A Method to Create a Universal Calibration Dataset for ... - IEEE Xplore

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, VOL. 22, NO. 3, MAY/JUNE 2016

A Method to Create a Universal Calibration Dataset for Raman Reconstruction Based on Wiener Estimation Shuo Chen, Yi Hong Ong, and Quan Liu

Abstract—Raman spectroscopy has been intensively explored in biomedical applications. However, Raman data acquisition is generally slow due to inherently weak Raman signals. Narrow-band Raman measurements can compensate for the weak Raman signal by performing integration in the wavenumber dimension, in which the Raman spectrum with high spectral resolution needs to be reconstructed from the narrow-band measurements. However, this method is limited in the requirement of a calibration dataset, in which the calibration samples have to be similar to test samples in Raman features. Therefore, a new calibration dataset is often needed for every type of samples. We propose a method to create a universal calibration dataset for Raman reconstruction to overcome this limitation. In our method, the Raman spectra measured from each basic biochemical component in samples instead of from actual samples are used in the calibration dataset. In this study, the proposed method was tested on 27 liquid phantoms and 56 cell measurements. The results demonstrated the excellent performance of Raman reconstruction using the universal calibration dataset compared to that using the traditional calibration dataset and the measured Raman spectra. Index Terms—Narrow-band measurements, universal calibration data, raman imaging, spectral reconstruction, Wiener estimation.

I. INTRODUCTION AMAN spectroscopy has been intensively explored in biomedical applications because of rich biochemical information that could be extracted from Raman spectra [1], [2]. However, Raman data acquisition is generally slow due to inherently weak Raman signals [3], which prohibits Raman spectroscopic imaging from being used to investigate fast changing phenomena especially in biological samples. Although methods such as surface enhanced Raman spectroscopy (SERS) can boost up the Raman signal significantly [4], [5], it requires more

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Manuscript received June 18, 2015; revised July 31, 2015; accepted September 5, 2015. Date of publication September 9, 2015; date of current version October 19, 2015. This work was supported by Tier 1 under Grant MOE RG 38/14 funded by the Ministry of Education and the public sector funding under Grant 122-PSF-0012 funded by the Agency for Science Technology and Research, Science and Engineering Research Council in Singapore. (Corresponding author: Quan Liu.) S. Chen is with the School of Chemical and Biomedical Engineering, Nanyang Technological University, 637457 Singapore, and also with the SinoDutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China (e-mail: [email protected]). Y. H. Ong and Q. Liu are with the School of Chemical and Biomedical Engineering, Nanyang Technological University, 637457 Singapore (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTQE.2015.2477463

elaborate sample preparation and spectral interpretation is often hampered because SERS spectra can be different in spectral features from the corresponding spontaneous Raman spectra [6]. An alternative way is to reconstruct the Raman spectrum with high spectral resolution from narrow-band measurements [7]. Narrow-band Raman measurements can compensate for the weak Raman signal at each wavenumber by performing integration in the wavenumber dimension. In addition, the approach of narrow-band Raman measurements at each pixel followed by Raman spectral reconstruction can facilitate the realization of fast Raman imaging, in which it requires only selected narrowband filters to get a few Raman images and the full Raman spectrum at each pixel can be reconstructed. Our previous studies have shown the success on the reconstruction of Raman spectra with [8] and without [9] fluorescence background from narrow-band measurements based on Wiener estimation. The potential improvement in speed by using this technique is dramatic compared with various other traditional Raman imaging techniques, such as point scanning and line scanning, as discussed in our previous study [8]. However, this method is limited in its requirement of a calibration data set in Wiener estimation. Moreover, calibration samples need to be similar to test samples in Raman features, for which the calibration data set is regarded as the traditional calibration data set as opposed to the universal calibration data set proposed in this paper. Therefore, a new calibration data set is often needed for every type of samples, which implies a huge burden and may prevent this Raman imaging approach from being widely adopted. In this study, we proposed a method to create a universal calibration data set for Raman reconstruction to overcome this limitation. In our method, Raman spectra measured from basic biochemical components of samples instead of similar samples will be used as the calibration data set. Because common biological samples, such as human cells, share the same set of basic biochemical components [10], the calibration data set based on these biochemical components is applicable to all and only a handful number of Raman measurements are needed to create such a universal calibration data set. In addition, the calibration data set created from basic biochemical components can be adapted to samples with new Raman features by adding or removing one or more basic biochemical components, in which the repeated measurements of all basic biochemical components is unnecessary. In this study, this approach was validated on two different types of samples, i.e., liquid phantoms with three basic biochemical components and live cells. A universal calibration data set acquired from three biochemical components was tested

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CHEN et al.: METHOD TO CREATE A UNIVERSAL CALIBRATION DATASET FOR RAMAN RECONSTRUCTION

on 27 liquid phantoms in which three basic biochemical components were mixed, while another universal calibration data set of 12 basic components were tested on 56 cell measurements (28 measurements from K562 leukemia cells and 28 measurements from Caki-2 kidney cells). The Raman spectra reconstructed from synthesized narrow-band measurements of phantoms or cell samples were compared to those directly measured from the same phantoms or cell samples. In addition, the results of reconstruction using the proposed universal calibration data set were compared with those using the traditional calibration data set, in which the traditional calibration data set were acquired from samples similar to those in the test data set. The results demonstrated the excellent performance of Raman reconstruction using the universal calibration data set compared to the measured Raman spectra and those reconstructed using the traditional calibration data set. II. MATERIALS AND METHODS In this study, two sets of Raman measurements from different samples, i.e., phantoms and cells, were performed to validate the feasibility of Raman reconstruction using the universal calibration data sets. The spectral reconstruction method was based on Wiener estimation [11]. The results of the proposed universal calibration data set were compared with the traditional calibration data set, in which the traditional calibration data set consists of the samples similar to those in the test data set. A. Sample Preparation The phantoms were made by dissolving urea (V3171, Promega corporation, USA), potassium formate (294454-500 G, Sigma-Aldrich, USA) and monosodium phosphate (20233-1 KG, Affymetrix, USA) in distilled water and mixing them. A total of 27 test phantoms were created by mixing urea (0.5, 1 and 1.5 M), potassium formate (0.5, 1 and 1.5 M) and monosodium phosphate (0.75, 1.5 and 2.25 M) in all possible combinations. A total of nine calibration phantoms included urea solution (0.5, 1 and 1.5 M), potassium formate solution (0.5, 1 and 1.5 M) and monosodium phosphate solution (0.75, 1.5 and 2.25 M). The nine calibration phantoms were used to acquire the calibration data set and the 27 test phantoms were used to acquire the test data set in the spectral reconstruction procedure based on Wiener estimation. Raman spectra of phantoms were measured over a range from 600 to 1800 cm−1 , using a micro-Raman system (innoRam-785S, B&W TEK, USA) coupled to a video microscope sampling system (BAC151A, B&W TEK, USA). The excitation wavelength was 785 nm and the spectral resolution was 4 cm−1 . The exposure time for both calibration data set and test data set was 10 s and each spectrum was accumulated for 30 times. For cell samples, K562 cells (CCL-243, American Type Culture Collection, VA, USA) were cultured in Iscove’s Modified Dulbecco’s Medium (IMDM) supplemented with 10% fetal bovine serum. Caki-2 cells (HTB-47, American Type Culture Collection, VA, USA) were cultured in Roswell Park Memorial Institute (RPMI) medium with 10% fetal bovine serum. Both two types of cells were incubated at 37 °C with 5% CO2 . Cul-

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tures were maintained by the addition or replacement of fresh medium every 2–3 days to maintain the cell density between 105 and 106 cells per milliliter. Before taking Raman measurements, cells were washed twice, rinsed and immersed in phosphate-buffered saline (PBS) to reduce fluorescence background from the culture media. Twenty-eight Raman measurements were taken from K562 cells and Caki-2 cells, respectively. The universal calibration data set were measured from the basic biochemical components (actin, albumin, DNA, RNA, glycogen, triolein, phosphatidylcholine), culture media (IMDM and RPMI) and other factors (PBS, quartz coverslip and aluminum foil). Raman spectra were measured over a range from 600 to 1800 cm−1 for both cells and basic biochemical components/media/other factors, using a micro-Raman system (inVia, Renishaw, U.K.) coupled to a microscope (Alpha 300, WITec, Germany). The excitation wavelength was 785 nm and the spectral resolution was 2 cm−1 . The exposure time in Raman measurements of cells was 10 s and each spectrum was accumulated for six times. The exposure time in Raman measurements of all basic biochemical components/media/other factors was 10 s and each spectrum was accumulated only once. B. Data Analysis In this study, a narrow-band measurement refers to the reading obtained after a Raman signal passes through a filter. Since a filter’s property is fully characterized by its transmission spectrum, the narrow-band measurement C can be modeled as the inner product of a Raman spectrum, S, and the transmittance spectrum of the filter, F, according to (1), in which the two spectra, S and F, are treated as two vectors. Narrow-band measurements are related to the fact that Raman spectra commonly acquired from a range of wavenumbers 400–2000 cm−1 , which corresponds to a wavelength range of approximate 120 nm for excitation wavelength of 785 nm. The narrow-band filters usually cover a portion of this range, which is not wide enough to be called “wide band” C = FS.

(1)

The non-negative PCs based filters were obtained from the published method [12]. Those non-negative PCs based filters can be created using dynamic optics [13]–[15] such as digital micromirror device or spatial light modulator. The commercial filters that are partially or totally overlapped with the Raman spectra were chosen from five manufacturers as shown in Table I. Wiener estimation was used for spectral reconstruction, in which the reconstructed Raman spectrum of the test data set can be reconstructed from narrow-band measurements using the Wiener matrix derived from the calibration data set. In Wiener estimation, a Wiener matrix W is calculated from the calibration data set according to (2) when ignoring the noise term [11] T −1 W = E[Sc CT c ]{E[Cc Cc ]}

(2)

where Sc is the measured Raman spectra in the calibration data set and Cc is the narrow-band measurements synthesized from the calibration data set based on (1). E[] represents the ensemble average, the superscript “−1” represents matrix inverse and

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, VOL. 22, NO. 3, MAY/JUNE 2016

Fig. 1. (a) Best case, (b) typical case, (c) worst case of the reconstructed Raman spectra when using six non-negative PCs based filters, and (d) shows the transmittance spectra of six non-negative PCs based filters after normalization.

TABLE I COMMERCIAL FILTERS USED IN THE SYNTHESIS OF NARROW-BAND MEASUREMENTS Manufacturer

Product numbers of commercial filters

Chroma Technique (Bellows Falls, VT, USA) Edmund Optics (Barrington, NJ, USA) Omega Filters (Brattleboro, VT, USA)

Semrock (Rochester, NY, USA)

Thorlabs (Newton, NJ, USA)

D850/20m, D850/40m NT 84-790, NT 84-791 3RD850LP, 3RD900LP, XB 142, XB 143, XB 146, XB 149, XF 3308, XL 19, XL 40, XLK 18, XLK 20 FF 01-830/2-25, FF 01-832/37-25, FF 01-835/70-25, FF 01-840/12-25, FF 01-857/30-25, FF 01-910/5-25 FB 830-10, FB 840-10, FB 850-10, FB 850-40, FB 860-10, FB 870-10, FB 880-10, FB 880-40, FB 890-10, FB 900-40, FB 910-10, FL 830-10, FL 850-10, FL 880-10, FL 905-10, FL 905-25

the superscript “T” represents matrix transpose. Then the reconstructed Raman spectrum of the test calibration data set sˇ can be reconstructed according to sˇ = WCt

(3)

where Ct is the narrow-band measurements synthesized from the test data set according to (1). To quantify the reconstruction accuracy of the proposed method, the relative RMSE of the reconstructed Raman spectrum after the removal of fluorescence background, relative to the measured Raman spectrum in which fluorescence background was also removed in the same manner, was computed according to 1/2  N 2 i=1 [Rr (λi ) − Rm (λi )] (4) Relative RMSE = N × max[Rm (λi )]2 where Rr and Rm are the reconstructed Raman spectrum and the measured Raman spectrum (both after fluorescence background removal by the fifth-order polynomial fitting [16]), respectively, λi is the ith wavenumber (i is varied from 1 to N) and the function, max[], returns the maximum intensity of the input spectrum. III. RESULTS AND DISCUSSIONS Table II shows the mean relative RMSE for Raman spectral reconstruction of liquid phantoms from narrow-band

CHEN et al.: METHOD TO CREATE A UNIVERSAL CALIBRATION DATASET FOR RAMAN RECONSTRUCTION

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Fig. 2. (a) Best case, (b) typical case, (c) worst case of the reconstructed Raman spectra when using ten non-negative PCs based filters, and (d) shows the transmittance spectra of ten non-negative PCs based filters after normalization.

TABLE II MEAN RELATIVE RMSE OF RAMAN SPECTRA FOR LIQUID PHANTOMS RECONSTRUCTED FROM NARROW-BAND MEASUREMENTS SYNTHESIZED WITH DIFFERENT NUMBERS OF NON-NEGATIVE PCS BASED FILTERS USING THE UNIVERSAL CALIBRATION DATASET AND TRADITIONAL CALIBRATION DATASET Number of non-negative PCs based filters 2 3 4 5 6 7

Mean relative RMSE using the universal calibration dataset

Mean relative RMSE using the traditional calibration dataset

0.0920 0.0851 0.0256 0.0127 0.0116 0.0119

0.0449 0.0259 0.0063 0.0060 0.0035 0.0030

measurements synthesized with different numbers of nonnegative PCs based filters using the universal calibration data set and traditional calibration data set. The number of the non-negative PCs based filters was varied from 2 to 7, in which the best reconstruction accuracy was achieved using six non-negative PCs based filters. All the results shown here were

obtained after removing fluorescence background by fitting the fifth-order polynomial to the spectrum and subtracting the polynomial from it. Compared with the traditional calibration data set, the mean relative RMSE using the universal calibration data set is higher by 0.007 to 0.047. Fig. 1 shows the best, typical and worst cases for Raman spectra of liquid phantoms reconstructed from narrow-band measurements synthesized with six non-negative PCs based filters and the transmittance spectra of the non-negative PCs based filters. Note that the transmittance spectra of the non-negative PCs based filters have been normalized by dividing every spectral value by the maximum transmittance of each filter. The relative RMSEs in the best, typical and worst cases are 0.0061, 0.0109 and 0.0174, respectively. Table III shows the mean relative RMSE of Raman spectra for cells reconstructed from narrow-band measurements synthesized with different number of non-negative PCs based filters using the universal calibration data set and traditional calibration data set. The number of the non-negative PCs based filters was varied from 2 to 11. It can be seen that the best reconstruction accuracy was achieved when using ten non-negative

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, VOL. 22, NO. 3, MAY/JUNE 2016

Fig. 3. (a) Best case, (b) typical case, (c) worst case of the reconstructed Raman spectra when using nine commercial filters, and (d) shows the transmittance spectra of nine commercial filters.

TABLE III MEAN RELATIVE RMSE OF RAMAN SPECTRA FOR CELLS RECONSTRUCTED FROM NARROW-BAND MEASUREMENTS SYNTHESIZED WITH DIFFERENT NUMBERS OF NON-NEGATIVE PCS BASED FILTERS USING THE UNIVERSAL CALIBRATION DATASET AND TRADITIONAL CALIBRATION DATASET Number of non-negative PCs based filters 2 3 4 5 6 7 8 9 10 11

Mean relative RMSE using the universal calibration dataset

Mean relative RMSE using the traditional calibration dataset

0.1934 0.1653 0.1449 0.1283 0.1317 0.1127 0.0577 0.0476 0.0440 0.0442

0.0601 0.0477 0.0387 0.0336 0.0315 0.0223 0.0186 0.0150 0.0150 0.0145

PCs based filters. Compared with the traditional calibration data set, the reconstruction accuracy using the universal calibration data set is lower by 0.029 to 0.133. Fig. 2 shows the best, typical, worst cases for Raman spectra of cells re-

constructed from narrow-band measurements synthesized with ten non-negative PCs based filters and the transmittance spectra of the non-negative PCs based filters. Note that the transmittance spectra of the non-negative PCs based filters have been normalized by dividing every spectral value by the maximum transmittance of each filter. The relative RMSEs in the best, typical and worst cases are 0.0245, 0.0440 and 0.0623, respectively. Table IV shows the mean relative RMSE of Raman spectra for cells reconstructed from narrow-band measurements synthesized with different numbers of commercial filters using the universal calibration data set and traditional calibration data set. The number of the commercial filters was varied from 2 to 10. It can be seen that the best reconstruction accuracy was achieved when using nine commercial filters. Each set of optimal commercial filters were selected using the genetic algorithm [17]. Compared with the traditional calibration data set, the reconstruction accuracy using the universal calibration data set is lower by 0.031 to 0.083. Fig. 3 shows the best, typical, worst cases for Raman spectra of cells reconstructed from narrowband measurements synthesized with nine commercial filters and the transmittance spectra of these commercial filters. The

CHEN et al.: METHOD TO CREATE A UNIVERSAL CALIBRATION DATASET FOR RAMAN RECONSTRUCTION

TABLE IV MEAN RELATIVE RMSE OF RAMAN SPECTRA FOR CELLS RECONSTRUCTED FROM NARROW-BAND MEASUREMENTS SYNTHESIZED WITH DIFFERENT NUMBERS OF COMMERCIAL FILTERS USING THE UNIVERSAL CALIBRATION DATASET AND TRADITIONAL CALIBRATION DATASET

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compared to that using the traditional calibration data set and the measured Raman spectra.

IV. CONCLUSION Number of commercial filters 2 3 4 5 6 7 8 9 10

Mean relative RMSE using the universal calibration dataset

Mean relative RMSE using the traditional calibration dataset

0.1348 0.1200 0.0958 0.0817 0.0628 0.0589 0.0555 0.0548 0.0561

0.0553 0.0372 0.0352 0.0281 0.0272 0.0276 0.0202 0.0199 0.0186

relative RMSEs in the best, typical and worst case are 0.0309, 0.0545 and 0.0881, respectively. From Tables II and III, the optimal reconstruction accuracy for cells is lower than that for phantoms. This difference could be attributed to complex spectral features of cells and potentially missing basic biochemical components of cells. Although the major biochemical components of cells have been included, there are still other components, e.g. metabolites and inorganic ions [18], which may affect the reconstruction accuracy. Besides that, only representative proteins, lipids and polysaccharides were included instead of all types of proteins, lipids and polysaccharides in cells. Comparing the results in Tables III and IV, it can be found that the reconstruction accuracy using commercial filters is better than that using the same number of non-negative PCs based filter when using fewer than four filters. In contrast, the reconstruction accuracy using commercial filters is always worse than that using non-negative PCs based filters when using more than or equal to four filters. This could be attributed to the fact that the variance of fluorescence background in the cells’ Raman spectra was considerably larger than that of the Raman signal in magnitude. Based on the characteristics of PCA, the first three or four PCs, from which the transmittance spectra of these PCs based filters were derived, capture most information from smooth fluorescence background and little information from the Raman signal on top of the fluorescence background, as shown by the relatively flat shape of the first a few PCs in Fig. 2(d). As the number of the PCs based filters increases, more information about the Raman signal can be captured due to the sharper peaks shown up in the subsequent PCs. Therefore the PCs based filters shows a greater advantage in capturing Raman features only when the number of PCs is larger. According to Figs. 1 to 3, for both phantoms and cells, both the peak locations and peak intensities match very well between the reconstructed Raman spectra and measured Raman spectra. Most importantly, according to Tables II through IV, the proposed universal calibration data set shows only slightly degradation in performance compared with the traditional calibration data set. Therefore, it can be concluded that the results shows excellent performance in Raman reconstruction using the universal calibration data set

In this study, we proposed a method of creating a universal calibration data set by measuring basic biochemical components and tested the method on 27 liquid phantoms with three basic biochemical components and 56 Raman measurements from cells. The proposed universal calibration data set is much easier to obtain than the traditional calibration data set because only a handful number of Raman measurements are needed to create such a universal calibration data set. The results demonstrated the excellent performance of Raman reconstruction using the universal calibration data set compared to that using the traditional calibration data set and the measured Raman spectra.

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Shuo Chen received the Bachelor’s degree from Shanghai Jiaotong University, Shanghai, China, and the Master’s degree from Heidelberg University, Heidelberg, Germany, and the Ph.D. degree from the School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore. He is currently an Associate Professor at the Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China. His research interest includes fast spectroscopic imaging in biomedical applications.

Quan Liu received the Bachelor’s degree in electrical engineering from Xidian University, Xi’an, China, the Master’s degree in electrical engineering from the Graduate School of University of Science and Technology of China, Beijing, China, and the Ph.D. degree in biomedical engineering from the University of Wisconsin, Madison, WI, USA. He is currently an Assistant Professor at the School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore. His research interests include optical imaging and spectroscopy for medical diagnostics.

Yi Hong Ong received the B.Eng. degree in biomedical engineering from the University of Malaya, Kuala Lumpur, Malaysia, and the Ph.D. degree from the School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore. His current research interests include the development of depth-sensitive optical measurement methods for medical diagnosis and also in optical instrument development for medical imaging.