Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is a low-energy technique which suffers from poor inherent signal-to-noise ratio (SNR). In a clinical setting, it is often desirable to study small regions of tissue in patients to aid in the detection and diagnosis of disease states. Analysis of the smaller regions, however, degrades the SNR further and renders conventional spectral estimation techniques such as the discrete Fourier transform useless. We demonstrate the utility of two complex eigenvector-based algorithms, Multiple Signal Classification (MUSIC) and Minimum Norm, in the detection of resonances within small sample volumes. The results indicate that these methods are clearly superior to Fourier transform-based techniques currently available on clinical NMR scanners.

Nuclear magnetic resonance imaging

William P. Rothwell
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Electro-mechano-optical detection of nuclear magnetic resonance

Kazuyuki Takeda, Kentaro Nagasaka, Atsushi Noguchi, Rekishu Yamazaki, Yasunobu Nakamura, Eiji Iwase, Jacob M. Taylor, and Koji Usami
Optica 5(2) 152-158 (2018)

Methods and algorithms for Fourier-transform nuclear magnetic resonance tomography

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Coregistered transcranial optoacoustic and magnetic resonance angiography of the human brain

Ruiqing Ni, Xosé Luís Deán-Ben, Valerie Treyer, Anton Gietl, Christoph Hock, Jan Klohs, Roger M. Nitsch, and Daniel Razansky
Opt. Lett. 48(3) 648-651 (2023)

Capability of physically reasonable OCT-based differentiation between intact brain tissues, human brain gliomas of different WHO grades, and glioma model 101.8 from rats

I. N. Dolganova, P. V. Aleksandrova, P. V. Nikitin, A. I. Alekseeva, N. V. Chernomyrdin, G. R. Musina, S. T. Beshplav, I. V. Reshetov, A. A. Potapov, V. N. Kurlov, V. V. Tuchin, and K. I. Zaytsev
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