Investigation of a two-point maximum entropy regularization method for signal enhancement applied to magnetoencephalography data

Matthews, Quinn; Jirásek, A; Virji‐Babul, Naznin; Babul, Arif; Cheung, Teresa

doi:10.1016/j.bspc.2007.09.004

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Spectral smoothing was performed with an in-house version of the previously described two-point maximum entropy method, 29,30 which has been particularly successful when applied to Raman spectra. 31,32 In this work, a very modest amount of smoothing was applied in order to maintain fidelity of the sharpest Raman peaks in the spectra.…”

Section: Methodsmentioning

confidence: 99%

Variability in Raman Spectra of Single Human Tumor Cells Cultured in vitro: Correlation with Cell Cycle and Culture Confluency

et al. 2010

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In this work we investigate the capability of Raman microscopy (RM) to detect inherent sources of biochemically based spectral variability between single cells of a human tumor cell line (DU145) cultured in vitro. Principal component analysis (PCA) is used to identify differences in single-cell Raman spectra. These spectral differences correlate with (1) cell cycle progression and (2) changing confluency of a cell culture during the first 3 to 4 days after sub-culturing. Cell cycle regulatory drugs are used to synchronize the cell cycle progression of cell cultures, and flow cytometry is used to determine the cell cycle distribution of cell cultures at the time of Raman analysis. Spectral variability arising from cell cycle progression is (1) expressed as varying intensities of protein and nucleic acid features relative to lipid features, (2) well correlated with known biochemical changes in cells as they progress through the cell cycle, and (3) shown to be the most significant source of inherent spectral variability between cells. Furthermore, the specific biomolecules responsible for the observed spectral variability due to both cell cycle progression and changes in cell culture confluency can be identified in the first and second components of principal component analysis (PCA). Our characterization of the inherent sources of variability in Raman spectra of single human cells will be useful for understanding subtle spectral differences in RM studies of single cells.

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Section: Methodsmentioning

confidence: 99%

Variability in Raman Spectra of Single Human Tumor Cells Cultured in vitro: Correlation with Cell Cycle and Culture Confluency

et al. 2010

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“…The methods and justification for the implementation of each processing step have been described previously (Matthews et al 2010). Briefly, a very conservative amount of spectral smoothing was applied with an in-house version of the two-point maximum entropy method (Greek et al 1995, Schulze et al 2007, Jirasek et al 2006, Matthews et al 2008. Baseline estimation was performed with a modified version of the signal removal method (Schulze et al 2005) for the LWN window and with three-point linear interpolation for the HWN window.…”

Section: Spectral Processing and Data Analysismentioning

confidence: 99%

Raman spectroscopy of single human tumour cells exposed to ionizing radiationin vitro

et al. 2010

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This work investigates the capability of Raman spectroscopy (RS) to study the effects of ionizing radiation on single human tumour cells. Prostate tumour cells (cell line DU145) are cultured in vitro and irradiated to doses between 15 and 50 Gy with single fractions of 6 MV photons. Single-cell Raman spectra are acquired from irradiated and unirradiated cultures up to 5 days post-irradiation. Principal component analysis is used to distinguish the uniquely radiation-induced spectral changes from inherent sources of spectral variability arising from cell cycle differences and other known factors. We observe uniquely radiation-induced spectral changes which are correlated with both the irradiated dose and the incubation time post-irradiation. The spectral changes induced by radiation arise from biochemical differences in lipids, nucleic acids, amino acids and conformational protein structures between irradiated and unirradiated cells. To our knowledge, this study is the first use of RS to observe radiation-induced biochemical differences in single cells, and is the first use of vibrational spectroscopy to observe uniquely radiation-induced biochemical differences in single cells independent of concurrent cell-cycle- or cell-death-related processes.

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“…It was originally developed for SNR enhancement and deconvolution of Raman spectroscopy data but has recently been applied successfully in other fields, such as signal enhancement of magnetoencephalography data. 9,10 A 2D form of TPMEM was recently developed by Jirasek et al and was demonstrated to perform extremely well when applied to image smoothing of X-ray computed tomography (CT) data of radiosensitive polymer materials. 11 Although the overall algorithm processes a true 2D image, it does so by smoothing individual vectors.…”

Section: Introductionmentioning

confidence: 99%

A Matrix-Based Two-Dimensional Regularization Algorithm for Signal-to-Noise Ratio Enhancement of Multidimensional Spectral Data

Foist

Schulze²,

Jirásek³

et al. 2010

Appl Spectrosc

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We present a new spectral image processing algorithm, the "matrix maximum entropy method" (MxMEM), which offers efficient signal-to-noise ratio (SNR) enhancement of multidimensional spectral data. MxMEM is based upon two previous regularization methods that employ the maximum entropy concept. The first is a one-dimensional (1D) algorithm, which smoothes individual vectors, called the two-point maximum entropy method (TPMEM). The second is a two-dimensional (2D) form called 2D TPMEM, that smoothes images but processes them one vector at a time. MxMEM is a truly two dimensional image processing algorithm in that its "smoothing engine" performs two-dimensional processing in every iteration. We demonstrate that this matrix-based construction makes more effective use of two-dimensionally embedded information and thus confers significant advantages over other regularization approaches. In addition, we utilize the concept that individual related Raman spectra can be combined in a matrix to form an artificial Raman "image". We show that, when processed as an image, superior SNR enhancement is achieved compared to processing the same data by TPMEM one spectrum at a time.

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Investigation of a two-point maximum entropy regularization method for signal enhancement applied to magnetoencephalography data

Cited by 3 publications

References 9 publications

Variability in Raman Spectra of Single Human Tumor Cells Cultured in vitro: Correlation with Cell Cycle and Culture Confluency

Variability in Raman Spectra of Single Human Tumor Cells Cultured in vitro: Correlation with Cell Cycle and Culture Confluency

Raman spectroscopy of single human tumour cells exposed to ionizing radiationin vitro

A Matrix-Based Two-Dimensional Regularization Algorithm for Signal-to-Noise Ratio Enhancement of Multidimensional Spectral Data

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