A computational framework for network modeling of fibrous biological tissue deformation and rupture

Recently there has been a growing interest to develop innovative surgical needles for percutaneous interventional procedures. Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets, such as a biopsy procedure to assess cancerous cells and tumors. One of the major issues in needle steering is the force during insertion, also known as the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. It has been well studied that tissue deformation causes the needle to deviate from its target thus causing an ineffective procedure. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs, specifically the insertion force, has been well recognized as a major challenge. Overcoming such obstacle by development of robust numerical models will enable realistic force feedback to the user during surgical simulation. This study investigates feasibility of predicting the insertion force of bevel-tip needles based on experimental data using neural network modeling. Simulation of the proposed neural network model is performed using Kera’s Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using a specially designed automated needle insertion test setup. Input-output datasets are generated where the inputs are defined as bevel-tip angles and gel tissue phantom stiffness, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train a neural network. Its performance is then evaluated using different and unseen input-output dataset. This paper shows that the proposed neural network model accurately predicts the insertion force.

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Use of Digital Image Correlation Method to Measure Bio-Tissue Deformation

Coatings ◽

10.3390/coatings11080924 ◽

2021 ◽

Vol 11 (8) ◽

pp. 924

Author(s):

Terry Yuan-Fang Chen ◽

Nhat Minh Dang ◽

Zhao-Ying Wang ◽

Liang-Wei Chang ◽

Wei-Yu Ku ◽

...

Keyword(s):

Digital Image Correlation ◽

Measurement System ◽

Digital Image ◽

Biological Tissue ◽

Correlation Method ◽

Image Correlation ◽

Optical Parameters ◽

Tissue Deformation ◽

Subset Size ◽

Digital Image Correlation Method

Traditionally, strain gauge, extensometer, and reflection tracking markers have been used to measure the deformation of materials under loading. However, the anisotropy and inhomogeneity of most biological materials restricted the accessibility of the real strain field. Compared to the video extensometer, digital image correlation has the advantage of providing full-field displacement as well as strain information. In this study, a digital image correlation method (DIC) measurement system was employed for chicken breast bio-tissue deformation measurement. To increase the contrast for better correlation, a mixture of ground black pepper and white sesame was sprayed on the surface of samples. The first step was to correct the distorted image caused by the lens using the inverse distorted calibration method and then the influence of subset size and correlation criteria, sum of squared differences (SSD), and zero-normalized sum of squared differences (ZNSSD) were investigated experimentally for accurate measurement. Test results of the sample was translated along the horizontal direction from 0 mm to 3 mm, with an increment of 0.1 mm and the measurement result was compared, and the displacement set on the translation stage. The result shows that the error is less than 3%, and accurate measurement can be achieved with proper surface preparation, subset size, correlation criterion, and image correction. Detailed examination of the strain values show that the strain εx is proportional to the displacement of crosshead, but the strain εy indicates the viscoelastic behavior of tested bio-tissue. In addition, the tested bio-tissue’s linear birefringence extracted by a Mueller matrix polarimetry is for comparison and is in good agreement. As noted above, the integration of the optical parameter measurement system and the digital image correlation method is proposed in this paper to analyze the relationship between the strain changes and optical parameters of biological tissue, and thus the relative optic-stress coefficient can be significantly characterized if Young's modulus of biological tissue is known.

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Forward Problem of Time-Resolved Diffuse Optical Tomography Considering Biological Tissue Deformation

Computational Biomechanics for Medicine ◽

10.1007/978-3-319-28329-6_5 ◽

2016 ◽

pp. 51-62

Author(s):

A. Y. Potlov ◽

T. I. Avsievich ◽

S. V. Frolov ◽

S. G. Proskurin

Keyword(s):

Biological Tissue ◽

Optical Tomography ◽

Diffuse Optical Tomography ◽

Forward Problem ◽

Tissue Deformation ◽

Time Resolved ◽

Problem Of Time

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Multiproduct Humanitarian Healthcare Supply Chains: A Network Modeling and Computational Framework

SSRN Electronic Journal ◽

10.2139/ssrn.1636294 ◽

2012 ◽

Cited By ~ 8

Author(s):

Anna Nagurney ◽

Min Yu ◽

Qiang Qiang

Keyword(s):

Supply Chains ◽

Network Modeling ◽

Computational Framework

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Advances in Microprobe Analysis Applied to Muscle, Nerve and Gland

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100054601 ◽

1982 ◽

Vol 40 ◽

pp. 404-407

Author(s):

T. E. Hutchinson ◽

D. E. Johnson ◽

A. C. Lee ◽

E. Y. Wang

Keyword(s):

Biological Tissue ◽

Microprobe Analysis ◽

Physiological State ◽

External Environment ◽

Physiological Relevance ◽

Muscle Nerve ◽

Technique Development

Microprobe analysis of biological tissue is now in the end phase of transition from instrumental and technique development to applications pertinent to questions of physiological relevance. The promise,implicit in early investigative efforts, is being fulfilled to an extent much greater than many had predicted. It would thus seem appropriate to briefly report studies exemplifying this, ∿. In general, the distributions of ions in tissue in a preselected physiological state produced by variations in the external environment is of importance in elucidating the mechanisms of exchange and regulation of these ions.

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Complementary Z-Contrast Imaging and Digital Image Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100118394 ◽

1985 ◽

Vol 43 ◽

pp. 304-305

Author(s):

K. N. Colonna ◽

G. Oliphant

Keyword(s):

Image Processing ◽

Heavy Metal ◽

Digital Image Processing ◽

Digital Image ◽

Biological Tissue ◽

Biological Imaging ◽

Tissue Preparation ◽

Contrast Imaging ◽

Imaging Tool ◽

Z Contrast

Harmonious use of Z-contrast imaging and digital image processing as an analytical imaging tool was developed and demonstrated in studying the elemental constitution of human and maturing rabbit spermatozoa. Due to its analog origin (Fig. 1), the Z-contrast image offers information unique to the science of biological imaging. Despite the information and distinct advantages it offers, the potential of Z-contrast imaging is extremely limited without the application of techniques of digital image processing. For the first time in biological imaging, this study demonstrates the tremendous potential involved in the complementary use of Z-contrast imaging and digital image processing.Imaging in the Z-contrast mode is powerful for three distinct reasons, the first of which involves tissue preparation. It affords biologists the opportunity to visualize biological tissue without the use of heavy metal fixatives and stains. For years biologists have used heavy metal components to compensate for the limited electron scattering properties of biological tissue.

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