Determination of contrast medium dose for hepatic CT enhancement with improved body size dependency using a non-linear analysis based on pharmacokinetic principles

2020 ◽  
Vol 75 (3) ◽  
pp. 238.e11-238.e19 ◽  
Author(s):  
T. Hibino ◽  
K. Ichikawa ◽  
Y. Fang ◽  
S. Ito ◽  
H. Kawashima ◽  
...  
Author(s):  
Oldřich Sucharda ◽  
David Mikolášek ◽  
Jiří Brožovský

Abstract This paper deals with the determination of compressive strength of concrete. Cubes, cylinders and re-used test beams were tested. The concrete beams were first subjected to three-point or fourpoint bending tests and then used for determination of the compressive strength of concrete. Some concrete beams were reinforced, while others had no reinforcement. Accuracy of the experiments and calculations was verified in a non-linear analysis.


Author(s):  
Kwanghyun Ahn ◽  
Minsung Chun ◽  
Sangmin Han ◽  
Kibok Jang ◽  
Yongsuk Suh

For the last few decades, necessity of direct non-linear FE analysis has been increasing for the accidental events at the vessel/offshore structures. One of major areas for the accidental design, dropped object analysis using non-linear analysis is indispensable for the verification of structural safety at the design process. This paper is concerned with the methodology, conditions, and design consideration of dropped object analysis using dynamic FE analysis. By comparing the results from direct FE analyses to those from simplified energy method described in DNV-RP-C204, necessities and advantages of direct non-linear analysis can be verified. In this paper, the effect of analysis condition is investigated using parametric study. The results are influenced by the application of failure criteria according to the rule requirements, application of material properties, dropping position, condition of the object, and so on. This study can suggest appropriate determination of the methodology and condition for the dropped object analysis using direct FE analysis.


Radiology ◽  
2004 ◽  
Vol 232 (3) ◽  
pp. 854-859 ◽  
Author(s):  
Lisa M. Ho ◽  
Rendon C. Nelson ◽  
John Thomas ◽  
Edgardo I. Gimenez ◽  
David M. DeLong

2004 ◽  
Vol 8 (1) ◽  
pp. 95-105 ◽  
Author(s):  
Christo Boyadjiev ◽  
Maria Doichinova

Many systems with non-linear heat and mass transfer processes might be unstable at certain conditions. Small disturbances might bring out them of their equilibrium state, after which they achieve itself to a new stable state. The method developed here concerns a non-linear analysis of hydrodynamic stability of the systems with intensive heat and mass transfer. It al lows the determination of the kinetic energy distribution between the main flow and the disturbance, when the equilibrium value of the disturbance amplitude is determined.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shota Ichikawa ◽  
Misaki Hamada ◽  
Hiroyuki Sugimori

AbstractBody weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947, p < 0.001) and abdominal datasets (ρ = 0.869, p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.


Author(s):  
Ben Hunter ◽  
Andrew Greenhalgh ◽  
Bettina Karsten ◽  
Mark Burnley ◽  
Daniel Muniz-Pumares

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