◾ Conventional Methods for Soft Tissue Elasticity Measurement in Vitro

2015 ◽  
pp. 28-43
Keyword(s):  
Soft Tissue ◽  
Tissue Elasticity ◽  
Conventional Methods ◽  
Elasticity Measurement

Numerical Analysis of Elasticity Measurement of Soft Tissues via a 2D Distributed-Deflection Sensor: Significance of Tissue Parameters

2017 ◽  
Author(s):  
Yichao Yang ◽  
Arthur Weidemann ◽  
Charles Tison ◽  
Zhili Hao
Keyword(s):  
Theoretical Model ◽  
Soft Tissue ◽  
Soft Tissues ◽  
Numerical Study ◽  
Tissue Elasticity ◽  
Tissue Thickness ◽  
Compression Depth ◽  
Testing Method ◽  
Tissue Region ◽  
Elasticity Measurement

This paper reports on a numerical study on how the elasticity of soft tissue measured by a Compression-Relaxation (C-R) testing method via a two-dimensional (2D) distributed-deflection sensor varies with the tissue parameters (i.e., elasticity, thickness and in-plane dimension). The 2D sensor entails a polydimethylsiloxane (PDMS) micro structure embedded with a 3×3 sensing-plate/transducer array deposited on a Pyrex substrate. By moving the 2D sensor against a soft tissue region with a pre-defined compression pattern, the average deflection-depth slope of the deflections of the sensing-plate array versus the compression depth of the testing tissue is measured, and is translated to the measured tissue elasticity via a 1D theoretical model. Since the measured tissue elasticity arises from the tissue-sensor interaction, a numerical model, which includes the 2D sensor and a soft tissue underneath, is created in COMSOL to investigate the sensitivity of the measured tissue elasticity to tissue parameters including tissue thickness, in-plane dimension and elasticity. The numerical results reveal that the theoretical model causes a 20% overestimate on the inherent tissue elasticity in the range of 25kPa∼200kPa. The measured tissue elasticity does not vary with tissue thickness when tissue thickness is above 6mm. However, a relatively thin tissue leads to higher measured tissue elasticity. As long as the tissue in-plane dimension is larger than the sensor in-plane dimension, the measured tissue elasticity is insensitive to the tissue in-plane dimension.

Evaluation of 99mTc-Label led Vitamin K4 as an Agent for Testicular Imaging

1991 ◽  
Vol 30 (01) ◽  
pp. 35-39 ◽  
Author(s):  
H. S. Durak ◽  
M. Kitapgi ◽  
B. E. Caner ◽  
R. Senekowitsch ◽  
M. T. Ercan
Keyword(s):  
Soft Tissue ◽  
Room Temperature ◽  
Normal Subjects ◽  
Left Testis ◽  
Wide Range ◽  
Activity Ratios ◽  
The Right ◽  
Radioactivity Levels

Vitamin K4 was labelled with 99mTc with an efficiency higher than 97%. The compound was stable up to 24 h at room temperature, and its biodistribution in NMRI mice indicated its in vivo stability. Blood radioactivity levels were high over a wide range. 10% of the injected activity remained in blood after 24 h. Excretion was mostly via kidneys. Only the liver and kidneys concentrated appreciable amounts of radioactivity. Testis/soft tissue ratios were 1.4 and 1.57 at 6 and 24 h, respectively. Testis/blood ratios were lower than 1. In vitro studies with mouse blood indicated that 33.9 ±9.6% of the radioactivity was associated with RBCs; it was washed out almost completely with saline. Protein binding was 28.7 ±6.3% as determined by TCA precipitation. Blood clearance of 99mTc-l<4 in normal subjects showed a slow decrease of radioactivity, reaching a plateau after 16 h at 20% of the injected activity. In scintigraphic images in men the testes could be well visualized. The right/left testis ratio was 1.08 ±0.13. Testis/soft tissue and testis/blood activity ratios were highest at 3 h. These ratios were higher than those obtained with pertechnetate at 20 min post injection.99mTc-l<4 appears to be a promising radiopharmaceutical for the scintigraphic visualization of testes.

scholarly journals Quantification of assembly forces during creation of head-neck taper junction considering soft tissue bearing: a biomechanical study

Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Toni Wendler ◽  
Torsten Prietzel ◽  
Robert Möbius ◽  
Jean-Pierre Fischer ◽  
Andreas Roth ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Work Experience ◽  
Soft Tissues ◽  
Force Sensor ◽  
Biomechanical Study ◽  
Measuring System ◽  
Wide Range ◽  
Head Neck

Abstract Background All current total hip arthroplasty (THA) systems are modular in design. Only during the operation femoral head and stem get connected by a Morse taper junction. The junction is realized by hammer blows from the surgeon. Decisive for the junction strength is the maximum force acting once in the direction of the neck axis, which is mainly influenced by the applied impulse and surrounding soft tissues. This leads to large differences in assembly forces between the surgeries. This study aimed to quantify the assembly forces of different surgeons under influence of surrounding soft tissue. Methods First, a measuring system, consisting of a prosthesis and a hammer, was developed. Both components are equipped with a piezoelectric force sensor. Initially, in situ experiments on human cadavers were carried out using this system in order to determine the actual assembly forces and to characterize the influence of human soft tissues. Afterwards, an in vitro model in the form of an artificial femur (Sawbones Europe AB, Malmo, Sweden) with implanted measuring stem embedded in gelatine was developed. The gelatine mixture was chosen in such a way that assembly forces applied to the model corresponded to those in situ. A study involving 31 surgeons was carried out on the aforementioned in vitro model, in which the assembly forces were determined. Results A model was developed, with the influence of human soft tissues being taken into account. The assembly forces measured on the in vitro model were, on average, 2037.2 N ± 724.9 N, ranging from 822.5 N to 3835.2 N. The comparison among the surgeons showed no significant differences in sex (P = 0.09), work experience (P = 0.71) and number of THAs performed per year (P = 0.69). Conclusions All measured assembly forces were below 4 kN, which is recommended in the literature. This could lead to increased corrosion following fretting in the head-neck interface. In addition, there was a very wide range of assembly forces among the surgeons, although other influencing factors such as different implant sizes or materials were not taken into account. To ensure optimal assembly force, the impaction should be standardized, e.g., by using an appropriate surgical instrument.

scholarly journals Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jan Oldenburg ◽  
Lisa Maletzki ◽  
Anne Strohbach ◽  
Paul Bellé ◽  
Stefan Siewert ◽  
...  
Keyword(s):  
Wound Healing ◽  
Deep Learning ◽  
Cell Density ◽  
Relative Error ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
Cell Velocity ◽  
Population Scale ◽  
Conventional Methods

Abstract Background Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. Results In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. Conclusion The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

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