared measurements are best explained by the combination of (1) photochemical haze in the exoplanetary atmosphere with a mode particle size r = 0.1 μm and haze-forming efficiency f haze = 10 % and (2) faculae in the unocculted stellar disk with a temperature contrast Δ T = 354 − 46 + 46 K, assuming 3.2% surface coverage. The CPAT model can be used to assess potential contributions of heterogeneous stellar photospheres to observations of exoplanet transmission spectra, which will be important for searches for spectral features in the optical.

Combined Application of UHPLC-QTOF/MS, HPLC-ELSD and 1 H-NMR Spectroscopy for Quality Assessment of DA-9801, A Standardised Dioscorea Extract.

DA-9801, a standardised 50% aqueous ethanolic extract of a mixture of Dioscorea japonica and D. nipponica, is a botanical drug candidate for the treatment of diabetic neuropathy, which finished its US phase II clinical trials recently. An advanced quality control method is needed for further development of DA-9801, considering its high contents of both primary and secondary metabolites. Development of a quality assessment strategy for DA-9801, based on the combination of UHPLC-QTOF/MS, HPLC-ELSD, and 1 H-NMR spectroscopy. The method was developed and tested with 15 batch products of DA-9801. The steroidal saponins of DA-9801 were tentatively identified by UHPLC-QTOF/MS and were quantified with the validated HPLC-ELSD method. Primary metabolites of DA-9801 were identified and profiled using 1 H-NMR spectrometry. The batch-to-batch equivalence of DA-9801 was tested with the 1 H-NMR spectra using spectral binning, correlation analysis, and principal component analysis. Six major saponins of DA-9801 were tentatively identified by UHPLC-QTOF/MS. Among them, protodioscin and dioscin were quantified by the validated HPLC-ELSD method. Twenty-six metabolites were identified in 1 H-NMR spectra. The similarity between DA-9801 batches could be evaluated with the NMR spectra of DA-9801. The 1 H-NMR method also revealed that two Dioscorea species contributed distinct amino acids to the contents of DA-9801. This study validates the effectiveness of UHPLC-QTOF/MS, HPLC-ELSD, and 1 H NMR-combined method for quality control of DA-9801 and its crude materials. Copyright © 2016 John Wiley & Sons, Ltd.

Potassium detection in the clear atmosphere of a hot-Jupiter

We present FORS2 (attached to ESO's Very Large Telescope) observations of the exoplanet WASP-17b during its primary transit, for the purpose of differential spectrophotometry analysis. We use the instrument in its Mask eXchange Unit (MXU) mode to simultaneously obtain low resolution spectra of the planet hosting star, as well as several reference stars in the field of view. The integration of these spectra within broadband and smaller 100$\AA$ bins provides us with 'white' and spectrophotometric light curves, from 5700 to 8000$\AA$. Through modelling the white light curve, we obtain refined bulk and transit parameters of the planet, as well as wavelength-dependent variations of the planetary radius from smaller spectral bins through which the transmission spectrum is obtained. The inference of transit parameters, as well as the noise statistics, is performed using a Gaussian Process model. We achieve a typical precision in the transit depth of a few hundred parts per million from various transit light curves. From the transmission spectra we rule out a flat spectrum at >3$\sigma$ and detect marginal presence of the pressure-broadened sodium wings. Furthermore, we detect the wing of the potassium absorption line in the upper atmosphere of the planet with 3$\sigma$ confidence, both facts pointing to a relatively shallow temperature gradient of the atmosphere. These conclusions are mostly consistent with previous studies of this exo-atmosphere, although previous potassium measurements have been inconclusive.

Dithering techniques in automatic recognition of speech corrupted by MP3 compression: Analysis, solutions and experiments

A large portion of the audio files distributed over the Internet or those stored in personal and corporate media archives are in a compressed form. There exist several compression techniques and algorithms but it is the MPEG Layer-3 (known as MP3) that has achieved a really wide popularity in general audio coding, and in speech, too. However, the algorithm is lossy in nature and introduces distortion into spectral and temporal characteristics of a signal. In this paper we study its impact on automatic speech recognition (ASR). We show that with decreasing MP3 bitrates the major source of ASR performance degradation is deep spectral valleys (i.e. bins with almost zero energy) caused by the masking effect of the MP3 algorithm. We demonstrate that these unnatural gaps in spectrum can be effectively compensated by adding a certain amount of noise to the distorted signal. We provide theoretical background for this approach where we show that the added noise affects mainly the spectral valleys. They are filled by the noise while the spectral bins with speech remain almost unchanged. This helps to restore a more natural shape of log spectrum and cepstrum, and consequently has a positive impact on ASR performance. In our previous work, we have proposed two types of the signal dithering (noise addition) technique, one applied globally, the other in a more selective way. In this paper, we offer a more detailed insight into their performance. We provide results from many experiments where we test them in various scenarios, using a large vocabulary continuous speech recognition (LVCSR) system, acoustic models based on gaussian-mixture model (GMM) as well as on deep-neural network (DNN), and multiple speech databases in three languages (Czech, English and German). Our results prove that both the proposed techniques, and the selective dithering method, in particular, yield consistent compensation of the negative impact of the MP3 compressed speech on ASR performance.

1H-Nuclear magnetic resonance-based metabolic profiling of nonsteroidal anti-inflammatory drug-induced adverse effects in rats

Nonsteroidal anti-inflammatory drugs (NSAIDs), which are globally prescribed, exhibit mainly anti-inflammatory and analgesic effects but also can cause adverse effects including gastrointestinal erosions, ulceration, bleeding, and perforation. The purpose of this study was to investigate surrogate biomarkers associated with the gastrointestinal (GI) damage caused by NSAID treatment using pattern recognition analysis of 1H-nuclear magnetic resonance (1H NMR) spectra of rat urine. Urine was collected for 5h after oral administration of the following NSAIDs at low or high doses: acetylsalicylic acid (10 or 200mgkg−1), diclofenac (0.5 or 15mgkg−1), piroxicam (1 or 10mgkg−1), indomethacin (1 or 25mgkg−1), or ibuprofen (10, or 150mgkg−1) as nonselective COX inhibitors and celecoxib (10 or 100mgkg−1) as a COX-2 selective inhibitor. The urine was analyzed using 500MHz 1H NMR for spectral binning and targeted profiling and the level of gastric damage was examined. The nonselective COX inhibitors caused severe gastric damage while no lesions were observed in the celecoxib-treated rats. The 1H NMR urine spectra were divided into spectral bins (0.04ppm) for global profiling, and a total of 44 endogenous metabolites were assigned for targeted profiling. Multivariate data analyses were performed to recognize the spectral pattern of endogenous metabolites related to NSAIDs using partial least square-discrimination analysis (PLS-DA). The 1H NMR spectra clustered differently according to gastric damage score in global profiling. In targeted profiling, the endogenous metabolites of citrate, allantoin, 2-oxoglutarate, acetate, benzoate, glycine, and trimethylamine N-oxide were selected as putative biomarkers for gastric damage caused by NSAIDs. These putative biomarkers might be useful for predicting the risk of adverse effects caused by NSAIDs in the early stage of drug development process.

Comprehensive 1H NMR metabolic profiling of body fluids for differentiation of meningitis in adults

Meningitis, a morbidly infectious central nervous system pathology is accompanied by acute inflammation of the meninges, causing raised intracranial pressure linked with serious neurological sequelae. To observe the variation in the metabolic profile, that may occur in serum and urine along with CSF in adults using 1H NMR spectroscopy, with an attempt of appropriate and timely treatment regimen. The 1H NMR-based metabolomics has been performed in 115 adult subjects for differentiating bacterial meningitis (BM) and tubercular meningitis (TBM). The discriminant function analysis (DFA) of the three bio-fluids collectively identified 3-hydroxyisovalerate, lactate, glucose, formate, valine, alanine, ketonic bodies, malonate and choline containing compounds (choline and GPC) as significant metabolites among cases versus control group. The differentiation of bacterial meningitis and tuberculous meningitis (BM vs. TBM) can be done on the basis of identification of 3-hydroxyisovalerate, isobutyrate and formate in case of CSF (with a correct classification of 78 %), alanine in serum (correct classification 60 %), valine and acetone in case of urine (correct classification 89.1 %). The NMR spectral bins based orthogonal signal correction principal component analysis score plots of significant metabolites obtained from DFA also provided group classification among cases versus control group in CSF, serum and urine samples. The variable importance in projection scores also identified similar significant metabolites as obtained from DFA, collectively in CSF, serum and urine samples, responsible for differentiation of meningitis. The CSF contained metabolites which are formed during infection and inflammation, and these were also found in significant quantity in serum and urine samples.

TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT.

This work develops a material reconstruction method for spectral CT, namely Total Image Constrained Material Reconstruction (TICMR), to maximize the utility of projection data in terms of both spectral information and high signal-to-noise ratio (SNR). This is motivated by the following fact: when viewed as a spectrally-integrated measurement, the projection data can be used to reconstruct a total image without spectral information, which however has a relatively high SNR; when viewed as a spectrally-resolved measurement, the projection data can be utilized to reconstruct the material composition, which however has a relatively low SNR. The material reconstruction synergizes material decomposition and image reconstruction, i.e., the direct reconstruction of material compositions instead of a two-step procedure that first reconstructs images and then decomposes images. For material reconstruction with high SNR, we propose TICMR with nonlocal total variation (NLTV) regularization. That is, first we reconstruct a total image using spectrally-integrated measurement without spectral binning, and build the NLTV weights from this image that characterize nonlocal image features; then the NLTV weights are incorporated into a NLTV-based iterative material reconstruction scheme using spectrally-binned projection data, so that these weights serve as a high-SNR reference to regularize material reconstruction. Note that the nonlocal property of NLTV is essential for material reconstruction, since material compositions may have significant local intensity variations although their structural information is often similar. In terms of solution algorithm, TICMR is formulated as an iterative reconstruction method with the NLTV regularization, in which the nonlocal divergence is utilized based on the adjoint relationship. The alternating direction method of multipliers is developed to solve this sparsity optimization problem. The proposed TICMR method was validated using both simulated and experimental data. In comparison with FBP and total-variation-based iterative method, TICMR had improved image quality, e.g., contrast-to-noise ratio and spatial resolution.