Search

Purpose: Earlier work showed that IVIM-NET orig, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NET optim, and characterizes its superior performance in pancreatic cancer patients. Method: In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CV NET), respectively. The best performing network, IVIM-NET optim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET optim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. Results: In simulations (SNR = 20), IVIM-NET optim outperformed IVIM-NET orig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CV NET(D) = 0.013 vs 0.104; CV NET(f) = 0.020 vs 0.054; CV NET(D*) = 0.036 vs 0.110). IVIM-NET optim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NET optim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET optim detected the most individual patients with significant parameter changes compared to day-to-day variations. Conclusion: IVIM-NET optim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Purpose For clinical implementation, a chemical exchange saturation transfer (CEST) imaging sequence must be fast, with high signal-to-noise ratio (SNR), 3D coverage, and produce robust contrast. However, spectrally selective CEST contrast requires dense sampling of the Z-spectrum, which increases scan duration. This article proposes a compromise: using a 3D snapshot gradient echo (GRE) readout with optimized CEST presaturation, sampling, and postprocessing, highly resolved Z-spectroscopy at 3T is made possible with 3D coverage at almost no extra time cost. Methods A 3D snapshot CEST sequence was optimized for low-power CEST MRI at 3T. Pulsed saturation was optimized for saturation power and saturation duration. Spectral sampling and postprocessing (B0 correction, denoising) was optimized for spectrally selective Lorentzian CEST effect extraction. Reproducibility was demonstrated in 3 healthy volunteers and feasibility was shown in 1 tumor patient. Results Low-power saturation was achieved by a train of 80 pulses of duration tp = 20 ms (total saturation time tsat = 3.2 seconds at 50% duty cycle) with B1 = 0.6 μT at 54 irradiation frequency offsets. With the 3D snapshot CEST sequence, a 180 × 220 × 54 mm field of view was acquired in 7 seconds per offset. Spectrally selective CEST effects at +3.5 and -3.5 ppm were quantified using multi-Lorentzian fitting. Reproducibility was high with an intersubject coefficient of variation below 10% in CEST contrasts. Amide and nuclear overhauser effect CEST effects showed similar correlations in tumor and necrosis as show in previous ultra-high field work. Conclusion A sophisticated CEST tool ready for clinical application was developed and tested for feasibility.

Since our previous publication of organ dose for the pediatric CT cohort in the UK, there have been questions about the magnitude of uncertainty in our dose estimates. We therefore quantified shared and unshared uncertainties in empirical CT parameters extracted from 1073 CT films (1978–2008) from 36 hospitals in the study and propagated these uncertainties into organ doses using Monte Carlo random sampling and NCICT organ dose calculator. The average of 500 median brain and marrow doses for the full cohort was 35 (95% confidence interval: 30–40) mGy and 6 (5–7) mGy, respectively. We estimated that shared uncertainty contributed ~99% of coefficient of variation of median brain doses in brain scans compared to unshared uncertainty (1% contribution). We found that the previous brain doses were slightly underestimated for 1990 compared to the results in the current study due to the revised CTDI models based on CT films.

Paravalvular leak (PVL) after transcatheter aortic valve implantation (TAVI) is challenging to quantitate. Transthoracic echocardiography (TTE) is the main tool used for the assessment of PVL but is modestly reproducible. We sought to develop a reproducible echocardiographic approach to assess PVL in the post-TAVI setting. Four observers independently analyzed eleven parameters of PVL severity in 50 pre-discharge TTE studies performed after TAVI. The parameters included color-Doppler parameters [jet circumferential extent (CE) and planimetered vena contracta area in the short-axis view and jet breadth and qualitative features in the long-axis views], continuous-wave Doppler parameters [jet velocity time integral (VTI) and pressure half time (PHT)], quantitative Doppler parameters (regurgitation volume and fraction and effective regurgitant orifice area), aortic diastolic flow reversal and valve stent eccentricity. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) for numerical parameters and kappa coefficient (κ) for categorical parameters were calculated for inter- and intra-observer comparisons. Inter-observer ICC was highest and CV lowest for CE (0.88 and 0.36), jet origin breadth (0.82 and 0.39), jet qualitative features in long-axis views (0.87 and 0.26), jet VTI (0.87 and 0.04) and PHT (0.73 and 0.10). Similar results were found in intra-observer comparisons. A 2-step granular approach combining the most reproducible parameters was used to grade PVL by the four observers. Inter-observer agreement was achieved in 86 % of cases (κ = 0.79). Combining color Doppler and continuous wave Doppler parameters in a granular algorithm yields excellent reproducibility of PVL assessment by TTE. Electronic supplementary material The online version of this article (doi:10.1007/s10554-016-0947-4) contains supplementary material, which is available to authorized users.

Severe brain damage is often followed by serious complications. Quantitative measurements, such as regional volume and surface area under various conditions, are essential for understanding functional changes in the brain and assessing prognosis. The affected brain tissue is variable, hence traditional imaging methods are not always applicable and automatic methods may not be able to match the individual observer. Stereological techniques are alternative tools in the quantitative description of biological structures, and have been increasingly applied to the human brain. In the present study, we applied stereological techniques to representative CT and MRI brain scans from five patients to describe how stereological methods, when applied to scans of trauma patients, can provide a useful supplement to the estimation of structural brain changes in head injuries. The reliability of the estimates was tested by obtaining repeated intra- and interobserver estimates of selected subdivisions of the brain in patients with acute head injury, as well as in an MR phantom. The estimates of different subdivisions showed a coefficient of variation (CV) below 12% in the patients and below 7% for phantom estimation. The validity of phantom estimates was tested by the average deviation from the true geometric values, and was below 10%. The stereological methods were compared with more traditional region-based methods performed on medical imaging, which showed a CV below 7% and bias below 14%. It is concluded that the stereological estimates may be useful tools in head injury quantification.

A.P.-F. and J.M.A.: conception and design, statistical analysis, interpretation of data, writing paper. F.J.A.G. and F.M.A.: conducted experiments, analyzed and interpretation of data, feedback on the paper. J.M.G.: feedback on the paper. J.H.M., J.S., and F.B.O.: interpretation of data, feedback on the paper. All authors read and approved the final manuscript., Heart rate variability (HRV) is a valid and non-invasive indicator of cardiac autonomic nervous system functioning. Short-term HRV recordings (e.g., 10 min long) produce data that usually is manually processed. Researcher subjective decision-making on data processing could produce inter- or intraresearcher differences whose magnitude has not been previously quantified in three independent human cohorts. This study examines the inter- and intra-researcher reproducibility of HRV parameters (i.e., the influence of R–R interval selection by different researchers and by the same researcher in different moments on the quantification of HRV parameters, respectively) derived from short-term recordings in a cohort of children with overweight/obesity, young adults and middle-age adults. Participants were recruited from 3 different studies: 107 children (10.03 ± 1.13 years, 58% male), 132 young adults (22.22 ± 2.20 years, 33% males) and 73 middle-aged adults (53.62 ± 5.18 years, 48% males). HRV was measured using a Polar RS800CX heart rate monitor. The intraclass correlation coefficient (ICC) ranged from 0.703 to 0.989 and from 0.950 to 0.998 for inter-and intra-researcher reproducibility, respectively. Limits of agreement for HRV parameters were higher for the interresearcher processing compared with the intra-researcher processing. On average, the intraresearcher differences were 31%, 62%, and 80% smaller than the inter-researchers differences based on Coefficient of Variation in children, young and middle-aged adults, respectively. Our study provides the quantification of the inter-researcher and intra-researcher differences in three independent human cohorts, which could elicit some clinical relevant differences for HRV parameters. Based on our findings, we recommend the HRV data signal processing to be performed always by the same trained researcher and we postulate a development of algorithms for an automatic ECG selection., Spanish Ministry of Economy and Competitiveness DEP2013-47540 DEP2016-79512-R PTA 12264-I, Spanish Ministry of Education, Culture and Sport FPU 16/02760 FPU15/02645 FPU15/04059 FPU14/06837 FPU14/04172, Unit of Excellence on Exercise and Health (UCEES), EXERNET Research Network on Exercise and Health in Special Populations DEP2005-00046/ACTI, Unit of Excellence in Sport and Health (UCEES) - University of Granada, Junta de Andalucía, European Union (EU) SOMM17/6107/UGR

Objective : In this paper, we assess the feasibility of using two detectors in a slot-scanning digital radiography system to acquire images for measuring bone mineral density (BMD) of the middle phalanx of the middle finger using dual-energy X-ray absorptiometry (DXA). Methods : Simulations were used to evaluate the spectral separation of the low- and high-energy spectra and detective quantum efficiency was used for assessing image quality. Scan parameters were chosen to optimize spectral separation, image quality, and radiation dose. We introduce the measurement of volumetric BMD ( v BMD) using basis material decomposition. We assess the accuracy of our methods by comparing measurements taken using bone images against reference data derived from subsequent incineration of the bones. In vivo scans were conducted to evaluate the system precision (repeatability) and agreement with a clinical densitometer. Results : Average errors for bone mineral content (BMC), areal BMD ( a BMD), and v BMD were 4.85%, 5.49%, and 12.77%, respectively. Our system had good agreement with a clinical densitometer based on concordance correlation coefficient values of 0.92 and 0.98 for a BMD and BMC, respectively. Precision studies yielded coefficient of variation (CV) values of 1.35% for a BMD, 1.48% for BMC, and 1.80% for v BMD. The CV values of all measurements were within 2%, indicating that the methods have clinically acceptable precision. Conclusion : We conclude that our techniques yield bone measurements with high accuracy, clinically acceptable precision, and good agreement with a clinical densitometer. Significance : We have shown the clinical potential of phalangeal DXA measurements of a BMD and v BMD on a slot-scanning digital radiography system.

In this paper, we propose a novel statistical index for the early diagnosis of ventricular arrhythmia (VA) using the time delay phase-space reconstruction (PSR) technique, from the electrocardiogram (ECG) signal. Patients with two classes of fatal VA - with preceding ventricular premature beats (VPBs) and with no VPBs have been analysed using extensive simulations. Three subclasses of VA with VPBs viz. ventricular tachycardia (VT), ventricular fibrillation (VF) and VT followed by VF are analyzed using the proposed technique. Measures of descriptive statistics like mean ({\mu}), standard deviation ({\sigma}), coefficient of variation (CV = {\sigma}/{\mu}), skewness ({\gamma}) and kurtosis (\{beta}) in phase-space diagrams are studied for a sliding window of 10 beats of ECG signal using the box-counting technique. Subsequently, a hybrid prediction index which is composed of a weighted sum of CV and kurtosis has been proposed for predicting the impending arrhythmia before its actual occurrence. The early diagnosis involves crossing the upper bound of a hybrid index which is capable of predicting an impending arrhythmia 356 ECG beats, on average (with 192 beats standard deviation) before its onset when tested with 32 VA patients (both with and without VPBs). The early diagnosis result is also verified using a leave out cross-validation (LOOCV) scheme with 96.88% sensitivity, 100% specificity and 98.44% accuracy., Comment: 25 pages, 16 figures, 2 tables

The purpose of this paper is to describe a clearly defined manual method for calculating cortical silent period (CSP) length that can be employed successfully and reliably by raters after minimal training in subjects with focal hand dystonia (FHD) and healthy subjects. A secondary purpose was to explore intra-subject variability of the CSP in subjects with FHD vs. healthy subjects. Two raters previously naïve to CSP identification and one experienced rater independently analyzed 170 CSP measurements collected in 6 subjects with focal hand dystonia (FHD) and 9 healthy subjects. Intraclass correlation coefficient (ICC) was calculated to quantify inter-rater reliability within the two groups of subjects. The relative variability of CSP in each group was calculated by the coefficient of variation (CV). Relative variation between raters within repeated measures of individual subjects was also quantified by CV. Reliability measures were as follows-mean of three raters: all subjects: ICC=0.976; within healthy subjects: ICC=0.965; in subjects with FHD: ICC=0.956. The median within-subject variability for the healthy group was CV=7.33% and in subjects with FHD:CV=11.78%. The median variability of calculating individual subject CSP duration between raters was CV=10.23% in subjects with dystonia and CV=10.46% in healthy subjects. Manual calculation of CSP results in excellent reliability between raters of varied levels of experience. Healthy subjects display less variability in CSP. Despite greater variability, the CSP in impaired subjects can be reliably calculated across raters.

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.

Background There is often a need in flow cytometry to display and analyze histograms at resolutions lower than those native to the data. It is common, for example, to analyze DNA histograms at 256-channel resolution, even though the data were acquired at 1,024 channels or more. The most common method for reducing resolution, referred to as the consecutive summation (CS) method, can introduce distortions into the shape of histograms. Peaks that were symmetric in the original data can become skewed in the reduced-resolution histogram. Data analysis can be negatively affected by the distortions produced by reducing the histogram resolution. An alternative technique for reducing histogram resolution, the unbiased summation (US) method, minimizes shape distortion. This paper describes the US method and examines the benefits it provides in the analysis of DNA histograms. Methods Reduced chi-square (RCS) was used to measure the response to three experimental variables in the least-squares analysis of simulated DNA histograms. For each variable (the percentage of coefficient of variation [%CV], number of events, and mean position of the G1 distribution), a test data set of 1,000 histograms was generated at 1,024-channel resolution. Histogram resolutions were reduced with each method and then analyzed with ModFit LT cell-cycle analysis software (Verity Software House, Topsham, ME). S-phase error and processor computation time of each method also were evaluated. A Monte Carlo experiment was performed to compare CS and US methods to theoretically correct reductions. Results CS method analysis results were negatively affected by changes in %CV, number of events, and G1 peak position. The US method produced consistently lower RCS values (more accurate results) within the tested ranges. The US method eliminated bias in S-phase error and had negligible impact on analysis processing speed. It improved RCS values 44.50% on average (P < 0.0002) with actual DNA histograms. Whereas the CS method became less accurate (chi-square test) as the amount of reduction increased, the US method was unaffected, producing consistently better results. Conclusions The US method is recommended for reducing histogram resolution in modeling applications such as DNA cell-cycle analysis. It may have implications in other areas of flow cytometric data analysis. Cytometry Part A 53A:103–111, 2003. © 2003 Wiley-Liss, Inc.

Cataloged from PDF version of article. In this paper, we address the problem of uninterruptedly scheduling a set of independent jobs that are ready at time zero with the objective of minimizing the coefficient of variation (CV) of their completion times. We first show that, for high processing time values of the longest job, a variance (V) minimizing schedule also minimizes CV. Using this equivalence, we next demonstrate the invalidity of an earlier conjecture about the structure of a CV-optimal schedule and proceed to establish the NP-hardness of the CV problem. Finally, drawing from our prior work on the V problem, we provide a pseudo-polynomial dynamic programming algorithm for the solution of the CV problem.

Purpose The purpose of this paper was to compare manual and automated analysis for the quantification of carotid wall obtained with sonography by using the computed tomography as validation technique. Material and methods 21 consecutive patients underwent MDCTA and ultrasound analysis of carotid arteries (mean age 68 years; age range 59–81 years). The intima–media-thickness (IMT) of the 42 carotids was measured with novel and dedicated automated software analysis (called AtheroEdge™, Biomedical Technologies, Denver, CO, USA) and by four observers that manually calculated the IMT. The carotid artery wall thickness (CAWT) was also quantified in the CT datasets. Bland–Altman statistics was employed to measure the agreement between methods. A Student's t -test was used to test the differences between the IMT values of AtheroEdge™. The study obtained the IRB approval. Results The correlation between automated AtheroEdge™ measurements and those of the human experts were equal to 95.5%, 73.5%, 88.9%, and 81.7%. The IMT coefficient of variation of the human experts was equal to 11.9%. By using a Student's t -test, the differences between the IMT values of AtheroEdge™ and those of the human experts were not found statistically significant ( p value=0.02). On comparing AtheroEdge™ (using Ultrasound) with CAWT (using CT), the results suggested a very good concordance of 84.96%. Conclusions Data of this preliminary study indicate that automated software AtheroEdge™ can analyze with precision the IMT of carotid arteries and that the concordance with CT is optimal.

Two physiological factors are assumed in this paper to mainly determine the myoelectric manifestations of fatigue: (1) the decrease of the conduction velocity (CV) of motor unit action potentials (MUAP) (peripheral fatigue), and (2) the increase of MU synchronization by the central nervous system (central fatigue). To describe separately the peripheral and central components of the myoelectric manifestations of fatigue, we investigated the following indexes: (1) mean spectral frequency – MNF, (2) median spectral frequency – MDF, (3) root mean square – RMS, (4) average rectified value – ARV, (5) estimation of muscle fiber conduction velocity – ECV, (6) percentage of determinism – %DET, (7) spectral indexes defined as the ratio between signal spectral moments – FI k , (8) MNF estimated by autoregressive analysis – MNF AR , (9) MNF estimated by Choi–Williams time-frequency representation – MNF CWD , (10) MNF estimated by continuous wavelet transform – MNF CWT , (11) signal entropy – S, (12) fractal dimension – FD. The indexes were tested with a set of synthetic EMG signals, with different CV distribution and level of MU synchronization. The indexes were calculated on epochs of 0.5s. It was observed that ECV is uncorrelated with the level of simulated synchronization (promising index of peripheral fatigue). On the other hand FD was the index least affected by CV changes and most related to the level of synchronism (promising index of central fatigue). A representative application to some experimental signals from vastus lateralis muscle during an isometric endurance test supported the results of the simulations. The vector (ECV, FD) is suggested to provide selective indications of peripheral and central fatigue. The description of EMG fatigue by a bi-dimensional vector opens new perspectives in the assessment of muscle properties, with potential application in both clinical and sport sciences.

Motivation: There are two general methods for making gene-expression microarrays: one is to hybridize a single test set of labeled targets to the probe, and measure the background-subtracted intensity at each probe site; the other is to hybridize both a test and a reference set of differentially labeled targets to a single detector array, and measure the ratio of the background-subtracted intensities at each probe site. Which method is better depends on the variability in the cell system and the random factors resulting from the microarray technology. It also depends on the purpose for which the microarray is being used. Classification is a fundamental application and it is the one considered here. Results: This paper describes a model-based simulation paradigm that compares the classification accuracy provided by these methods over a variety of noise types and presents the results of a study modeled on noise typical of cDNA microarray data. The model consists of four parts: (1) the measurement equation for genes in the reference state; (2) the measurement equation for genes in the test state; (3) the ratio and normalization procedure for a dual-channel system; and (4) the intensity and normalization procedure for a single-channel system. In the reference state, the mean intensities are modeled as a shifted exponential distribution, and the intensity for a particular gene is modeled via a normal distribution, Normal(I, αI), about its mean intensity I, with α being the coefficient of variation of the cell system. In the test state, some genes have their intensities up-regulated by a random factor. The model includes a number of random factors affecting intensity measurement: deposition gain d, labeling gain, and post-image-processing residual noise. The key conclusion resulting from the study is that the coefficient of variation governing the randomness of the intensities and the deposition gain are the most important factors for determining whether a single-channel or dual-channel system provides superior classification, and the decision region in the α−d plane is approximately linear. Supplementary information: A companion website containing supplementary and background material can be accessed at http://ee.tamu.edu/~edward/ratio_intensity/