Image Reconstruction for Multi-frequency Electromagnetic Tomography based on Multiple Measurement Vector Model

Jinxi Xiang, Yonggui Dong, Yunjie Yang

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Imaging the bio-impedance distribution of a biological sample can provide understandings about the sample’s electrical properties which is an important indicator of physiological status. This paper presents a multi-frequency electromagnetic tomography (mfEMT) technique for biomedical imaging. The
system consists of 8 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To exploit the frequency correlation among each measurement, we reconstruct multiple frequency data simultaneously based on the Multiple Measurement Vector (MMV) model. The MMV problem is solved by using a sparse Bayesian learning method that is especially effective for sparse distribution. Both simulations and experiments have been conducted to verify the performance of the method. Results show that by taking advantage of multiple measurements, the proposed method is more robust to noisy data for ill-posed problems compared to the commonly used single measurement
vector model.
Original languageEnglish
Publication statusAccepted/In press - 2020
Event2020 IEEE International Instrumentation and Measurement Technology Conference - Dubrovnik, Dubrovnik, Croatia
Duration: 25 May 202028 May 2020
https://i2mtc2020.ieee-ims.org/

Conference

Conference2020 IEEE International Instrumentation and Measurement Technology Conference
Abbreviated titleI2MTC2020
Country/TerritoryCroatia
CityDubrovnik
Period25/05/2028/05/20
Internet address

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