Focusing Sensor Design for Open Electrical Impedance Tomography Based on Shape Conformal Transformation

Wang, Yu; Ren, Shangjie; Dong, Feng

doi:10.3390/s19092060

Cited by 9 publications

(5 citation statements)

References 31 publications

(35 reference statements)

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“…However, these may not sufficiently address the decrease in out of plane sensitivity towards the deformable contact surface. Potential solutions to this problem are to further increase the SNR of the system to ensure these small changes still detected, or improved reconstruction algorithms targeting this problem [17], [18]. Placing electrodes onto the deformable surface would greatly increase the sensitivity.…”

Section: Discussionmentioning

confidence: 99%

Tactile Sensor for Minimally Invasive Surgery Using Electrical Impedance Tomography

Avery

Shulakova

Runciman

et al. 2020

IEEE Trans. Med. Robot. Bionics

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Whilst offering numerous benefits to patients, minimally invasive surgery (MIS) has a disadvantage in the loss of tactile feedback to the surgeon, traditionally offering valuable qualitative tissue assessment, such as tumour identification and localisation. Tactile sensors aim to overcome this loss of sensation by detecting tissue characteristics such as stiffness, composition and temperature. Tactile sensors have previously been incorporated into MIS robotic end effectors, which require lengthy scanning procedures due to localised sensitivity. Distributed tactile sensors, or "artificial skin" offer a map of tissue properties in a single instance but are often not suitable for MIS applications due to limited biocompatibility or large collapsed volumes. We propose a deployable, soft, tactile sensor with a deformable saline chamber and integrated Electrical Impedance Tomography (EIT) electrodes. During contact with tissue, the saline is displaced from the chamber and the lesion size and stiffness can be inferred from the resultant impedance changes. Through optimisation of the EIT measurement protocol and hardware the sensor was capable of localising the centre of mass of palpation targets within 1.5 mm in simulation and 2.3-4.6mm in phantom experiments. Reconstructed image metrics differentiated target objects from 8-30 mm.

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Section: Discussionmentioning

confidence: 99%

Tactile Sensor for Minimally Invasive Surgery Using Electrical Impedance Tomography

Avery

Shulakova

Runciman

et al. 2020

IEEE Trans. Med. Robot. Bionics

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“…In most practical applications of EIT, the electrodes are always with the same size and placed evenly on the boundary of the observation domain such as the chest. However, scholars have proved that with this electrode arrangement, the reconstruction quality of the inclusions near and far from the electrode is relatively poor, but this problem can be effectively improved by optimizing the sensor array [20]. In this paper, a known chest boundary shape was considered, e.g., one extracted from a pre-collected lung CT image.…”

Section: Introductionmentioning

confidence: 99%

Case-Specific Focal Sensor Design for Cardiac Electrical Impedance Tomography

Zhang

Wang

Ren

et al. 2022

Sensors

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Electrical impedance tomography (EIT) is a non-invasive detection technology that uses the electrical response value at the boundary of an observation field to image the conductivity changes in an area. When EIT is applied to the thoracic cavity of the human body, the conductivity change caused by the heartbeat will be concentrated in a sub-region of the thoracic cavity, that is, the heart region. In order to improve the spatial resolution of the target region, two sensor optimization methods based on conformal mapping theory were proposed in this study. The effectiveness of the proposed method was verified by simulation and phantom experiment. The qualitative analysis and quantitative index evaluation of the reconstructed image showed that the optimized model could achieve higher imaging accuracy of the heart region compared with the standard sensor. The reconstruction results could effectively reflect the periodic diastolic and systolic movements of the heart and had a better ability to recognize the position of the heart in the thoracic cavity.

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“…Magnetic detection electrical impedance tomography (MDEIT) is a novel imaging modality to reconstruct the conductivity distribution by measuring the magnetic flux density surrounding the imaging object [5], [6]. Considering that the measurement of magnetic flux density does not require surface contact, the sensor fixing problems of tradition Electrical impedance tomography (EIT) [7] is eliminated in MDEIT and a great number of measurements can be recorded with precise detector positions [8]. Therefore, compared to the traditional EIT, MDEIT has the advantages of non-contact sensing, less electrodes, and extensive application domains [9], [10].…”

Section: Introductionmentioning

confidence: 99%

A Stacked Autoencoder Neural Network Algorithm for Breast Cancer Diagnosis With Magnetic Detection Electrical Impedance Tomography

Chen

et al. 2020

IEEE Access

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Magnetic detection electrical impedance tomography (MDEIT) is a novel imaging technique that aims to reconstruct the conductivity distribution with electrical current injection and the external magnetic flux density measurement by magnetic sensors. Aiming at improving the resolution and accuracy of MDEIT and providing an efficient imaging method for breast cancer diagnosis, a new algorithm based on stacked auto-encoder (SAE) neural network is proposed. Both numerical simulation and phantom experiments are done to verify its feasibility. In the numerical simulation, an amount of sample data with different conductivity distribution are calculated. Then a neural network model is established and trained by training these samples. Finally, the conductivity distribution of an imaging target with the anomaly location can be reconstructed by the network model. The reconstruction result of the SAE algorithm is compared with the reconstruction results of the traditional sensitivity matrix (SM) algorithm and the back propagation (BP) neural network algorithm. Under the noise of 30dB, the relative errors of BP algorithm, SM algorithm and SAE algorithm are 137.19%, 24.90% and 15.28% respectively. Result shows by the SAE algorithm, the location of anomalies is reconstructed more accurately, the conductivity value is more closely to the real one and the anti-noise performance is more robust. At last, a breast phantom experiment by self-made platforms is completed to verify the application feasibility of the new algorithm. The relative reconstruction error of conductivity by proposed SAE algorithm can be reduced to 14.56%. The results show that by SAE algorithm, MDEIT can be a promising approach in clinical diagnosis of breast cancer, and it also provide more potential application prospect for the extensive application of MDEIT. INDEX TERMS Breast cancer diagnosis, inverse problem, magnetic detection electrical impedance tomography, stacked auto-encoder.

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Focusing Sensor Design for Open Electrical Impedance Tomography Based on Shape Conformal Transformation

Cited by 9 publications

References 31 publications

Tactile Sensor for Minimally Invasive Surgery Using Electrical Impedance Tomography

Tactile Sensor for Minimally Invasive Surgery Using Electrical Impedance Tomography

Case-Specific Focal Sensor Design for Cardiac Electrical Impedance Tomography

A Stacked Autoencoder Neural Network Algorithm for Breast Cancer Diagnosis With Magnetic Detection Electrical Impedance Tomography

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