Optimal Pixel Size Research Articles

38‐3: Design of Random Depolarization Films Based on Modulation Transfer Function Measurements

The random depolarization film (RDF), a polymer film doped with birefringent microparticles, solves polarization problems of displays. To design the RDF that realizes sharper images, the optimal dopant particle size and suitable display's pixel size were investigated based on modulation transfer function (MTF) measurements. The MTFs indicated that the dopant particle size of the RDF and the pixel size of the display should be larger to prevent the sharpness degradation.

Optimization of quasi-hemispherical CdZnTe detectors by means of first principles simulation

In this paper we present the development of quasi-hemispherical gamma-ray detectors based on CdZnTe. Among the possible single-polarity electrode configurations, such as coplanar, pixelated, or virtual Frisch-grid geometries, quasi-hemispherical detectors are the most cost-effective alternative with comparable raw energy resolution in the high and low energy range. The optimal configuration of the sensor in terms of dimension of the crystals and electrode specifications has been first determined by simulations, and successively validated with experimental measures. Spectra from different sources have been acquired to evaluate the detectors performances. Three types of detectors with different CZT volumes have been fabricated, namely 10 × 10 × 5 mm3, 15 × 15 × 10 mm3 and 20 × 20 × 10 mm3. In the case of 10 × 10 × 5 mm3 crystals, the optimum pixel size determined by our simulation tool was confirmed by experiments: the best spectroscopic resolution of 1.3% at 662 keV has been found for a 750 μm diameter pixel detector. The best energy resolution values obtained for the 15 × 15 × 10 mm3 and 20 × 20 × 10 mm3 detectors were respectively 1.7% and 2.7% at 662 keV.

Optimizing the Ultrasound Tongue Image Representation for Residual Network-Based Articulatory-to-Acoustic Mapping.

Within speech processing, articulatory-to-acoustic mapping (AAM) methods can apply ultrasound tongue imaging (UTI) as an input. (Micro)convex transducers are mostly used, which provide a wedge-shape visual image. However, this process is optimized for the visual inspection of the human eye, and the signal is often post-processed by the equipment. With newer ultrasound equipment, now it is possible to gain access to the raw scanline data (i.e., ultrasound echo return) without any internal post-processing. In this study, we compared the raw scanline representation with the wedge-shaped processed UTI as the input for the residual network applied for AAM, and we also investigated the optimal size of the input image. We found no significant differences between the performance attained using the raw data and the wedge-shaped image extrapolated from it. We found the optimal pixel size to be 64 × 43 in the case of the raw scanline input, and 64 × 64 when transformed to a wedge. Therefore, it is not necessary to use the full original 64 × 842 pixels raw scanline, but a smaller image is enough. This allows for the building of smaller networks, and will be beneficial for the development of session and speaker-independent methods for practical applications. AAM systems have the target application of a "silent speech interface", which could be helpful for the communication of the speaking-impaired, in military applications, or in extremely noisy conditions.

Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran

The main goal of this study was to consider and compare the effects of different spatial resolutions of covariates from different sources on predicting SOC in a semi-arid region located in the west of Iran. For this purpose, 67 topsoil samples (0–30 cm) with the measured SOC contents were used as the dependent variable. The covariates controlling the SOC content from different sources were provided in two scenarios. For the first scenario (scenario I), six covariate sets with spatial resolution ranging from 2 to 30 m, and original and aggregated pixel sizes were prepared using the digital elevation models (DEMs) and remote sensing data to predict SOC. In the second scenario (scenario II), the available legacy data, including geology, land use and soil texture maps, were prepared with compatible spatial resolution and added to each covariate set provided for scenario I. After feature selection analysis, the modelling processes were performed using two machine learning models, namely, Random Forest (RF) and Support Vector Machine (SVM). The results of performance analysis, as obtained by leave one out cross validation (LOOCV), showed that the RF and covariate set B (with 10 m spatial resolution) in scenario I, with R2 = 0.21, CCC = 0.41, MAE = 0.26 and RMSE = 0.34%, and also, in scenario II, with R2 = 0.32, CCC = 0.51, MAE = 0.24, and RMSE = 0.32%, had a better performance in predicting SOC. In addition, the remote sensing data were identified as the most important variables controlling the spatial distribution of SOC. Finally, by using the RF model as the superior model, the SOC map provided by the covariate set B in scenario II, which was the combination of the three types of covariates (DEM, remote sensing data and legacy data), was shown to have the lowest uncertainty in comparison to the SOC provided by the covariate set B in scenario I. In general, our results showed that the model type, source, resolution and the combination of these variables could greatly influence the prediction outputs. In fact, the SOC map provided with the combination of parsimonious variables at the optimal pixel size could help decision-making in environmental resources management.

Handwritten digit recognition by three-layer diffractive neural network

Optical diffractive neural network (ODNN) uses light wave as a computing medium to perform the inference and prediction function of neural network, which has the advantages of high speed, low power consumption, and parallel processing. In this work, an ODNN with only three layers of phase modulation is designed, and a method to improve the recognition performance of ODNN based on the first-order spectral distribution of targets is proposed. Using this method, the parameters of a three-layer ODNN are effectively optimized and the optimal pixel size, diffraction distance, and method for image preprocessing are obtained. The three-layer ODNN designed in this work has a recognition accuracy rate of 95.3% for MNIST (handwritten digit set), compared with the 91.75% accuracy achieved by the five-layer ODNN in the reference (Lin X, Rivenson Y, Yardimci N T, Veli M, Luo Y, Jarrahi M, Ozcan A <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1126/science.aat8084">2018 <i>Science</i> <b>361</b> 1004</ext-link>). In addition, the system volume is greatly reduced and the system structure is simplified. Combined with the advantages of high speed and low power consumption, it has huge potential applications in the fields such as edge computing in the future.

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.

Determination of optimum pixel size and slice thickness for tractography and ulnar nerve diffusion tensor imaging at the cubital tunnel using 3T MRI.

Small peripheral nerve tractography is challenging because of the trade-off among resolution, image acquisition time, and signal-to-noise ratio. To optimize pixel size and slice thickness parameters for fiber tractography and diffusion tensor imaging (DTI) of the ulnar nerve at the cubital tunnel using 3T magnetic resonance imaging (MRI). Fifteen healthy volunteers (mean age 30 ± 6.8 years) were recruited prospectively. Axial T2-weighted and DTI scans were acquired, covering the cubital tunnel, using different pixel sizes and slice thicknesses. Three-dimensional (3D) nerve tractography was evaluated for the median number and length of the reconstructed fiber tracts and visual score from 0 to 5. Two-dimensional (2D) cross-sectional DTI was evaluated for fractional anisotropy (FA) values throughout the length of the ulnar nerve. A pixel size of 1.3 mm2 revealed the highest number of reconstructed nerve fibers compared to that of 1.1 mm2 (P = 0.048), with a good visual score. A slice thickness of 4 mm had the highest number of reconstructed nerve fibers and visual score compared with other thicknesses (all P < 0.05). In 2D cross-sectional images, the median FA values were in the range of 0.40-0.63 at the proximal, central, and distal portions of the cubital tunnel. Inter-observer agreement for all parameters was good to excellent. For fiber tractography and DTI of the ulnar nerve at the cubital tunnel, optimal image quality was obtained using a 1.3-mm2 pixel size and 4-mm slice thickness under MR parameters of this study at 3T.

Geographical Origin Discrimination of White Rice Based on Image Pixel Size Using Hyperspectral Fluorescence Imaging Analysis

Geographical origin discrimination of white rice is an important endeavor in preventing illegal distribution of white rice and regulating and standardizing food safety and quality assurance. The aim of this study was to develop a method for geographical origin discrimination between South Korean and Chinese rice using a hyperspectral fluorescence imaging technique and multivariate analysis. Hyperspectral fluorescence images of South Korean and Chinese rice samples were obtained in the wavelength range of 420 nm to 780 nm with intervals of 4.8 nm using 365 nm wavelength ultraviolet-A excitation light. Partial least squares discriminant analysis models were developed and applied to the acquired image to determine the geographical origins of the rice samples. In addition, various pre-processing techniques were applied to improve the discrimination accuracy. Accordingly, the pixel size of the hyperspectral image was determined. The results revealed that the optimum pixel size of the hyperspectral image that was above 7 mm × 7 mm showed a high discrimination accuracy. Moreover, the geographical origin discrimination model that applied the first-order derivative achieved a high discrimination accuracy of 98.89%. The results of this study showed that hyperspectral fluorescence imaging technology can be used to quickly and accurately discriminate the geographical origins of white rice.

Computational Protocol for Assessing the Optimal Pixel Size to Improve the Accuracy of Single-particle Cryo-electron Microscopy Maps.

Cryo-electron microscopy (cryo-EM) single-particle analysis has come a long way in achieving atomic-level resolution when imaging biomolecules. To obtain the best possible three-dimensional (3D) structure in cryo-EM, many parameters have to be carefully considered. Here we address the often-overlooked parameter of the pixel size, which describes the magnification of the image produced by the experiment. While efforts are made to refine and validate this parameter in the analysis of cryo-EM experimental data, there is no systematic protocol in place. Since the pixel size parameter can have an impact on the resolution and accuracy of a cryo-EM map, and the atomic resolution 3D structure models derived from it, we propose a computational protocol to estimate the appropriate pixel size parameter. In our protocol, we fit and refine atomic structures against cryo-EM maps at multiple pixel sizes. The resulting fitted and refined structures are evaluated using the GOAP (generalized orientation-dependent, all-atom statistical potential) score, which we found to perform better than other commonly used functions, such as Molprobity and the correlation coefficient from refinement. Finally, we describe the efficacy of this protocol in retrieving appropriate pixel sizes for several examples; simulated data based on yeast elongation factor 2 and experimental data from Gro-EL chaperone, beta-galactosidase, and the TRPV1 ion channel.

A Framework for Land Use Scenes Classification Based on Landscape Photos

Space-earth integrated stereoscopic mapping promotes the progress of earth observation technologies. The method which combined remote sensing images with zenith perspectives and ground-level landscape photos with slanted viewing angles improves the efficiency and accuracy of land surveys. Recently, numerous efforts have been devoted to combining deep learning and remote sensing images for the classification of land use scenes. However, improvement of classification accuracy has been limited because of the lack of sectional representation. Landscape photos can describe the cross-sections in detail. For this reason, this study constructed a land-use semantic photo dataset (LSPD) and proposed a land-use classification framework for photos (LUCFP) based on Inception-v4. LSPD was constructed through semantic planning, scene segmentation, supervised iteration transfer learning, and augmentation of photos. LSPD has 1.4 million photos collected from seven geographic regions of China, and covers 13 land-use categories and 44 semantic categories. LUCFP adapts scene segmentation based on depth of field, multisemantic block labeling, and weighting of semantic joint spatial ranges to determine the land use category. To validate LUCFP, nine semantic samples (9×3×2000 photos) were chosen from LSPD, obtaining an overall accuracy of 97.64%. The best photo cropping method was masking, which crops the boundary of the scene labeled by the photo, leading to an accuracy of 90.32%. The optimal pixel size that balances speed and accuracy is 675×675, with speed reaching 30 photos per second with an average accuracy of 93.80%. LUCFP has been successfully applied to the automatic verification of land surveys in China.

Natural Heritage Reconstruction Using Full-Color 3D Printing: A Case Study of the Valley of Five Polish Ponds

The research presented in this paper proposes a method for the development of photorealistic, physical terrain models using full-color 3D printing, along with an efficiency assessment (i.e., cartographic correctness). The rapid development of 3D printing technology in recent years has caused a growth in the number of solutions allowing the automatic or semi-automatic generation of digital terrain representations that can be then 3D printed. Unfortunately, most of these solutions do not consider cartographic principles and their products cannot be referred to as 3D maps. The methodology proposed in this paper takes into account issues related to proper cartographic design, so that highly reliable models can be created. The main challenges identified during the research include choosing the optimal pixel size for Digital Elevation Model (DEM) generation, which is used for landform representation, and integrating raster and vector data. Printing accuracy assessment is of high importance and, thus, a method for its implementation has been proposed. In order to verify the usability of the proposed methodology, the natural heritage site of the Valley of Five Polish Ponds, in the Tatra Mountains, Poland, has been chosen as the case study area. The results suggest that the proposed methodology can be successfully used for the preparation of highly accurate 3D maps that can be used for natural heritage documentation, promotion and visualization, as well as for the purposes of spatial planning and education.

Sea surface wind retrieval in coastal areas by means of Sentinel-1 and numerical weather prediction model data

The present paper applies Synthetic Aperture Radar (SAR) based on Local Gradient-Modified (LG-Mod) algorithm to retrieve wind directions from Sentinel-1 data in the Camargue and the Wadden Sea protected coastal areas. Wind speeds are estimated through the inversion of the C-band MODel 5.N (CMOD5.N) backscattering model. Both Interferometric Wide Swath (IW) and Extra Wide Swath (EW) Level 1 products were evaluated for wind fields retrieval at high (5 km) and medium (12.5 km) output spatial resolutions. SSW fields from Sentinel-1 were compared with Numerical Weather Prediction (NWP) models and in situ data. Exploitation of the LG-Mod provided wind direction with a related marginal error parameter (i.e., MEαROI) which proved useful for selecting the optimal input pixel size of SAR data processing. When compared to in situ data, the selection of the optimal pixel size reduced the Root Mean Squared Error (RMSE) values of LG-Mod wind directions up to 7° and about 45° for Wadden Sea and the Camargue site, respectively. In turn, such reduction provided a decrease of the wind speed RMSE values up to 0.7 m/s and 2.1 m/s, for Wadden Sea and the Camargue site, respectively. In addition, the LG-Mod gave better performance than the global NWP model European Centre for Medium-Range Weather Forecasts (ECMWF) in estimation of wind direction, at 12.5 km output spatial resolution, for both sites. The MEαROI exploitation in the directional analysis of IW and EW products evidenced that at high resolution (5 km) the percentage of reliable wind directions from IW images (84.5%) resulted much larger than that obtained from EW images (30.1%). At medium resolution (12.5 km) instead, the percentage values resulted quite close to each other (99.2% and 86.3%, respectively). IW images proved optimal for high resolution SSW retrieval, whereas EW images suitable for medium resolution. With respect to NWP models, the spectral analysis confirmed the suitability of Sentinel-1 to represent the local wind fields spatial variability in coastal areas, at both high and medium output resolution. Our findings suggest that the combination of the LG-Mod algorithm with NWP models could better resolve spatially wind patterns in complex coastal areas.

Measurement Extraction for a Point Target From an Optical Sensor

This paper considers the measurement extraction for a point target from an optical sensor's focal plane array (FPA) with a dead zone separating neighboring pixels. Assuming that the energy density of the target deposited in the FPA conforms to a Gaussian point spread function and that the noise mean and variance in each pixel are proportional to the pixel area (i.e., according to a Poisson noise model), we derive the Cramer–Rao lower bound (CRLB) for the covariance of the estimated target location. It is observed that that there is an optimal pixel size that minimizes the CRLB for a given dead-zone width, and the maximum likelihood estimator is shown to be efficient via Monte Carlo runs for moderate-to-large signal-to-noise ratios. The test statistic for target detection is derived and it is shown to be a matched filter at the estimated location. The distributions of the test statistic under both hypotheses are derived using some approximations. The detection probability is then obtained.

Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping

Gully erosion has been identified as an important soil degradation process and sediment source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial occurrence of this form of water erosion in the landscape and the most vulnerable areas. In this study, we explored the effects of different pixel sizes on some controlling factors extracted from a digital elevation model and remote sensing data when producing a gully erosion susceptibility map (GESM) of Ekbatan Dam Basin, Hamadan, Iran. An inventory map of the gully landforms was prepared based on global positioning system routes of the gullies, extensive field surveys, and visual interpretations of satellite images obtained from Google Earth. Five data sets with pixel sizes ranging from 2 to 30 m were obtained using topographic attributes and remote sensing data comprising the elevation, slope degree, slope aspect, catchment area, plan curvature, profile curvature, stream power index, topographic position index, topographic wetness index, land use, and normalized difference vegetation index, which can affect the distribution of gully erosion. For each data set, 70% and 30% of the data were selected randomly for calibrating and validating the models, respectively. The statistical relationships between the occurrence of gully erosion and controlling factors were calculated using four machine-learning models, i.e., generalized linear model, boosted regression tree (BRT), multivariate adaptive regression spline, and artificial neural network (ANN). Statistical tests comprising the kappa coefficient and the area under the receiver operating characteristic curve (AUC) were calculated for both the calibration and validation data sets to estimate the optimal pixel size. The results showed that among the data sets with different pixel sizes, the optimal pixel size was 10 m for each model. In addition, the capacity of the four techniques for modeling gully erosion occurrence was quite stable when the calibration and validation samples were changed in the data set. Finally, based on three changes of the calibration and validation data sets with a pixel size of 10 m, the BRT and ANN models obtained outstanding performance (AUC > 0.9), where they had the highest goodness-of-fit and predictive power, and thus the greatest robustness to changes in the calibration/validation data (i.e., lowest sensitivity to altering calibration/validation data). Our results demonstrate the importance of selecting a suitable pixel size when producing a GESM for soil and water management practices.

Spectral resolution and high-flux capability tradeoffs in CdTe detectors for clinical CT.

Photon-counting detectors using CdTe or CZT substrates are promising candidates for future CT systems but suffer from a number of nonidealities, including charge sharing and pulse pileup. By increasing the pixel size of the detector, the system can improve charge sharing characteristics at the expense of increasing pileup. The purpose of this work is to describe these considerations in the optimization of the detector pixel pitch. The transport of x rays through the CdTe substrate was simulated in a Monte Carlo fashion using GEANT4. Deposited energy was converted into charges distributed as a Gaussian function with size dependent on interaction depth to capture spreading from diffusion and Coulomb repulsion. The charges were then collected in a pixelated fashion. Pulse pileup was incorporated separately with Monte Carlo simulation. The Cramér-Rao lower bound (CRLB) of the measurement variance was numerically estimated for the basis material projections. Noise in these estimates was propagated into CT images. We simulated pixel pitches of 250, 350, and 450 microns and compared the results to a photon counting detector with pileup but otherwise ideal energy response and an ideal dual-energy system (80/140kVp with tin filtration). The modeled CdTe thickness was 2mm, the incident spectrum was 140kVp and 500mA, and the effective dead time was 67ns. Charge summing circuitry was not modeled. We restricted our simulations to objects of uniform thickness and did not consider the potential advantage of smaller pixels at high spatial frequencies. At very high x-ray flux, pulse pileup dominates and small pixel sizes perform best. At low flux or for thick objects, charge sharing dominates and large pixel sizes perform best. At low flux and depending on the beam hardness, the CRLB of variance in basis material projections tasks can be 32%-55% higher with a 250 micron pixel pitch compared to a 450 micron pixel pitch. However, both are about four times worse in variance than the ideal photon counting detector. The optimal pixel size depends on a number of factors such as x-ray technique and object size. At high technique (140kVp/500mA), the ratio of variance for a 450 micron pixel compared to a 250 micron pixel size is 2126%, 200%, 97%, and 78% when imaging 10, 15, 20, and 25cm of water, respectively. If 300mg/cm2 of iodine is also added to the object, the variance ratio is 117%, 91%, 74%, and 72%, respectively. Nonspectral tasks, such as equivalent monoenergetic imaging, are less sensitive to spectral distortion. The detector pixel size is an important design consideration in CdTe detectors. Smaller pixels allow for improved capabilities at high flux but increase charge sharing, which in turn compromises spectral performance. The optimal pixel size will depend on the specific task and on the charge shaping time.

Modeling and evaluation of a high-resolution CMOS detector for cone-beam CT of the extremities.

Quantitative assessment of trabecular bone microarchitecture in extremity cone-beam CT (CBCT) would benefit from the high spatial resolution, low electronic noise, and fast scan time provided by complementary metal-oxide semiconductor (CMOS) x-ray detectors. We investigate the performance of CMOS sensors in extremity CBCT, in particular with respect to potential advantages of thin (<0.7mm) scintillators offering higher spatial resolution. A cascaded systems model of a CMOS x-ray detector incorporating the effects of CsI:Tl scintillator thickness was developed. Simulation studies were performed using nominal extremity CBCT acquisition protocols (90kVp, 0.126mAs/projection). A range of scintillator thickness (0.35-0.75mm), pixel size (0.05-0.4mm), focal spot size (0.05-0.7mm), magnification (1.1-2.1), and dose (15-40mGy) was considered. The detectability index was evaluated for both CMOS and a-Si:H flat-panel detector (FPD) configurations for a range of imaging tasks emphasizing spatial frequencies associated with feature size aobj. Experimental validation was performed on a CBCT test bench in the geometry of a compact orthopedic CBCT system (SAD=43.1cm, SDD=56.0cm, matching that of the Carestream OnSight 3D system). The test-bench studies involved a 0.3mm focal spot x-ray source and two CMOS detectors (Dalsa Xineos-3030HR, 0.099mm pixel pitch) - one with the standard CsI:Tl thickness of 0.7mm (C700) and one with a custom 0.4mm thick scintillator (C400). Measurements of modulation transfer function (MTF), detective quantum efficiency (DQE), and CBCT scans of a cadaveric knee (15mGy) were obtained for each detector. Optimal detectability for high-frequency tasks (feature size of ~0.06mm, consistent with the size of trabeculae) was ~4× for the C700 CMOS detector compared to the a-Si:H FPD at nominal system geometry of extremity CBCT. This is due to ~5× lower electronic noise of a CMOS sensor, which enables input quantum-limited imaging at smaller pixel size. Optimal pixel size for high-frequency tasks was <0.1mm for a CMOS, compared to ~0.14mm for an a-Si:H FPD. For this fine pixel pitch, detectability of fine features could be improved by using a thinner scintillator to reduce light spread blur. A 22% increase in detectability of 0.06mm features was found for the C400 configuration compared to C700. An improvement in the frequency at 50% modulation (f50 ) of MTF was measured, increasing from 1.8lp/mm for C700 to 2.5lp/mm for C400. The C400 configuration also achieved equivalent or better DQE as C700 for frequencies above ~2mm-1 . Images of cadaver specimens confirmed improved visualization of trabeculae with the C400 sensor. The small pixel size of CMOS detectors yields improved performance in high-resolution extremity CBCT compared to a-Si:H FPDs, particularly when coupled with a custom 0.4mm thick scintillator. The results indicate that adoption of a CMOS detector in extremity CBCT can benefit applications in quantitative imaging of trabecular microstructure in humans.

Raster-Mode Continuous-Flow Liquid Microjunction Mass Spectrometry Imaging of Proteins in Thin Tissue Sections.

Mass spectrometry imaging by use of continuous-flow liquid microjunction sampling at discrete locations (array mode) has previously been demonstrated. In this Letter, we demonstrate continuous-flow liquid microjunction mass spectrometry imaging of proteins from thin tissue sections in raster mode and discuss advantages (a 10-fold reduction in analysis time) and challenges (suitable solvent systems, data interpretation) of the approach. Visualization of data is nontrivial, requiring correlation of solvent-flow, mass spectral data acquisition rate, data quality, and liquid microjunction sampling area. The latter is particularly important for determining optimum pixel size. The minimum achievable pixel size is related to the scan time of the instrument used. Here we show a minimum achievable pixel size of 50 μm (x-dimension) when using an Orbitrap Elite; however a pixel size of 600 μm is recommended in order to minimize the effects of oversampling on image accuracy.

Impact of noise sources and optical design on defect detection sensitivity in extreme ultraviolet actinic pattern inspection tool

We discuss the impact of various noise sources and the optical design in bright field extreme ultraviolet (EUV) actinic inspection of mask features for defects in the patterned absorber. It is shown that an optimum pixel size is needed to maximize the defect signal-to-noise ratio (SNR) to balance the trade-off in increasing signal strength with shot noise from defect signal and the background pattern intensity (mask layout image) and speckle noise from the mask blank roughness. Moreover, we consider defocus showing that the EUV mask phase effect has an asymmetric impact on pattern defect SNR’s through-focus behavior. The impact of defocus limits inspection performance based on defect SNR. Using critical defect sizes in a case study, we show the defect SNR performance of the limiting case and discuss the possibility of utilizing a nominal defocus in the inspection system to leverage the phase effect of EUV mask absorber to improve the defect SNR. A 50% improvement in defect SNR is shown to be possible by introducing a −50 nm nominal defocus into the bright field inspection system.

Photon-counting hexagonal pixel array CdTe detector: Spatial resolution characteristics for image-guided interventional applications.

High-resolution, photon-counting, energy-resolved detector with fast-framing capability can facilitate simultaneous acquisition of precontrast and postcontrast images for subtraction angiography without pixel registration artifacts and can facilitate high-resolution real-time imaging during image-guided interventions. Hence, this study was conducted to determine the spatial resolution characteristics of a hexagonal pixel array photon-counting cadmium telluride (CdTe) detector. A 650 μm thick CdTe Schottky photon-counting detector capable of concurrently acquiring up to two energy-windowed images was operated in a single energy-window mode to include photons of 10 keV or higher. The detector had hexagonal pixels with apothem of 30 μm resulting in pixel pitch of 60 and 51.96 μm along the two orthogonal directions. The detector was characterized at IEC-RQA5 spectral conditions. Linear response of the detector was determined over the air kerma rate relevant to image-guided interventional procedures ranging from 1.3 nGy/frame to 91.4 μGy/frame. Presampled modulation transfer was determined using a tungsten edge test device. The edge-spread function and the finely sampled line spread function accounted for hexagonal sampling, from which the presampled modulation transfer function (MTF) was determined. Since detectors with hexagonal pixels require resampling to square pixels for distortion-free display, the optimal square pixel size was determined by minimizing the root-mean-squared-error of the aperture functions for the square and hexagonal pixels up to the Nyquist limit. At Nyquist frequencies of 8.33 and 9.62 cycles/mm along the apothem and orthogonal to the apothem directions, the modulation factors were 0.397 and 0.228, respectively. For the corresponding axis, the limiting resolution defined as 10% MTF occurred at 13.3 and 12 cycles/mm, respectively. Evaluation of the aperture functions yielded an optimal square pixel size of 54 μm. After resampling to 54 μm square pixels using trilinear interpolation, the presampled MTF at Nyquist frequency of 9.26 cycles/mm was 0.29 and 0.24 along the orthogonal directions and the limiting resolution (10% MTF) occurred at approximately 12 cycles/mm. Visual analysis of a bar pattern image showed the ability to resolve close to 12 line-pairs/mm and qualitative evaluation of a neurovascular nitinol-stent showed the ability to visualize its struts at clinically relevant conditions. Hexagonal pixel array photon-counting CdTe detector provides high spatial resolution in single-photon counting mode. After resampling to optimal square pixel size for distortion-free display, the spatial resolution is preserved. The dual-energy capabilities of the detector could allow for artifact-free subtraction angiography and basis material decomposition. The proposed high-resolution photon-counting detector with energy-resolving capability can be of importance for several image-guided interventional procedures as well as for pediatric applications.

WE‐G‐204‐03: Photon‐Counting Hexagonal Pixel Array CdTe Detector: Optimal Resampling to Square Pixels

Purpose:Detectors with hexagonal pixels require resampling to square pixels for distortion‐free display of acquired images. In this work, the presampling modulation transfer function (MTF) of a hexagonal pixel array photon‐counting CdTe detector for region‐of‐interest fluoroscopy was measured and the optimal square pixel size for resampling was determined.Methods:A 0.65mm thick CdTe Schottky sensor capable of concurrently acquiring up to 3 energy‐windowed images was operated in a single energy‐window mode to include ≥10 KeV photons. The detector had hexagonal pixels with apothem of 30 microns resulting in pixel spacing of 60 and 51.96 microns along the two orthogonal directions. Images of a tungsten edge test device acquired under IEC RQA5 conditions were double Hough transformed to identify the edge and numerically differentiated. The presampling MTF was determined from the finely sampled line spread function that accounted for the hexagonal sampling. The optimal square pixel size was determined in two ways; the square pixel size for which the aperture function evaluated at the Nyquist frequencies along the two orthogonal directions matched that from the hexagonal pixel aperture functions, and the square pixel size for which the mean absolute difference between the square and hexagonal aperture functions was minimized over all frequencies up to the Nyquist limit.Results:Evaluation of the aperture functions over the entire frequency range resulted in square pixel size of 53 microns with less than 2% difference from the hexagonal pixel. Evaluation of the aperture functions at Nyquist frequencies alone resulted in 54 microns square pixels. For the photon‐counting CdTe detector and after resampling to 53 microns square pixels using quadratic interpolation, the presampling MTF at Nyquist frequency of 9.434 cycles/mm along the two directions were 0.501 and 0.507.Conclusion:Hexagonal pixel array photon‐counting CdTe detector after resampling to square pixels provides high‐resolution imaging suitable for fluoroscopy.