Spectral Wavelet‐feature Analysis and Classification Assisted Denoising for enhancing magnetic resonance spectroscopy
Autor: | Xinhua Tu, Hui Mao, Liya Wang, Lei Zhou, Zahra Hosseini, Bing Ji |
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Rok vydání: | 2021 |
Předmět: |
Adult
Male Magnetic Resonance Spectroscopy Computer science Noise reduction Feature extraction Wavelet Analysis Signal-To-Noise Ratio Signal Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Wavelet Signal-to-noise ratio Sampling (signal processing) Humans Radiology Nuclear Medicine and imaging Spectroscopy Noise (signal processing) business.industry Brain Signal Processing Computer-Assisted Pattern recognition Databases as Topic Metabolome Molecular Medicine Female Signal averaging Artificial intelligence business Algorithms 030217 neurology & neurosurgery |
Zdroj: | NMR Biomed |
ISSN: | 1099-1492 0952-3480 |
Popis: | Magnetic resonance spectroscopy (MRS) is capable of revealing important biochemical and metabolic information of tissues non-invasively. However, the low concentrations of metabolites often lead to poor signal-to-noise ratio (SNR) and long acquisition time. Therefore, applications of MRS in detection and quantitative measurements of metabolites in vivo remain to be limited. Reducing or even eliminating noise can improve SNR sufficiently to obtain high quality spectra in addition to increasing the number of signal averaging (NSA) or the field strength, both of which are limited in clinical applications. We present a Spectral Wavelet-feature Analysis and Classification Assisted Denoising (SWANCAD) approach to differentiate signal and noise peaks in magnetic resonance spectra based on their respective wavelet features, followed by removing the identified noise components to improve SNR. The performance of this new denosing approach was evaluated by measuring and comparing SNRs and quantified metabolite levels of the low NSA spectra (e.g., NSA = 8) before and after denoising using the SWANCAD approach or conventional spectral fitting and denoising methods, such as LCModel and wavelet threshold methods as well as the high NSA spectra (e.g., NSA = 192) recorded in the same sampling volumes. The results demonstrated that SWANCAD offers a more effective way to detect the signals and improve SNR by removing noise from the noisy spectra collected with low NSA or in the sub-minute scan time (e.g., NSA = 8 or 16 seconds). The potential applications of SWANCAD include using low NSA to accelerate MRS acquisition while maintaining adequate spectroscopic information for detection and quantification of the metabolites of interest when a limited time is available for an MRS exam in the clinical setting. |
Databáze: | OpenAIRE |
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