Density-dependent Energy Loss of Protons in Pb and Be Targets and Percent Mass-Stopping Power from Bethe-Bloch Formula and Bichsel-Sternheimer Data Within 1–12 MeV Energy Range: A Comparative Study Based on Bland-Altman Analysis

2019 ◽  
Vol 50 (1) ◽  
pp. 149-156 ◽  
Author(s):  
Azmat Iqbal ◽  
Nasir Ullah ◽  
Amin Ur Rahman
Keyword(s):  
Energy Loss ◽  
Comparative Study ◽  
Energy Range ◽  
Stopping Power ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
Density Dependent

scholarly journals Calculation of the total mass stopping power for electrons in some human body tissues in the energy range 0.01-1000 MeV

2019 ◽  
Vol 15 (34) ◽  
pp. 72-80
Author(s):  
Jinan F. Mahdi
Keyword(s):  
Energy Loss ◽  
Energy Range ◽  
Excellent Agreement ◽  
Total Mass ◽  
Stopping Power ◽  
Radiative Energy ◽  
Power Calculation ◽  
Radiative Energy Loss ◽  
Body Tissues ◽  
Modified Equation

The mass collision energy loss (dE/dX), the mass radiative energy loss (Srad/) and the total mass stopping power of electrons in the energy range of 0.01 MeV up to 1000 MeV has been calculated for Lung, Urea and Skin. The results of the present work for the mass collision stopping power of electrons in Lung, Urea and Skin are in excellent agreement with the standard results given by ESTAR program, where the maximum percentage error between the present calculated values and that of ESTAR program in Lung tissue, Urea and Skin tissue is 0.27%, 0.3% and 0.8% respectively. The mass radiative energy loss of electrons in the same energy range is also calculated using a modified equation, and the results are found to be in very good agreement with the standard published values. The employed modified equation used to calculate the mass radiative energy loss of electrons is valid in the energy range of electrons from 0.01 MeV up to 1000 MeV and gives accurate results. As the results of total stopping power calculation are concerned, they are found in excellent agreement with the published results, where the error is less than 1%.

Stopping-power determination for compound by EELS

1994 ◽  
Vol 52 ◽  
pp. 948-949 ◽  
Author(s):  
David C. Joy ◽  
Suichu Luo ◽  
John R. Dunlap ◽  
Dick Williams ◽  
Siqi Cao
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Materials Science ◽  
Average Energy ◽  
Stopping Power ◽  
Accurate Information ◽  
Power Calculation ◽  
Coulomb Collisions ◽  
Electron Energy Loss

In Physics, Chemistry, Materials Science, Biology and Medicine, it is very important to have accurate information about the stopping power of various media for electrons, that is the average energy loss per unit pathlength due to inelastic Coulomb collisions with atomic electrons of the specimen along their trajectories. Techniques such as photoemission spectroscopy, Auger electron spectroscopy, and electron energy loss spectroscopy have been used in the measurements of electron-solid interaction. In this paper we present a comprehensive technique which combines experimental and theoretical work to determine the electron stopping power for various materials by electron energy loss spectroscopy (EELS ). As an example, we measured stopping power for Si, C, and their compound SiC. The method, results and discussion are described briefly as below.The stopping power calculation is based on the modified Bethe formula at low energy:where Neff and Ieff are the effective values of the mean ionization potential, and the number of electrons participating in the process respectively. Neff and Ieff can be obtained from the sum rule relations as we discussed before3 using the energy loss function Im(−1/ε).

Reply: Bland-Altman analysis for pupillometer comparison

2010 ◽  
Vol 36 (10) ◽  
pp. 1803-1804
Author(s):  
Magdalena Scheffel ◽  
Christoph Kuehne ◽  
Thomas Kohnen
Keyword(s):  
Altman Analysis ◽  
Bland Altman Analysis

scholarly journals Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mercy I. Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Input Function ◽  
Brain Regions ◽  
Population Based ◽  
Patient Specific ◽  
Systematic Bias ◽  
Altman Analysis ◽  
Bland Altman Analysis

Abstract Introduction Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and methods Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is −10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). Conclusions PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

scholarly journals Evaluation of automated microvascular flow analysis software AVA 4: a validation study

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Christian S. Guay ◽  
Mariam Khebir ◽  
T. Shiva Shahiri ◽  
Ariana Szilagyi ◽  
Erin Elizabeth Cole ◽  
...  
Keyword(s):  
General Anesthesia ◽  
Validation Study ◽  
Validation Studies ◽  
Gold Standard ◽  
Flow Analysis ◽  
Automated Analysis ◽  
Altman Analysis ◽  
Analysis Software ◽  
Bland Altman Analysis ◽  
Microvascular Flow

Abstract Background Real-time automated analysis of videos of the microvasculature is an essential step in the development of research protocols and clinical algorithms that incorporate point-of-care microvascular analysis. In response to the call for validation studies of available automated analysis software by the European Society of Intensive Care Medicine, and building on a previous validation study in sheep, we report the first human validation study of AVA 4. Methods Two retrospective perioperative datasets of human microcirculation videos (P1 and P2) and one prospective healthy volunteer dataset (V1) were used in this validation study. Video quality was assessed using the modified Microcirculation Image Quality Selection (MIQS) score. Videos were initially analyzed with (1) AVA software 3.2 by two experienced investigators using the gold standard semi-automated method, followed by an analysis with (2) AVA automated software 4.1. Microvascular variables measured were perfused vessel density (PVD), total vessel density (TVD), and proportion of perfused vessels (PPV). Bland–Altman analysis and intraclass correlation coefficients (ICC) were used to measure agreement between the two methods. Each method’s ability to discriminate between microcirculatory states before and after induction of general anesthesia was assessed using paired t-tests. Results Fifty-two videos from P1, 128 videos from P2 and 26 videos from V1 met inclusion criteria for analysis. Correlational analysis and Bland–Altman analysis revealed poor agreement and no correlation between AVA 4.1 and AVA 3.2. Following the induction of general anesthesia, TVD and PVD measured using AVA 3.2 increased significantly for P1 (p < 0.05) and P2 (p < 0.05). However, these changes could not be replicated with the data generated by AVA 4.1. Conclusions AVA 4.1 is not a suitable tool for research or clinical purposes at this time. Future validation studies of automated microvascular flow analysis software should aim to measure the new software’s agreement with the gold standard, its ability to discriminate between clinical states and the quality thresholds at which its performance becomes unacceptable.

scholarly journals Comparison of IOP Measurement by Goldmann Applanation Tonometer, ICare Rebound Tonometer, and Tono-Pen in Keratoconus Patients after MyoRing Implantation

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Mahmoud Rateb ◽  
Mahmoud Abdel-Radi ◽  
Zeiad Eldaly ◽  
Mohamed Nagy Elmohamady ◽  
Asaad Noor El Din
Keyword(s):  
Central Corneal Thickness ◽  
Pearson Correlation ◽  
Corneal Thickness ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
Rebound Tonometer ◽  
Goldmann Applanation Tonometer ◽  
Significant Difference ◽  
Applanation Tonometer ◽  
Icare Rebound Tonometer

Purpose. To evaluate the different IOP readings by Goldmann applanation tonometer (GAT), ICare rebound tonometer, and Tono-Pen in keratoconus patients after MyoRing implantation. To assess the influence of central corneal thickness (CCT) and thinnest corneal location (TCL) on IOP measurements by different tonometers. Setting. Prospective observational study was conducted in two private centers in Egypt from February 2015 to November 2016. Methods. Seventeen eyes of 10 patients suffering from keratoconus and who underwent MyoRing implantation were recruited. All subjects underwent GAT, ICare, and Tono-Pen IOP measurements in random order. Central corneal thickness and thinnest corneal location were assessed by Pentacam. Difference in mean in IOP readings was assessed by T-test. Correlation between each pair of devices was evaluated by Pearson correlation coefficient. The Bland–Altman analysis was used to assess intertonometer agreement. Results. Seventeen eyes (10 patients) were evaluated. The mean IOP reading was 13.9 ± 3.68, 12.41 ± 2.87, and 14.29 ± 1.31 mmHg in GAT, ICare, and Tono-Pen group, respectively. There was a significant difference between IOP readings by GAT/ICare and Tono-Pen/ICare (p value: 0.032 and 0.002, respectively) with no significant difference between GAT/Tono-Pen (p value: 0.554). Mean difference in IOP measurements between GAT/ICare was 1.49 ± 2.61 mmHg, Tono-Pen/ICare was 1.89 ± 2.15 mmHg, and GAT/Tono-Pen was −0.39 ± 2.59 mmHg. There was no significant correlation between the difference in IOP readings among any pair of devices and CCC or TCL. The Bland–Altman analysis showed a reasonable agreement between any pair of tonometers.

Stopping power of heavy ions in solids: A comparative study

1995 ◽  
Vol 46 (1) ◽  
pp. 39-52 ◽  
Author(s):  
S.K. Sharma ◽  
Shyam Kumar ◽  
J.S. Yadav ◽  
A.P. Sharma
Keyword(s):  
Comparative Study ◽  
Heavy Ions ◽  
Stopping Power

Performance of Automated Telemetry in Diagnosing QT Prolongation in Critically Ill Patients

2021 ◽  
Vol 30 (6) ◽  
pp. 466-470
Author(s):  
Enrique Calvo-Ayala ◽  
Vince Procopio ◽  
Hayk Papukhyan ◽  
Girish B. Nair
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Demographic Data ◽  
Area Under The Curve ◽  
Qt Prolongation ◽  
Altman Analysis ◽  
Poor Agreement ◽  
Bland Altman Analysis ◽  
Prolonged Qt ◽  
Definition Of

Background QT prolongation increases the risk of ventricular arrhythmia and is common among critically ill patients. The gold standard for QT measurement is electrocardiography. Automated measurement of corrected QT (QTc) by cardiac telemetry has been developed, but this method has not been compared with electrocardiography in critically ill patients. Objective To compare the diagnostic performance of QTc values obtained with cardiac telemetry versus electrocardiography. Methods This prospective observational study included patients admitted to intensive care who had an electrocardiogram ordered simultaneously with cardiac telemetry. Demographic data and QTc determined by electrocardiography and telemetry were recorded. Bland-Altman analysis was done, and correlation coefficient and receiver operating characteristic (ROC) coefficient were calculated. Results Fifty-one data points were obtained from 43 patients (65% men). Bland-Altman analysis revealed poor agreement between telemetry and electrocardiography and evidence of fixed and proportional bias. Area under the ROC curve for QTc determined by telemetry was 0.9 (P &lt; .001) for a definition of prolonged QT as QTc ≥ 450 milliseconds in electrocardiography (sensitivity, 88.89%; specificity, 83.33%; cutoff of 464 milliseconds used). Correlation between the 2 methods was only moderate (r = 0.6, P &lt; .001). Conclusions QTc determination by telemetry has poor agreement and moderate correlation with electrocardiography. However, telemetry has an acceptable area under the curve in ROC analysis with tolerable sensitivity and specificity depending on the cutoff used to define prolonged QT. Cardiac telemetry should be used with caution in critically ill patients.

scholarly journals A Hard X-ray Observation of CYG X-1 in 1985

1987 ◽  
Vol 125 ◽  
pp. 202-202
Author(s):  
Y.Q. Ma ◽  
G.H. Li ◽  
C.M. Zhang ◽  
Q.Y. Xiao ◽  
Y.M. Qian ◽  
...  
Keyword(s):  
Energy Loss ◽  
Energy Range ◽  
Arrival Time ◽  
X Ray ◽  
Main Detector ◽  
Time And Energy

On September 22, 1985, a hard X-ray observation of Cyg X-1 was performed by using a balloon-borne CsI-NaI phoswich telescope HAPI-2 at Xianghe Balloon Facility in China. The main detector is CsI(T1) with a thickness of 0.4cm and an area of 140cm2. The energy range is 20–200KeV. The telescope reached a float altitude of about 38Km(4g/cm2). The photon's arrival time and energy loss spectrum were measured for both background and active tracking on-source observations.

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