지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.11
- 수록면
- 263 - 266 (4page)
이용수
초록· 키워드
오류제보하기
Magnetic resonance electrical impedance tomography (MREIT) aims to visualize a conductivity distribution inside the human body. In MREIT, we inject current to produce a current density J and magnetic flux density B inside the body, and measure Bz which is the z-component of B using an MRI scanner with its main field in z direction. Using fundamental relations between the measured Bz and the conductivity, we can reconstruct cross-sectional images of the internal conductivity distribution. We adopt the harmonic Bz algorithm, which is based on the key observation that ▽²Bz reveals changes in log of the conductivity distribution along any equipotential curve on an imaging slice. When we apply the method to measured Bz data from animal or human subjects, however, there occur a few technical difficulties that are mainly related with measurement errors in Bz data especially in a local region where MR signals are very small. This demands innovative data processing methods based on a rigorous mathematical analysis of such defective data. We carefully investigate sources of the error and its adverse effects on the image reconstruction process. We suggest a new error propagation blocking algorithm to prevent defective data at one local region from influencing badly on conductivity images of other regions. We experimentally examine the performance of the proposed method by comparing reconstructed images with and without applying the error propagation blocking algorithm. We found that the error blocking algorithm improves the accuracy of reconstructed conductivity images.
목차
ABSTRACT
1. INTRODUCTION
2. ERROR ANALYSIS AND A BLOCKING ALGORITHM
3. NUMERICAL SIMULATIONS AND EXPERIMENTS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-410-000443939