Detail publikace

An Effective Detection of Conductivity Changes in Biologic Tissue

Originální název

An Effective Detection of Conductivity Changes in Biologic Tissue

Anglický název

An Effective Detection of Conductivity Changes in Biologic Tissue

Jazyk

en

Originální abstrakt

There are described positive and negative properties of weighty recent numerical techniques for a solution of electrical impedance tomography (EIT) inverse problem and their influences to the quality of image reconstruction. There are two different types of EIT image reconstructions, static and dynamic EIT. In static EIT, only the absolute conductivity in each element is computed and a picture of the internal organs of different conductivity is imaged. In dynamic EIT, temporal variations in conductivity are computed. Both types can be very useful especially in medical applications. The aim of this paper is to propose and realize a new algorithm for a successful detection of conductivity changes in biologic tissues. It is desirable to obtain high-quality reconstruction process because the medical imaging is a non-invasive and very helpful technique for a detection of pulmonary emboli, non-invasive monitoring of a heart function and a blood flow, or for the breast cancer detection. To obtain the stable reconstruction process for an effective detection of conductivity changes in biologic tissue we created a new algorithm based on Tikhonov regularization method (TMR) and level set method (LSM). An image reconstruction of EIT is an inverse problem. Solution is very dependent on initial parameters. There are tested two parameters in this article, parameter of regularization alfa and starting value of conductivity. The results are presented for tested parameters, theirs effect to reconstruction quality and speed of solution.

Anglický abstrakt

There are described positive and negative properties of weighty recent numerical techniques for a solution of electrical impedance tomography (EIT) inverse problem and their influences to the quality of image reconstruction. There are two different types of EIT image reconstructions, static and dynamic EIT. In static EIT, only the absolute conductivity in each element is computed and a picture of the internal organs of different conductivity is imaged. In dynamic EIT, temporal variations in conductivity are computed. Both types can be very useful especially in medical applications. The aim of this paper is to propose and realize a new algorithm for a successful detection of conductivity changes in biologic tissues. It is desirable to obtain high-quality reconstruction process because the medical imaging is a non-invasive and very helpful technique for a detection of pulmonary emboli, non-invasive monitoring of a heart function and a blood flow, or for the breast cancer detection. To obtain the stable reconstruction process for an effective detection of conductivity changes in biologic tissue we created a new algorithm based on Tikhonov regularization method (TMR) and level set method (LSM). An image reconstruction of EIT is an inverse problem. Solution is very dependent on initial parameters. There are tested two parameters in this article, parameter of regularization alfa and starting value of conductivity. The results are presented for tested parameters, theirs effect to reconstruction quality and speed of solution.

BibTex


@inproceedings{BUT34349,
  author="Tomáš {Kříž} and Jan {Mikulka} and Jarmila {Dědková}",
  title="An Effective Detection of Conductivity Changes in Biologic Tissue",
  annote="There are described positive and negative properties of weighty recent numerical techniques for a solution of electrical impedance tomography (EIT) inverse problem and their influences to the quality of image reconstruction. There are two different types of EIT image reconstructions, static and dynamic EIT. In static EIT, only the absolute conductivity in each element is computed and a picture of the internal organs of different conductivity is imaged. In dynamic EIT, temporal variations in conductivity are computed. Both types can be very useful especially in medical applications. The aim of this paper is to propose and realize a new algorithm
for a successful detection of conductivity changes in biologic tissues. It is desirable to obtain high-quality reconstruction process because the medical imaging is a non-invasive and very helpful technique for a detection of pulmonary emboli, non-invasive monitoring of a heart function and a blood flow, or for the breast cancer detection. To obtain the stable reconstruction process for an effective detection of conductivity changes in biologic tissue we created a new algorithm based on Tikhonov regularization method (TMR) and level set method (LSM). An image reconstruction of EIT is an inverse problem. Solution is very dependent on initial parameters. There are tested two parameters in this article, parameter of regularization alfa and starting value of conductivity. The results are presented for tested parameters, theirs effect to reconstruction quality and speed of solution.",
  booktitle="Proceedings of PIERS 2010 in Cambridge",
  chapter="34349",
  howpublished="online",
  year="2010",
  month="july",
  pages="575--579",
  type="conference paper"
}