ArticleNMR in biomedicine2020
Optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition.
Article in NMR in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Adaptively Optimized Combination of Phased-Array MR Spectroscopy Data Without Noise Correlation Estimation: A Joint Diagonalization Method.NMR in biomedicine · 2026Article
- Coil Combination Using OpTIMUS Results in Improved Signal-to-Noise Ratios of In Vivo MR Spectra Acquired at 7 T.NMR in biomedicine · 2025Article
- An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis.Radiology. Imaging cancer · 2024Review
- Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging.Chemical & biomedical imaging · 2023Review
- Resting-State Brain Temperature: Dynamic Fluctuations in Brain Temperature and the Brain-Body Temperature Gradient.Journal of magnetic resonance imaging : JMRI · 2023Article
- Glutamine Imaging: A New Avenue for Glioma Management.AJNR. American journal of neuroradiology · 2022Review
- Optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition.NMR in biomedicine · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Multi-channel phased receive arrays have been widely adopted for magnetic resonance imaging (MRI) and spectroscopy (MRS). An important step in the use of receive arrays for MRS is the combination of spectra collected from individual coil channels. The goal of this work was to implement an improved strategy termed OpTIMUS (i.e., optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition) for combining data from individual channels. OpTIMUS relies on spectral windowing coupled with a rank-R decomposition to calculate the optimal coil channel weights. MRS data acquired from a brain spectroscopy phantom and 11 healthy volunteers were first processed using a whitening transformation to remove correlated noise. Whitened spectra were then iteratively windowed or truncated, followed by a rank-R singular value decomposition (SVD) to empirically determine the coil channel weights. Spectra combined using the vendor-supplied method, signal/noise
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