Predicting MRI RF Exposure for Complex-shaped Medical Implants Using Artificial Neural Network

Lan, Qianlong; Zheng, Jianfeng; Chen, Ji

doi:10.1109/apusncursinrsm.2019.8889347

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…A three-layer feed-forward network was used to fit the SAR m , [12][13][14] which has two hidden layers with 15 sigmoid neurons each and one output layer with a linear neuron, as shown in Figure 2B.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Genetic algorithm search for the worst‐case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks

Zheng

Lan

Kainz

et al. 2020

Magnetic Resonance in Med

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Purpose This paper presents a method to search for the worst‐case configuration leading to the highest RF exposure for a multiconfiguration implantable fixation system under MRI. Methods A two‐step method combining an artificial neural network and a genetic algorithm is developed to achieve this purpose. In the first step, the level of RF exposure in terms of peak 1‐g and/or 10‐g averaged specific absorption rate (SAR1g/10g), related to the multiconfiguration system, is predicted using an artificial neural network. A genetic algorithm is then used to search for the worst‐case configuration of this multidimensional nonlinear problem within both the enumerated discrete sample space and generalized continuous sample space. As an example, a generic plate system with a total of 576 configurations is used for both 1.5T and 3T MRI systems. Results The presented method can effectively identify the worst‐case configuration and accurately predict the SAR1g/10g with no more than 20% of the samples in the studied discrete sample space, and can even predict the worst case in the generalized continuous sample space. The worst‐case prediction error in the generalized continuous sample space is less than 1.6% for SAR1g and less than 1.3% for SAR10g compared with the simulation results. Conclusion The combination of an artificial neural network with genetic algorithm is a robust technique to determine the worst‐case RF exposure level for a multiconfiguration system, and only needs a small amount of training data from the entire system.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Genetic algorithm search for the worst‐case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks

Zheng

Lan

Kainz

et al. 2020

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…To overcome this predicament, one of the techniques is to replace the GMM with reliable models that can tackle with a massive amount of data and achieve higher accuracy. With the surging of deep learning, neural networks can model multiple events and learn richer representations that have the potential to learn better models of nonlinear data [15][16][17]. With multiple layers, deep neural networks (DNNs) [18,19] perform well on decision boundary and feature engineering problems by using a massive amount of data [20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Zhang

Chen

2021

Applied Sciences

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In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

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Predicting MRI RF Exposure for Passive Implantable Medical Devices Using a Mesh-based Convolutional Neural Network

Lan

Zheng

Chang

et al. 2021

2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)

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Predicting MRI RF Exposure for Complex-shaped Medical Implants Using Artificial Neural Network

Cited by 6 publications

References 3 publications

Genetic algorithm search for the worst‐case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks

Genetic algorithm search for the worst‐case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Predicting MRI RF Exposure for Passive Implantable Medical Devices Using a Mesh-based Convolutional Neural Network

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