Soft Tissue Characterization Using Genetic Algorithm

2013 ◽  
pp. 97-112
Keyword(s):  
Genetic Algorithm ◽  
Soft Tissue ◽  
Tissue Characterization

Complementary imaging of ultrasound and PET/CT: A new opportunity?

2021 ◽  
pp. 1-16
Author(s):  
Janine Rennert ◽  
Jirka Grosse ◽  
Ingo Einspieler ◽  
Wolf Bäumler ◽  
Christian Stroszczynski ◽  
...  
Keyword(s):  
High Resolution ◽  
Soft Tissue ◽  
Therapeutic Approach ◽  
Tissue Characterization ◽  
Tumor Detection ◽  
Tracer Uptake ◽  
Liver Lesions ◽  
Definite Diagnosis ◽  
Renal Lesions ◽  
Pet Ct

AIM: To evaluate the effectiveness of complementary imaging of high-resolution ultrasound including CEUS with PET/CT for tissue characterization and tumor detection. MATERIAL AND METHODS: 100 patients were examined with PET/CT and US/CEUS between January 2018 until February 2020. All patients underwent PET/CT followed by selective US/CEUS within 4 weeks. Comparison regarding concordant or diverging findings in PET/CT and US. Analysis of the differences concerning the lesions number of found by PET/CT and US/CEUS or the possibility of a secured diagnosis following ultrasound causing therapeutic changes. RESULTS: Diverging findings regarding the number of liver lesions in PET/CT and CEUS were found in 35 out of 64 patients (54%). Regarding renal lesions, a more definite diagnosis following ultrasound, causing a change of therapeutic approach, was achieved in 89%. Concordant results in PET/CT and US were found in 83%of patients with splenic and nodal findings. In 78%of patients with increased musculoskeletal or soft tissue tracer uptake, US was able to make a secured diagnosis with therapeutic changes. CONCLUSION: The present results indicate a strong benefit of complementary imaging of PET/CT and selective, high-resolution ultrasound especially in patients with liver, renal and musculoskeletal or soft tissue findings.

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

Study on the Mechanical Properties of Tissue-Mimicking Phantom Composites Using Ultrasound Indentation

2007 ◽  
Vol 334-335 ◽  
pp. 133-136
Author(s):  
Hang Yin Ling ◽  
P. Carrie Choi ◽  
Y.P. Zheng ◽  
Alan Kin Tak Lau
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Soft Tissue ◽  
Soft Tissues ◽  
Load Cell ◽  
Indentation Technique ◽  
Deformation Curves ◽  
Ultrasound Indentation ◽  
Indentation Process

This paper demonstrates the use of ultrasound (US) indentation technique for estimating the mechanical properties of tissue- mimicking phantom composites. A tissue-mimicking phantom composite is used to simulate two-layer soft tissue in human. Investigation on the mechanical properties of the phantom composites is extremely important for the understanding of the viscoelastic behaviours of soft tissues and the validation of our proposed US indentation system. The hand-held indentation probe embedded with a US transducer and a load cell together with a US pulser/ receiver. The output of the whole indentation process can be illustrated as force-deformation curves. The mechanical properties of the phantom composites can be estimated by analyzing the force-deformation curves using genetic algorithm (GA).

scholarly journals Integration of Geometrical Boundary Conditions on Soft Tissue Characterization under large deformation

2008 ◽  
Author(s):  
Bummo Ahn ◽  
Jung Kim
Keyword(s):  
Soft Tissue ◽  
Boundary Conditions ◽  
Large Deformation ◽  
Tissue Characterization ◽  
Surface Deformation ◽  
Viscoelastic Model ◽  
Fe Modeling ◽  
Porcine Liver ◽  
Radius Of Influence ◽  
Geometrical Boundary

Soft tissue characterization with finite element (FE) modeling is important to develop a realistic model for medical simulation, since it is possible to display complex tool-tissue interactions during medical interventions. However, it is difficult to integrate large deformation and geometrical boundary conditions to the FE computations. In this paper, the force responses and surface deformation fields of the tissues against the indentation were measured by a force transducer and three-dimensional optical system. Large indentation experiments on porcine liver were performed to estimate the radius of influence from the indented point up to 8 mm indentation and to measure the force response for 7mm indentation. The radius of influence region was plotted against various indentation depths and indenter shapes, and it could be used to determine the model size for the characterization. The tissue behavior of large deformation considering influence of the boundary conditions was characterized with FE modeling via hyperelastic and linear viscoelastic model.

A Combined FEM/Genetic Algorithm for Vascular Soft tissue Elasticity Estimation

2006 ◽  
Vol 6 (3) ◽  
pp. 93-102 ◽  
Author(s):  
Ahmad S. Khalil ◽  
Brett E. Bouma ◽  
Mohammad R. Kaazempur Mofrad
Keyword(s):  
Genetic Algorithm ◽  
Soft Tissue ◽  
Tissue Elasticity
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