Standard Deviation Of Background Research Articles

Unpaired data training enables super-resolution confocal microscopy from low-resolution acquisitions

Supervised deep-learning models have enabled super-resolution imaging in several microscopic imaging modalities, increasing the spatial lateral bandwidth of the original input images beyond the diffraction limit. Despite their success, their practical application poses several challenges in terms of the amount of training data and its quality, requiring the experimental acquisition of large, paired databases to generate an accurate generalized model whose performance remains invariant to unseen data. Cycle-consistent generative adversarial networks (cycleGANs) are unsupervised models for image-to-image translation tasks that are trained on unpaired datasets. This paper introduces a cycleGAN framework specifically designed to increase the lateral resolution limit in confocal microscopy by training a cycleGAN model using low- and high-resolution unpaired confocal images of human glioblastoma cells. Training and testing performances of the cycleGAN model have been assessed by measuring specific metrics such as background standard deviation, peak-to-noise ratio, and a customized frequency content measure. Our cycleGAN model has been evaluated in terms of image fidelity and resolution improvement using a paired dataset, showing superior performance than other reported methods. This work highlights the efficacy and promise of cycleGAN models in tackling super-resolution microscopic imaging without paired training, paving the path for turning home-built low-resolution microscopic systems into low-cost super-resolution instruments by means of unsupervised deep learning.

Dual projection generalized sidelobe canceller based on mixed signal subspace for ultrasound imaging.

In this paper, we propose a dual projection generalized sidelobe canceller (DPGSC) based on mixed subspace (MS) for ultrasound imaging, which aims to improve the speckle signal-noise-ratio (sSNR) and decrease the dark-region artifacts. A mixed signal subspace based on the correlation between the desired steering vector and the eigenvectors is constructed to further optimize the desired steering vector and the final weight vector. The simulated and experimental results show that the proposed method can greatly improve the speckle uniformity. In the geabr_0 experiment, the standard deviation of background and sSNR of MS-DPGSC can be improved by 48.07% and 58.49% more than those of eigenspace-based generalized sidelobe canceller (ESGSC). Furthermore, for a hyperechoic target, the maximal improvement of contrast ratio is 95.29%. In terms of anechoic cyst, the contrast-to-noise ratio of MS-DPGSC is increased by 123.08% than that of ESGSC. The rat mammary tumor experimental data show that the proposed method has better comprehensive imaging effect than traditional generalized sidelobe cancellers and ESGSCs.

Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images.

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.

Anatomically aided PET image reconstruction using deep neural networks.

The developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning three-dimensional (3D) convolutional neural network (CNN) to extract modality-specific features from PET/CT image pairs and integrate complementary features into an iterative reconstruction framework to improve PET image reconstruction. We used a pretrained deep neural network to represent PET images. The network was trained using low-count PET and CT image pairs as inputs and high-count PET images as labels. This network was then incorporated into a constrained maximum likelihood framework to regularize PET image reconstruction. Two different network structures were investigated for the integration of anatomical information from CT images. One was a multichannel CNN, which treated PET and CT volumes as separate channels of the input. The other one was multibranch CNN, which implemented separate encoders for PET and CT images to extract latent features and fed the combined latent features into a decoder. Using computer-based Monte Carlo simulations and two real patient datasets, the proposed method has been compared with existing methods, including the maximum likelihood expectation maximization (MLEM) reconstruction, a kernel-based reconstruction and a CNN-based deep penalty method with and without anatomical guidance. Reconstructed images showed that the proposed constrained ML reconstruction approach produced higher quality images than the competing methods. The tumors in the lung region have higher contrast in the proposed constrained ML reconstruction than in the CNN-based deep penalty reconstruction. The image quality was further improved by incorporating the anatomical information. Moreover, the liver standard deviation was lower in the proposed approach than all the competing methods at a matched lesion contrast. The supervised co-learning strategy can improve the performance of constrained maximum likelihood reconstruction. Compared with existing techniques, the proposed method produced a better lesion contrast versus background standard deviation trade-off curve, which can potentially improve lesion detection.

Validating lane drifts as a predictive measure of drug or sleepiness induced driving impairment

BackgroundStandard deviation of lateral position (SDLP) has been accepted as a reliable parameter for measuring driving impairment due to lowered vigilance caused by sleepiness or the use of sedating drugs. Recently, lane drifts were proposed as an additional outcome measure quantifying momentary lapses of attention. The purpose of this study was to validate lane drifts as outcome measure of driver impairment in a large data pool from two independent research centers.MethodsData from 11 placebo-controlled studies that assessed the impact of alcohol, hypnotics, and sleep deprivation on actual driving performance were pooled. In total, 717 on-the-road tests performed by 315 drivers were subjected to an automated algorithm to detect occurrences of lane drifts. Lane drifts were defined as deviations > 100 cm from the mean (LDmlp) and from the absolute lateral position (LDalp) for 8 s.ResultsThe number of LDmlp was low and did not differ between treatments and baseline, i.e., 14 vs. 3 events, respectively. LDalp were frequent and significantly higher during treatment relative to baseline, i.e., 1646 vs. 470 events. The correlation between LDalp and SDLP in the treatment conditions was very high (rs = 0.77). The frequency of the occurrence of treatment-induced lane drifts however depended on baseline SDLP of drivers, whereas treatment-induced changes in SDLP occurred independent of baseline SDLP.ConclusionLDmlp is not useful as an outcome measure of driver impairment due to its rare occurrence, even when treatment-induced increments in SDLP are evident. Treatment effects on LDalp and SDLP are closely related.

Effective Automatic Foreground Motion Detection Using the Statistic Information of Background

In this paper, we proposed and implemented the effective automatic foreground motion detection algorithm that detect the foreground motion by analyzing the digital video data that captured by the network camera. We classified the background as moving background, fixed background and normal background based on the standard deviation of background and used it to detect the foreground motion. According to the result of experiment, our algorithm decreased the fault detection of the moving background and increased the accuracy of the foreground motion detection. Also it could extract foreground more exactly by using the statistic information of background in the phase of our foreground extraction.

WE-AB-204-01: Performance Characterization of Regularized-Reconstruction Algorithm for 90Y PET/CT Images

Purpose: 9⁰Y PET/CT imaging and quantification have recently been suggested as an approach of treatment verification. However, due to low positron yield (32ppm), the 9⁰Y-PET/CT images are very noisy. Iterative reconstruction techniques that employ regularization, e.g. block sequential regularized expectation maximization (BSREM) algorithm (recently implemented on GE scanners – QClear™), has the potential to increase quantitative accuracy with lower noise penalty compared to OSEM. Our aim is to investigate the performance of RR algorithms in 9⁰Y PET/CT studies. Methods: A NEMA IEC phantom filled with 3GBq 9⁰YCl₂ (to simulate patient treatment) was imaged on GE-D690 for 1800s/bed. The sphere-to-background ratio of 7. The data were reconstructed using OSEM and BSREM with PSF modeling and TOF correction while varying the iterations (IT) from 1–6 with fixed 24subsets. For BSREM, the edge-preservation parameter (γ ) was 2 and the penalty-parameters (β) was varied 350–950. In all cases a post-reconstruction filter of 5.2mm (2pixel) transaxial and standard z-axis were used. Sphere average activity concentration (AC) and background standard deviation (SD) were then calculated from VOIs drawn in the spheres and background. Results: Increasing IT from 1to6, the %SD in OSEM increased from 30% to 80%, whereas %SD in BSREM images increased by <5% for all βs. BSREM with β=350 didn't offer any improvement over OSEM (convergence of mean achieved at 2 IT, in this study). Increasing β from 350 to 950 reduced the AC accuracy of small spheres (<20mm) by 10% and noise from 40% to 20%, which resulted in CNR increase from 11 to 17. Conclusion: In count-limited studies such as 9⁰Y PET/CT, BSREM can be used to suppress image noise and increase CNR at the expense of a relatively small decrease of quantitative accuracy. The BSREM parameters need to be optimized for each study depending on the radionuclides and count densities. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA13898 and was also partly supported by General Electric.

Total variation minimization filter for DBT imaging.

Compressed sensing (CS) is a new approach in medical imaging which allows a sparse image to be reconstructed from undersampled data. Total variation (TV) based minimization algorithms are the one CS technique that has achieved great success due to its virtue of preserving edges while reducing image noise. The purpose of this work is to implement and evaluate the performance of a TV minimization filter able to increase the signal difference to noise ratio (SDNR) of digital breast tomosynthesis (DBT) images. Assuming a Poisson noise model, the authors present a practical methodology, based on Rudin, Osher, and Fatemi model, which directly applies a TV minimization filter to real phantom and clinical DBT images. Different moments of filter application (before and after image reconstruction) and the suitable Lagrange multiplier (λ) to be used in filter equation are studied. Also, the relationship between background standard deviation (σB) of unfiltered images and optimal λ values is determined, in order to maximize the SDNR. Qualitative and quantitative analyses are conducted between unfiltered and filtered images and between the different moments of filter application. The proposed methodology is also tested with one clinical DBT data set. Using phantom data, when the filter is applied to the projections, the authors observed a decrease of 31.34% in TV and an increase of 5.29% and 5.44% in SDNR and full width at half maximum (FWHM), respectively. When applied after reconstruction, a decrease of 35.48% and 2.59% was achieved for TV and FWHM, respectively, and an increase of 8.32% for SDNR. For each moment of filter application, the optimal λ value found through a comprehensive study was λ = 85 and λ = 60 when the filter is applied before and after reconstruction, respectively. The best fit found for the relationship between σB and the corresponding λ values that allowed the highest filtered SDNR was the logarithmic adjustment. The difference between the λ values obtained by the first approach and the logarithmic adjustment ranges from 0.11% (filter applied before reconstruction) to 2.54% (filter applied after reconstruction). On the other hand, a decrease of 37.63% and 2.42% in TV and FWHM, respectively, and an increase of 24.39% in SDNR were obtained when the filter is applied to clinical data. This great minimization is present through a visual inspection of unfiltered and filtered clinical images, where areas with higher noise level become smoother while preserving edges and details of the structures. An optimized digital filter for TV minimization in DBT imaging has been presented. The reliability of a logarithmic relation found between σB and λ values was confirmed and can be used in future work. Both quantitative and qualitative analyses performed in a clinical DBT image confirmed the relevance of this approach in improving image quality in DBT imaging. The results obtained are very encouraging about increasing SDNR in a short time and preserving the principal variations in image, the structures' boundary.

TU‐A‐9A‐02: Analysis of Variations in Clinical Doppler Ultrasound Peak Velocity Measurements

Purpose:Doppler ultrasound (US) peak velocity (Vmax) measurements show considerable variations due to intrinsic spectral broadening with different scanning techniques, machines and manufacturers. We developed a semi‐automated Vmax estimation method and used this method to investigate the performance of a US system for clinical Doppler Vmax measurement.Methods:Semi‐automated Vmax is defined as the velocity at which the computed mean spectral profile falls to within 1 background standard deviation of the background mean. GE LOGIQ E9 system with 9L and ML6‐15 probes were studied with steady flow (5.3 – 12.5 ml/s) in a Gammex OPTIMIZER 1425A phantom. All Doppler spectra were acquired by 1 operator at the distal end of 5 mm angular tube using a modified clinical carotid artery protocol. Repeatability and variation of Vmax to scanning parameters and probes were analyzed and reported as percentage, i.e. (max‐min)/mean.Results:Vmax estimation had good repeatability (3.1% over 6 days for 9L, and 3.6% for ML6‐15). For 9L probe, varying gain, compression, scale, SV depth and length, and frequency had minimal impact on Vmax (all variations less than 4.0%). Beam steering had slightly higher influence (largest variations across flow rates were 4.9% for 9L and 6.9% for ML6‐15). For both probes, Doppler angle had the greatest effect on Vmax. Percentage increase of Vmax was largely independent of actual flow rates. For Doppler angle varied from 30 to 60°, Vmax increased 24% for 9L, and 20% for ML6‐15. Vmax measured by ML6‐15 were lower than that by 9L at each Doppler angle with differences less than 5%.Conclusion:The proposed Vmax estimation method is shown to be a useful tool to evaluate clinical Doppler US system performance. For the tested system and probes, Doppler angle had largest impact in measured Vmax.

Delimitated strike artifacts from FBP using a robust morphological structure operation

When using cardiac nuclear medicine images for diagnosis, the filtered back-projection (FBP) algorithm can reconstruct positron emission tomography (PET) images under low count rates. However, background strike artifacts in PET images are affected by diagnostic judgment. Hence, this study developed a robust method of removing background strike artifacts from FBP images without reducing image quality. A Jaszczak anthropomorphic torso phantom and a laboratory rabbit were used for performance tests of the proposed method. Parallel computing was applied to optimize the mask size of morphological structure operator (MSO) by minimizing the background standard deviation (Std). The optimal MSO mask size for the evaluated Jaszczak phantom was 3×3. The FBP images processed by MSO had significantly reduced strike artifacts measured by background Std (P=1E-5). After MSO processing, the time activity curve (TAC) of FBP images was stable and resembled the original FBP images (P=0.5). The proposed approach is highly stable and reduces noise by 13.08±2.32 in FBP images after MSO processing with 3×3 mask.

SU‐E‐I‐53: Radiation Dose‐Dependent Characteristics of CT Images Reconstructed with Statistical Iterative Algorithms

Purpose: To characterize the dependence of image noise, contrast‐to‐noise and noise structure in CT images on algorithm weighting‐factors (AWF) and radiation dose for three different commercially available iterative reconstruction algorithms. Methods: Images were acquired using the 20 cm ACR CT accreditation phantom on scanners manufactured by three different vendors at four different CTDI levels from approximately 10 mGy‐70 mGy. Images were reconstructed using conventional filtered backprojection (FBP) image reconstruction and with each vendor's proprietary statistical iterative reconstruction (SIR) algorithms: ASiR™, SAFIRE™ and iDOSE™. At each CTDI level, images were reconstructed with multiple AWF. Image noise (standard deviation of background near contrast objects), contrast‐to‐noise (CNR) and Noise Power Spectrum (NPS) were plotted as a function of both weighting factor and CTDI. Results: All SIR algorithms demonstrated significant improvement in both noise reduction and CNR, typically 30%– 60% depending upon the AWF. NPS was found to be a function of both reconstruction kernel and SIR weighting factor. All SIR reconstructed images showed the same general shift in noise power to lower spatial frequencies with increased AWF regardless of vendor. With dose reduction as high as 80%, heavily weighted SIR reconstructed images can produce CNR values comparable to images produced with 100% dose levels. Conclusion: All three SIR algorithms provide significant noise reduction and CNR improvement with minimal reduction of spatial resolution. The noise‐reduction performance with different algorithm weighting was approximately linear for all SIR algorithms. For all CTDI levels measured, each algorithm was capable of providing increased CNR by at least a factor of 90%. However, the weighting factors are vendor specific, thus a direct comparison is not appropriate. The use of greater SIR weighting factors also resulted in a shift in the NPS to lower spatial frequencies, producing a background texture that could potentially affect image interpretation.

Implementation of dual‐energy technique for virtual monochromatic and linearly mixed CBCTs

To implement dual-energy imaging technique for virtual monochromatic (VM) and linearly mixed (LM) cone beam CTs (CBCTs) and to demonstrate their potential applications in metal artifact reduction and contrast enhancement in image-guided radiation therapy (IGRT). A bench-top CBCT system was used to acquire 80 kVp and 150 kVp projections, with an additional 0.8 mm tin filtration. To implement the VM technique, these projections were first decomposed into acrylic and aluminum basis material projections to synthesize VM projections, which were then used to reconstruct VM CBCTs. The effect of VM CBCT on the metal artifact reduction was evaluated with an in-house titanium-BB phantom. The optimal VM energy to maximize contrast-to-noise ratio (CNR) for iodine contrast and minimize beam hardening in VM CBCT was determined using a water phantom containing two iodine concentrations. The LM technique was implemented by linearly combining the low-energy (80 kVp) and high-energy (150 kVp) CBCTs. The dose partitioning between low-energy and high-energy CBCTs was varied (20%, 40%, 60%, and 80% for low-energy) while keeping total dose approximately equal to single-energy CBCTs, measured using an ion chamber. Noise levels and CNRs for four tissue types were investigated for dual-energy LM CBCTs in comparison with single-energy CBCTs at 80, 100, 125, and 150 kVp. The VM technique showed substantial reduction of metal artifacts at 100 keV with a 40% reduction in the background standard deviation compared to a 125 kVp single-energy scan of equal dose. The VM energy to maximize CNR for both iodine concentrations and minimize beam hardening in the metal-free object was 50 keV and 60 keV, respectively. The difference of average noise levels measured in the phantom background was 1.2% between dual-energy LM CBCTs and equivalent-dose single-energy CBCTs. CNR values in the LM CBCTs of any dose partitioning are better than those of 150 kVp single-energy CBCTs. The average CNR for four tissue types with 80% dose fraction at low-energy showed 9.0% and 4.1% improvement relative to 100 kVp and 125 kVp single-energy CBCTs, respectively. CNRs for low-contrast objects improved as dose partitioning was more heavily weighted toward low-energy (80 kVp) for LM CBCTs. Dual-energy CBCT imaging techniques were implemented to synthesize VM CBCT and LM CBCTs. VM CBCT was effective at achieving metal artifact reduction. Depending on the dose-partitioning scheme, LM CBCT demonstrated the potential to improve CNR for low contrast objects compared to single-energy CBCT acquired with equivalent dose.

Mid-infrared observations of the transitional disks around DH Tauri, DM Tauri, and GM Aurigae

Aims: We present mid-infrared observations and photometry of the transitional disks around the young stellar objects DH Tau, DM Tau, and GM Aur, obtained with VISIR/VLT in N band. Our aim is to resolve the inner region and the large-scale structures of these transitional disks, carrying potential signatures of intermediate or later stages of disk evolution and ongoing planet formation. Methods: We use the simultaneously observed standard-stars as PSF reference to constrain the radial flux profiles of our target objects. Subtracting the obtained standard-star profile from the corresponding science object profile yields the flux residuals produced by the star-disk system. A detection threshold takes into account the background standard deviation and also the seeing variations during the observations to evaluate the significance of these flux residuals. On the basis of a simple model for the dust re-emission, we derive constraints on the inner radius of the dust disk. Results: We spatially resolve the transitional disk around GM Aur and determine an inner-disk hole radius of 20.5(+1.0,-0.5) AU. The circumstellar disks around DH Tau and DM Tau are not spatially resolved but we are able to constrain the inner-disk hole radius to <15.5(+9.0,-2.0) AU and <15.5(+0.5,-0.5) AU, respectively. The performed photometry yields fluxes of 178+-31 mJy for DH Tau, 56+-6 mJy for DM Tau, and 229+-14 mJy for GM Aur.

Microarray-based Transcriptional and Epigenetic Profiling of Matrix Metalloproteinases, Collagens, and Related Genes in Cancer

Epigenetic parameters (DNA methylation, histone modifications, and miRNAs) play a significant role in cancer. To identify the common epigenetic signatures of both the individual matrix metalloproteinases (MMPs) and the additional genes, the function of which is also linked to proteolysis, migration, and tumorigenesis, we performed epigenetic profiling of 486 selected genes in unrelated non-migratory MCF-7 breast carcinoma and highly migratory U251 glioma cells. Genome-wide transcriptional profiling, quantitative reverse transcription-PCR, and microRNA analyses were used to support the results of our epigenetic studies. Transcriptional silencing in both glioma and breast carcinoma cells predominantly involved the repressive histone H3 Lys-27 trimethylation (H3K27me3) mark. In turn, epigenetic stimulation was primarily performed through a gain in the histone H3 Lys-4 dimethylation (H3K4me2) and H3 hyperacetylation and by a global reduction of H3K27me3. Inactive pro-invasive genes in MCF-7 cells but not in U251 cells frequently exhibited a stem cell-like bivalent mark (enrichment in both H3K27me3 and H3K4me2), a characteristic of developmental genes. In contrast with other MMPs, MMP-8 was epigenetically silenced in both cell types, thus providing evidence for the strict epigenetic control of this anti-tumorigenic proteinase in cancer. Epigenetic stimulation of multiple collagen genes observed in cultured glioma cells was then directly confirmed using orthotopic xenografts and tumor specimens. We suggest that the epigenetic mechanisms allow gliomas to deposit an invasion-promoting collagen-enriched matrix and then to use this matrix to accomplish their rapid migration through the brain tissue.

Incorporating the value of changes in price volatility into cost-benefit analysis—an application to oil prices in the transport sector

This paper contains a tentative suggestion of how to take into account the value of changes in price volatility in real world cost-benefit analyses. Price volatility is an important aspect of security of supply which first of all concerns physical availability, but assuming that consumers are risk averse, security of supply can also be viewed as a matter of avoiding oscillations in consumption originating from volatile prices of for instance oil. When the government makes transport-related choices on behalf of the consumers, the effect on oscillations in general consumption should be included in the policy assessment taking into account the most significant correlations between prices of alternative fuels and between fuel prices and consumption in general. In the present paper, a method of valuing changes in price volatility based on portfolio theory is applied to some very simple transport-related examples. They indicate that including the value of changes in price volatility often makes very little difference to the results of cost-benefit analyses, but more work has to be done on quantifying, among other things, consumers’ risk aversion and the background standard deviation in total consumption before firm conclusions can be drawn.

Thermal Ecology Of The Neotropical Army Ant Eciton Burchellii

I explored the thermal ecology of Eciton burchellii, a New World army ant, in primary forest and forest fragments in the Atlantic lowlands of Costa Rica in 2002 and 2003. My primary objective was to determine whether high surface temperatures in pastures surrounding forest fragments posed a thermal barrier to ant colonies within those fragments; secondarily, I assessed whether thermal gradients within continuous moist forest were sufficient to elicit avoidance reactions from foraging colonies. E. burchellii colonies in forest fragments avoided entering open pasture in full sun (51.3 degrees C) on 100% of all edge interactions; however, edges were readily crossed where artificial shaded areas had previously been installed. Ant raids in primary forest avoided artificially established temperatures >43 degrees C but tolerated 45.5 degrees C in the presence of prey baits. Captive ants held at 43 degrees C survived 18.5 min; at temperatures of 51.3 degrees C survival time was only 2.8 min. Ants running on established foraging trails increased running velocity by 18% when substrate temperature was raised from 28.4 degrees to 38.0 degrees C, and they abandoned trails at temperatures >43 degrees C. The standard deviation (s) of temperatures on active raid trails in continuous forest was 2.13 degrees C, while nearby systematic sampling revealed a greater background standard deviation of 4.13 degrees C. E. burchellii colonies in this region appear to be living surprisingly near their upper limits of thermal tolerance. The heat of open pastures alone is sufficient to prevent their exiting forest fragments, or entering similarly hot areas within continuous forest. Shaded vegetative corridors are sufficient to permit mobility between isolated fragments, and their preservation should be encouraged. Despite views that tropical lowland moist forests have an essentially homogenous microclimate, army ants appear to avoid local hot spots on the forest floor, steering daily foraging trails to follow cooler routes than would be expected by chance. As deforestation remakes tropical landscapes worldwide, it is important to consider the thermal consequences of these actions and their effect on keystone organisms such as army ants. Changes in global climate patterns are likely to affect even evergreen tropical systems whose organisms may be sensitive to finer microclimatic variation than previously suspected.

Evaluation of eight decision rules for low-level radioactivity counting.

In low-level radioactivity measurements, it is often important to decide whether a measurement differs from background. A traditional formula for decision level (DL) is given in numerous sources, including the recent ANSI/HPS N13.30-1996, Performance Criteria for Radiobioassay and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM). This formula, which we dub the N13.30 rule, does not adequately account for the discrete nature of the Poisson distribution for paired blank (equal count times for background and sample) measurements, especially at low numbers of counts. We calculate the actual false positive rates that occur using the N13.30 DL formula as a function of a priori false positive rate a and background Poisson mean mu = rhot, where rho is the underlying Poisson rate and t is the counting time. False positive rates exceed a by significant amounts for alpha < or = 0.2 and mu < 100 counts, peaking at 25% at mu approximately equal to 0.71, nearly independent of alpha. Monte Carlo simulations verified calculations. Currie's derivation of the N13.30 DL was based on knowing a good estimate of the mean and standard deviation of background, a case that does not hold for paired blanks and low background rates. We propose one new decision rule (simply add 1 to the number of background counts), and we present six additional decision rules from various sources. We evaluate the actual false positive rate for all eight decision rules as a function of a priori false positive rate and background mean. All of the seven alternative rules perform better than the N13.30 rule. Each has advantages and drawbacks. Given these results, we believe that many regulations, national standards, guidance documents, and texts should be corrected or modified to use a better decision rule.

NTERPRETING THE PIXEL STANDARD DEVIATION STATISTIC FROM AN X-RAY TOMOGRAPHIC SCANNER

ABSTRACThe mean absorption and pixel standard deviation represent two quantitative outputs from x-ray computed tomography (X-ray CT). The standard deviation is a measure of dispersion of individual pixel absorption values. An approach for using the standard deviation statistic in interpreting image texture was developed and was investigated using specially constructed fixtures. The theory assumes a scanned region could be divided into two or three subregions having known absorption values and having a known background standard deviation. Using the model, predicted vs. observed standard deviation agreed within 10%. Prediction of pixel numbers representing pore media compared with actual pixels occupied by pore space agreed within 20%. The standard deviation statistic appears to be a significant aid in quantifying relative proportions of two constituent zones within images representing two zones.

Simplified calculation of the effects of jitter on clutter leakage

A simple analytical solution has been found that enables prediction of the effects of line-of-sight motion of a sensor on clutter leakage after first differencing. The effect of finite footprint size is included, and the crucial importance of the ratio of this quantity to the background correlation length is given. For small motions the clutter leakage as a fraction of the value of background standard deviation is given by the very simple formula 2[1−exp−(L/d)], where L is the footprint size, and d is the background correlation length.