Authors: Yasin Mamatjan; Ali Roula; Shabuerjiang Wubuli
Addresses: Princess Margaret Cancer Centre, Toronto, Canada ' Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK ' Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK
Abstract: Magnetic induction tomography (MIT) is a contactless, inexpensive and non-invasive technique for imaging the conductivity distribution inside a body. A time-difference imaging can be used for monitoring the progression of stroke or oedema. However, MIT signals are more sensitive to body movements than the conductivity changes inside the body, because small movements during data acquisition can overwhelm the signals of interest and cause significant image artefacts. Thus, it is crucial to accurately estimate and compensate body movements for image reconstruction or alert clinicians to avoid misinterpretation. We propose frequency domain analysis and statistical approaches for identifying and estimating object movements from MIT data prior to the image reconstruction step. Results show that high amounts of movements totally distorted the images, whereas the proposed approaches produced good performance on elimination of image artefacts and its estimation while maintaining good computational efficiency for patient monitoring.
Keywords: magnetic induction tomography; movement estimation; movement compensation; FFT; fast Fourier transform; ICA; independent component analysis.
International Journal of Biomedical Engineering and Technology, 2018 Vol.27 No.4, pp.290 - 302
Available online: 07 Aug 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article