Cellular neural network modelling of soft tissue dynamics for surgical simulation

Zhang, Jinao; Zhong, Yongmin; Smith, Julian A.; Gu, Chengfan

doi:10.3233/thc-171337

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…The hardware acceleration of cellular neural networks using FPGAs is actually the parallel signal processing capabilities of FPGAs and the parallelization of algorithms. Cellular networks are a special type of convolutional neural network [24][25]. There is no data correlation between different convolutional operations in the same layer of a convolutional neural network.…”

Section: B Feasibility Analysis Of Parallel Implementation Of Cellulmentioning

confidence: 99%

Retracted: Computer Medical Image Segmentation Based on Neural Network

Wang

2020

IEEE Access

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Image segmentation in medical imaging has long been a problem in radiological image processing. Most of the image segmentation methods in traditional vision algorithms are difficult to achieve high-resolution image segmentation due to the complexity of the algorithm. This paper proposes an image segmentation method based on an optimized cellular neural network. This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. We then applied the method to a computer-aided system to classify tumor lesions in mammograms. Finally, we propose an FPGA-based multilevel optimization architecture for energy-efficient cellular neural networks. The optimization scheme includes three levels: system level, module level, and design space. This solution improves computing performance by increasing system parallelism, using data reuse technology to fully utilize loading bandwidth, and using data quantization to reduce computational redundancy. It also introduces pipeline and dual cache structures to optimize memory access, and analyzes the limited resources through the Roofline model. System for best performance. The experimental results show that the FPGA accelerator in this paper can improve unit performance by 34% compared with other existing research work. The nonlinear quantified cellular neural network proposed in this paper can reduce LUT resource consumption by 74% and energy of 48.2%. Compared with the original network, in the two projection position segmentation results of the mammogram, only 1.5% and 0.6% of the accuracy loss, respectively.

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Section: B Feasibility Analysis Of Parallel Implementation Of Cellulmentioning

confidence: 99%

Retracted: Computer Medical Image Segmentation Based on Neural Network

Wang

2020

IEEE Access

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“…The critical time step decreases as the mesh becomes finer for the blood vessels, increasing the number of simulation steps that prolongs the total computation time; therefore, a careful selection of the included blood vessels and other fine details of the liver needs to be considered for achieving a balance of critical time steps for efficient simulation. For numerical stability, it was reported that neural networks [60,61], adaptive semi-explicit/explicit time marching [59] and unconditionally stable explicit scheme [62] could achieve stable numerical analysis for non-linear dynamic systems; these methods may be incorporated into the proposed method for stable dynamic bio-heat transfer analysis under soft tissue deformation.…”

Section: Discussionmentioning

confidence: 99%

Fast computation of soft tissue thermal response under deformation based on fast explicit dynamics finite element algorithm for surgical simulation

Zhang

Chauhan

2020

Computer Methods and Programs in Biomedicine

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Background and Objectives: During thermal heating surgical procedures such as electrosurgery, thermal ablative treatment and hyperthermia, soft tissue deformation due to surgical tool-tissue interaction and patient movement can affect the distribution of thermal energy induced. Soft tissue temperature must be obtained from the deformed tissue for precise delivery of thermal energy. However, the classical Pennes bio-heat transfer model can handle only the static non-moving state of tissue. In addition, in order to enable a surgeon to visualize the simulated results immediately, the solution procedure must be suitable for real-time thermal applications.Methods: This paper presents a formulation of bio-heat transfer under the effect of soft tissue deformation for fast or near real-time tissue temperature prediction, based on fast explicit dynamics finite element algorithm (FED-FEM) for transient heat transfer. The proposed thermal analysis under deformation is achieved by transformation of the unknown deformed tissue state to the known initial static state via a mapping function. The appropriateness and effectiveness of the proposed formulation are evaluated on a realistic virtual human liver model with blood vessels to demonstrate a clinically relevant scenario of thermal ablation of hepatic cancer.Results: For numerical accuracy, the proposed formulation can achieve a typical 10 −3 level of normalized relative error at nodes and between 10 −4 and 10 −5 level of total errors for the simulation, by comparing solutions against the commercial finite element analysis package. For computation time, the proposed formulation under tissue deformation with anisotropic temperature-dependent properties consumes 2.518 × 10 −4 for one element thermal loads computation, compared to 2.237 × 10 −4 for the formulation without deformation which is 0.89 times of the former. Comparisons with three other formulations for isotropic and temperature-independent properties are also presented.Conclusions: Compared to conventional methods focusing on numerical accuracy, convergence and stability, the proposed formulation focuses on computational performance for fast tissue thermal analysis. Compared to the classical Pennes model that handles only the static state of tissue, the proposed formulation can achieve fast thermal analysis on deformed states of tissue and can be applied in addition to tissue deformable models for nonlinear heating analysis at even large deformation of soft tissue, leading to great translational potential in dynamic tissue temperature analysis and thermal dosimetry computation for computer-integrated medical education and personalized treatment.

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“…Some newly developed numerical methods such as the 3D alpha FEM [12] and edge-based smoothed FEM [13] are focused more on the numerical accuracy, convergence, and stability rather than the real-time computational performance, the proposed algorithm may be incorporated into these methods to enable real-time Pennes bio-heat transfer simulation. In terms of numerical stability, it was reported that neural networks [53,54] can achieve stable simulation in the dynamic mechanical systems; they may be incorporated into the proposed method for stable dynamic bio-heat transfer.…”

Section: Discussionmentioning

confidence: 99%

Real-time computation of bio-heat transfer in the fast explicit dynamics finite element algorithm (FED-FEM) framework

Zhang

Chauhan

2019

Numerical Heat Transfer, Part B: Fundamentals

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Real-time analysis of bio-heat transfer is very beneficial in improving clinical outcomes of hyperthermia and thermal ablative treatments but challenging to achieve due to large computational costs. This paper presents a fast numerical algorithm well suited for real-time solutions of bio-heat transfer, and it achieves real-time computation via the (i) computationally efficient explicit dynamics in the temporal domain, (ii) element-level thermal load computation, (iii) computationally efficient finite elements, (iv) explicit formulation for unknown nodal temperature, and (v) pre-computation of constant simulation matrices and parameters, all of which lead to a significant reduction in computation time for fast run-time computation. The proposed methodology considers temperature-dependent thermal properties for nonlinear characteristics of bio-heat transfer in soft tissue. Utilising a parallel execution, the proposed method achieves computation time reduction of 107.71 and 274.57 times compared to those of with and without parallelisation of the commercial finite element codes if temperature-dependent thermal properties are considered, and 303.07 and 772.58 times if temperature-independent thermal properties are considered, far exceeding the computational performance of the commercial finite element codes, presenting great potential in real-time predictive analysis of tissue temperature for planning, optimisation and evaluation of thermo-therapeutic treatments.Various techniques were reported to facilitate the computational performance of numerical methods for solutions of the bio-heat transfer equation, and most of the existing techniques were based on the finite difference method (FDM). Schwenke et al.[20] studied a Graphics Processing Unit (GPU)-accelerated FDM to achieve fast simulation of focused ultrasound treatment via a parallel execution of the solution procedure on GPU; however, FDM requires a regular computation grid to approximate spatial derivatives, but human organs/tissue are irregular shapes with curvilinear boundaries, resulting in inaccuracy for accommodating soft tissue material properties and enforcing boundary conditions. He and Liu [21] developed a parallel alternating direction explicit (ADE) scheme based on FDM to solve the bio-heat equation; Carluccio et al. [22] devised a spatial filter method based on Fast Fourier Transform (FFT) with FDM to reduce computation time; Kalantzis et al.[23] studied a GPU-accelerated FDM for fast simulation of focused ultrasound thermal ablation; Dillenseger and Esneault [24] also studied an FFT-based FDM method; Chen et al. [25] presented a GPU-accelerated microwave imaging method based on FDM to monitor temperature in thermal therapy; Johnson and Saidel [26] studied an FDM-based methodology for fast simulation of radiofrequency tumour ablation; and Niu et al. [27] employed cellular neural networks (CNN) based on FDM for efficient estimation of tissue temperature field. Despite the improved computational performance by the above methods, they all suffer from ...

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Cellular neural network modelling of soft tissue dynamics for surgical simulation

Cited by 10 publications

References 22 publications

Retracted: Computer Medical Image Segmentation Based on Neural Network

Retracted: Computer Medical Image Segmentation Based on Neural Network

Fast computation of soft tissue thermal response under deformation based on fast explicit dynamics finite element algorithm for surgical simulation

Real-time computation of bio-heat transfer in the fast explicit dynamics finite element algorithm (FED-FEM) framework

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