Optimization of the detection coil of high-Tc superconducting quantum interference device-based nuclear magnetic resonance for discriminating a minimum amount of liver tumor of rats in microtesla fields

Hsin Hsien Chen, Kai Wen Huang*, Hong Chang Yang, Herng Er Horng, Shu Hsien Liao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This study presents an optimization of the detection coil of high-T c superconducting quantum interference device (SQUID)-based nuclear magnetic resonance (NMR) in microtesla fields for discriminating a minimum amount of liver tumor in rats by characterizing the longitudinal relaxation rate, T1-1, of tested samples. The detection coil, which was coupled to the SQUID through a flux transformer, was optimized by varying the copper wires' winding turns and diameters. When comparing the measured NMR signals, we found that the simulated NMR signal agrees with simulated signals. When discriminating liver tumors in rats, the averaged longitudinal relaxation rate was observed to be T1-1 = 3.3 s-1 for cancerous liver tissue and T1-1 = 6.6 s-1 for normal liver tissue. The results suggest that it can be used to successfully discriminate cancerous liver tissue from normal liver tissues in rats. The minimum amount of samples that can be detected is 0.2 g for liver tumor and 0.4 g for normal liver tissue in 100 μT fields. The specimen was not damaged; it can be used for other pathological analyses. The proposed method provides more possibilities for examining undersized specimens.

Original languageEnglish
Article number064701
JournalJournal of Applied Physics
Volume114
Issue number6
DOIs
Publication statusPublished - 2013 Aug 14

ASJC Scopus subject areas

  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Optimization of the detection coil of high-Tc superconducting quantum interference device-based nuclear magnetic resonance for discriminating a minimum amount of liver tumor of rats in microtesla fields'. Together they form a unique fingerprint.

Cite this