Mean To Standard Deviation Ratio Research Articles

Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling.

Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view. In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation-maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator. The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors. The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.

ADCOMS sensitivity versus baseline diagnosis and progression phenotypes.

The Alzheimer's Disease COMposite Score (ADCOMS) is more sensitive in clinical trials than conventional measures when assessing pre-dementia. This study compares ADCOMS trajectories using clustered progression characteristics to better understand different patterns of decline. Post-baseline ADCOMS values were analyzed for sensitivity using mean-to-standard deviation ratio (MSDR), partitioned by baseline diagnosis, comparing with the original scales upon which ADCOMS is based. Because baseline diagnosis was not a particularly reliable predictor of progression, individuals were also grouped into similar ADCOMS progression trajectories using clustering methods and the MSDR compared for each progression group. ADCOMS demonstrated increased sensitivity for clinically important progression groups. ADCOMS did not show statistically significant sensitivity or clinical relevance for the less-severe baseline diagnoses and marginal progression groups. This analysis complements and extends previous work validating the sensitivity of ADCOMS. The large data set permitted evaluation-in a novel approach-by the clustered progression group.

Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)

Chest radiograph ribs obstruct lung nodules. To see the nodule under the chest radiograph ribs, remove or suppress them. The paper describes a circular median filter approach for finding outliers in chest radiographs. The method uses 147 Japanese Society of Radiological Technology x-ray pictures (JSRT). Pixels with intensities two standard deviations above the median are median outliers. Contrast-Limited Adaptive Histogram Equalization enhances nodule visibility (CLAHE). The method is tested on modest chest radiographs and compared to the Budapest University Bone Shadow Eliminated X-Ray Dataset methodology. The initial test uses 50 modest chest radiographs (Test 1). The proposed approach is applied after active shape modelling (ASM) lung segmentation. True positive nodules are seen on 89% of chest radiographs of various subtleties. Test-2 and Test-3 used 20 subtlety-level photos. In Test-2, the peak signal-to-noise ratio (PSNR), mean-to-standard deviation ratio (MSR), and universal image quality index (IQI) are evaluated for the full image and compared to the existing algorithm. For all three parameters, the suggested technique outperforms the algorithm. Test-3 computes nodule MSR and compares it to Budapest University's Bone Shadow Eliminated Dataset and original chest radiographs. The new algorithm improved nodule area contrast by 3.83% and 23.94% compared to the original chest radiograph. This approach improves chest radiograph nodule visualization.

Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging

BackgroundThe minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter.MethodsIn this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation.ResultsThe proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer.ConclusionSimulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of O(N^2) has the potential to be implemented in real time using the graphics processing unit.

The Alzheimer\u2019s Prevention Initiative Composite Cognitive Test: a practical measure for tracking cognitive decline in preclinical Alzheimer\u2019s disease

BackgroundThere is growing interest in identifying sensitive composite cognitive tests to serve as primary endpoints in preclinical Alzheimer’s disease (AD) treatment trials. We reported previously a composite cognitive test score sensitive to tracking preclinical AD decline up to 5 years prior to clinical diagnosis. Here we expand upon and refine this work, empirically deriving a composite cognitive test score sensitive to tracking preclinical AD decline up to 11 years prior to diagnosis and suitable for use as a primary endpoint in a preclinical AD trial.MethodsThis study used a longitudinal approach to maximize sensitivity to tracking progressive cognitive decline in people who progressed to the clinical stages of AD (n = 868) compared to those who remained cognitively unimpaired during the same time period (n = 989), thereby correcting for normal aging and practice effects. Specifically, we developed the Alzheimer’s Prevention Initiative Preclinical Composite Cognitive test (APCC) to measure very early longitudinal cognitive decline in older adults with preclinical AD. Data from three cohorts from Rush University were analyzed using a partial least squares (PLS) regression model to identify optimal composites within different time periods prior to diagnosis, up to 11 years prior to diagnosis. The mean-to-standard deviation ratio (MSDRs) is an indicator of sensitivity to change and was used to inform the final calculation of the composite score.ResultsThe optimal composite, the APCC, is calculated: 0.26*Symbol Digit Modalities + 2.24*MMSE Orientation to Time + 2.14*MMSE Orientation to Place + 0.53*Logical Memory Delayed Recall + 1.36* Word List-Delayed Recall + 0.68*Judgment of Line Orientation + 1.39*Raven’s Progressive Matrices Matrices (subset of 9 items from A and B). The MSDR of the APCC in a population of preclinical AD individuals who eventually progress to cognitive impairment, compared to those who remained cognitively unimpaired during the same time period, was − 1.10 over 1 year.ConclusionsThe APCC is an empirically derived composite cognitive test score with high face validity that is sensitive to preclinical AD decline up to 11 years prior to diagnosis of the clinical stages of AD. The components of the APCC are supported by theoretical understanding of cognitive decline that occurs during preclinical AD. The APCC was used as a primary outcome in the API Generation Program trials.

Development of a novel cognitive composite outcome to assess therapeutic effects of exercise in the EXERT trial for adults with MCI: The ADAS-Cog-Exec.

IntroductionUse of cognitive composites as primary outcome measures is increasingly common in clinical trials of preclinical and prodromal Alzheimer's disease (AD). Composite outcomes can decrease intra‐individual variability, resulting in improved sensitivity to detect longitudinal change and increased statistical power. We developed a novel composite outcome, the ADAS‐Cog‐Exec, for use in the EXERT trial—a Phase 3 randomized, controlled, 12‐month exercise intervention in mild cognitive impairment (MCI).MethodsThree combinations of cognitive measures selected from the Alzheimer's Disease Assessment Scale‐Cognitive Subscale version 13 (ADAS‐Cog13), tests of executive function, and the Clinical Dementia Rating (CDR) were created based on previously documented sensitivity to longitudinal change in MCI and to the effects of exercise. Optimally weighted composites of each combination were modeled using data from the ADNI‐1 MCI cohort. Ten‐fold cross‐validation was performed to obtain a bias‐corrected mean to standard deviation ratio (MSDR). The cognitive composites were assessed for their sensitivity to detect 12‐month change in MCI.ResultsThe MSDR of 12‐month change for each of the composite outcomes tested exceeded that of the ADAS‐Cog13 total score. The composite with the highest MSDR (MSDR = 0.48) and associated statistical power included scores on ADAS‐Cog13 Word Recall, Delayed Word Recall, Orientation, and Number Cancellation subtests; Trail‐Making Tests A & B, Digit Symbol Substitution and Category Fluency; and cognitive components of the CDR (Memory, Orientation, Judgement & Problem Solving).DiscussionAn optimally weighted cognitive composite measure was identified and validated for use in EXERT. This composite contained selected subtests from the ADAS‐Cog13, additional measures of executive function, and box scores for cognitive components of the CDR. Because this composite score demonstrated high sensitivity to longitudinal change in MCI it will be used as the primary outcome measure for the EXERT trial.

SEX-SPECIFIC COMPOSITE SCALES FOR LONGITUDINAL STUDIES OF INCIPIENT ALZHEIMER’S DISEASE

The impact of incipient Alzheimer's disease (AD) on cognition and function differs in men and women. This may be relevant to cohort studies and clinical trials investigating the earliest stages of disease, where sex-specific global scales may be more sensitive, and statistically powerful, measures of progression. We derived optimal composite assessment scales from component scales available in the AD Neuroimaging Initiative (ADNI) cohort database restricting to cognitively normal and MCI subjects by CSF Aβ biomarker positive for early AD. Maximally sensitive composites were constructed separately for men and women using standard formulas to determine the weighted sum of component measures that has the greatest mean to standard deviation ratio (MSDR) and therefore greatest statistical power in longitudinal studies [Ard et al., Pharmaceutical Statistics. 2016]. We considered composites constructed from all possible combinations of the Clinical Dementia Rating Scale Sum of Boxes (CDR-sob) and 8 psychometric scales available in ADNI. For reference, we compared the statistical power of the composites with the ADNI-PACC as outcome measures in a clinical trial. The ADNI-PACC is an approximation of the Prodromal Alzheimer's Cognitive Composite which sums component measures without regard to sex. Twenty four month data were available for 54 men (age 54.6 to 87.8; mean MMSE 27.3) and 84 women (age 55.2 to 85.6; mean MMSE 27.4). Improvement in MSDR was negligible after a total of 5 components was included in the composites (five component scales were both parsimonious and near maximal in terms of statistical power). The CDR-sob was the most sensitive measure in both the men and women. Beyond this, the sex-specific composites differed. The male composite included the Clock Drawing, Rey-AVLT Immediate Learning and 30 Minute Delayed Recall, and Logical Memory Delayed Recall, and the female composite included the Logical Memory Immediate Recall, Boston Naming, Animal Category Fluency, and a timed test calculated as the Trails B minus Trials A. In terms of statistical power, the sex-specific composites consistently out-performed composites constructed without regards to sex (not shown) as well as the ADNI-PACC (Figure).

Establishing Composite Cognitive Endpoints for Use in Preclinical Alzheimer's Disease Trials.

Establishing Composite Cognitive Endpoints for Use in Preclinical Alzheimer's Disease Trials.

微焦点X射线图像乘性加性混合噪声的去除

In consideration of the lower imaging Signal to Noise Ratio(SNR)and serious mixed noise of a micro-focus X-ray inspector,a denoising method was proposed for the images corrupted with mixed multiplicative noise and additive noise.Firstly,an image model was established to represent the micro-focus X-ray images with mixed multiplicative and additive noises.Then,to remove the mixed noises,the objective functions were proposed based on the principle of total variation and sparse representation.Finally,the multiplicative noise and the additive noise were removed by explicitdifference method and gradient projection in steps.Experiment results show that the proposed method enhances the Mean to Standard deviation Ratio(MSR)of the images by 10.9%in smooth areas,the Laplacian Sum(LS)by 15.6% in detail areas as compared with total variation algorithm for the additive noise model.The experiments demonstrate that the proposed method not only removes the mixed noises in X-ray image but also retains the details of the image edge.It meets the requirements of integrated circuit detection for image smoothness and detail definition.

High efficiency stereo audio compression method using polar coordinate principle component analysis for wireless communications

High efficiency audio compression is the basic technology in audio involved multimedia communications. Downmixing and parametric coding is efficient coding scheme with wide applications in some up-to-date audio codecs such as Parametric Stereo (PS) in EAAC+ and MPEG-Surround. Principle Component Analysis (PCA) stereo coding followed this idea to map two channels to one channel with maximum energy and parameterize the secondary channel. This paper investigates the conventional PCA method performance under general stereo model with multiple sound sources and different directions, and then proposes a Polar Coordinate based PCA (PC-PCA) stereo coding method. It has been proved that when multiple sound sources exist with different directions, PC-PCA is better than the conventional PCA method when Mean to Standard deviation Ratio (MSR) is large. A stereo codec based on PC-PCA is proposed to validate the performance improvement of proposed method. Objective and subjective tests show the proposed method achieves a comparative quality and saves 50% parameter bit rate comparing with conventional PCA method, and obtains a 4–8 MUSHRA scores improvement comparing with state-of-the-art stereo codec at the same parameter bit rate.

Denoising by using multineural networks for medical X-ray imaging applications

In this paper, a new type of multineural network filter (MNNF) is presented that is trained for restoration and enhancement of the digital radiological images. In medical radiographices, noise has been categorized as quantum mottle, which is related to the incident X-ray exposure and artificial noise, which is caused by the grid, etc. MNNF consists of several neural network filters (NNFs). A novel analysis method is proposed to make the characteristics of the trained MNNF clearly. In the proposed method, a characteristics judgement system is presented to decide which NNF will be executed through the standard deviation value of pixels in the input region. The new approach was tested on nine clinical medical X-ray images and five synthesized noisy X-ray images. In all cases, the proposed MNNF produced better results in terms of peak signal-to-noise ratio (PSNR), mean-to-standard-deviation ratio (MSR) and contrast to noise ratio (CNR) measures than the original NNF, linear inverse filter and nonlinear median filter.

The progression of thrombus in an ex-vivo shunt model evaluated by intravascular ultrasound radiofrequency analysis

We tested the ability of ultrasound radiofrequency (RF) signal analysis to characterize thrombus accumulation in a Dacron graft incorporated into the exteriorized arteriovenous shunt in 3 baboons with constant blood flow for 60 min. Thrombus formation was quantified by sequential measurements of 111Indium-labeled platelet deposition. RF signals were acquired every 15 min at 2 sites in the graft, using a 2.9 Fr intravascular ultrasound catheter-based transducer (30 MHz) and digitized at 250 MHz in 8-bit resolution. Regions of interest were placed within a 0.5-mm perimeter adjacent to the graft wall. Integrated backscatter increased significantly ( p < 0.001) with increasing platelet deposition. However, mean-to–standard deviation ratio of the RF envelope showed no significant change and the distribution pattern of the RF probability function remained constant and consistent with a Rayleigh scattering process. These results provide a basis for using RF analysis to monitor the time-course of thrombus formation.

Methods for Estimation of Statistical Properties of Envelopes of Ultrasonic Echoes from Myocardium

Several investigators have characterized various forms of heart disease from the statistical properties of envelopes of ultrasonic echos from myocardium. In particular, the mean-to-standard deviation ratio (MSR), skewness, and kurtosis of the envelope probability density function have been used for the detection of myocardial ischemia, infarction, reperfusion, and hypertrophy. In this paper, the effects of phenomena other than tissue acoustic properties upon estimates of statistical parameters are investigated. These include system characteristics (center frequency, bandwidth, beam width, etc.), sample volume dimensions, and tissue velocity. In myocardium, relatively small amounts of tissue are available for interrogation. It is shown that, under these limited data acquisition conditions, substantial systematic biases in the estimates of statistical parameters may occur. Analytic forms for errors in the envelope variance estimate are derived. Estimation of the envelope mean, variance, MSR, skewness, and kurtosis is investigated experimentally, using a commercial medical ultrasound scanner and a tissue-mimicking phantom.