Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging. (29th June 2021)
- Record Type:
- Journal Article
- Title:
- Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging. (29th June 2021)
- Main Title:
- Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging
- Authors:
- Zhang, Tao
Liu, Xuechao
Zhang, Weirui
Dai, Meng
Chen, Cheng
Dong, Xiuzhen
Liu, Ruigang
Xu, Canhua - Abstract:
- Abstract: Objective . Traditional magnetic induction tomography (MIT) algorithms have problems in reconstruction, such as large area error (AE), blurred boundaries of reconstructed targets, and considerable image noise (IN). As the size and boundary of a lesion greatly affect the treatment plan, more accurate algorithms are necessary to meet clinical needs. Approach . In this study, adaptive threshold split Bregman (ATSB) is proposed for brain injury monitoring imaging in MIT. We established a 3D brain MIT simulation model with the actual anatomical structure and a phantom model and obtained the reconstructed images of single targets in different positions and multiple targets, using the Tikhonov, eigenvalue threshold regularisation (ETR), split Bregman (SB), and ATSB algorithms. Main results . Compared with the Tikhonov and ETR algorithms, the ATSB algorithm reduced the AE by 95% and the IN by 17% in a simulation and reduced the AE by 87% and IN by 6% in phantom experiments. Compared with the SB algorithm, the ATSB algorithm can reduce the difficulty of adjusting parameters and is easier to use in clinical practice. The simulation and phantom experiments results showed that the ATSB algorithm could reconstruct the target size more accurately and could distinguish multiple targets more effectively than the other three algorithms. Significance . The ATSB algorithm could improve the image quality of MIT and better meet the needs of clinical applications and is expected toAbstract: Objective . Traditional magnetic induction tomography (MIT) algorithms have problems in reconstruction, such as large area error (AE), blurred boundaries of reconstructed targets, and considerable image noise (IN). As the size and boundary of a lesion greatly affect the treatment plan, more accurate algorithms are necessary to meet clinical needs. Approach . In this study, adaptive threshold split Bregman (ATSB) is proposed for brain injury monitoring imaging in MIT. We established a 3D brain MIT simulation model with the actual anatomical structure and a phantom model and obtained the reconstructed images of single targets in different positions and multiple targets, using the Tikhonov, eigenvalue threshold regularisation (ETR), split Bregman (SB), and ATSB algorithms. Main results . Compared with the Tikhonov and ETR algorithms, the ATSB algorithm reduced the AE by 95% and the IN by 17% in a simulation and reduced the AE by 87% and IN by 6% in phantom experiments. Compared with the SB algorithm, the ATSB algorithm can reduce the difficulty of adjusting parameters and is easier to use in clinical practice. The simulation and phantom experiments results showed that the ATSB algorithm could reconstruct the target size more accurately and could distinguish multiple targets more effectively than the other three algorithms. Significance . The ATSB algorithm could improve the image quality of MIT and better meet the needs of clinical applications and is expected to promote brain injury monitoring imaging via MIT. … (more)
- Is Part Of:
- Physiological measurement. Volume 42:Number 6(2021)
- Journal:
- Physiological measurement
- Issue:
- Volume 42:Number 6(2021)
- Issue Display:
- Volume 42, Issue 6 (2021)
- Year:
- 2021
- Volume:
- 42
- Issue:
- 6
- Issue Sort Value:
- 2021-0042-0006-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-06-29
- Subjects:
- brain injury monitoring imaging -- magnetic induction tomography -- reconstruction algorithm -- simulation -- split Bregman algorithm -- three-dimensional head model
Physiology -- Measurement -- Periodicals
Patient monitoring -- Periodicals
612 - Journal URLs:
- http://ioppublishing.org/ ↗
http://iopscience.iop.org/0967-3334 ↗ - DOI:
- 10.1088/1361-6579/ac05d4 ↗
- Languages:
- English
- ISSNs:
- 0967-3334
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 17430.xml