1.7-μm spectroscopic spectral-domain optical coherence tomography for imaging lipid distribution within blood vessel Masato Tanaka,* Mitsuharu Hirano, Kiyotaka Murashima, Hiroshi Obi, Ryo Yamaguchi, and Takemi Hasegawa Life Science R&D Dept., New Business Frontier R&D Laboratories, Sumitomo Electric Industries, Ltd., 1 Taya-cho Sakae-ku, Yokohama, 244-8588, Japan * [email protected]
Abstract: We have developed a spectroscopic optical coherence tomography (OCT) for imaging lipid distribution within blood vessel in order to detect coronary artery plaque. A 1.7-μm spectral-domain OCT with A-scan rate of 47 kHz is fabricated using a broadband light source based on super-luminescent diodes and spectrometers based on extended InGaAs line sensors. We demonstrate imaging of lipid distribution in an in vitro artery model with lipid. The sensitivity and specificity in the differentiation between artery and lipid are 87% and 90% in the training, respectively. The validation test also shows detection of lipid with an accuracy over 90%. ©2015 Optical Society of America OCIS codes: (110.4500) Optical coherence tomography; (300.6340) Spectroscopy, infrared.
References and links 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
R. Virmani, F. D. Kolodgie, A. P. Burke, A. Farb, and S. M. Schwartz, “Lessons from sudden coronary death: A comprehensive morphological classification scheme for atherosclerotic lesions,” Arterioscler. Thromb. Vasc. Biol. 20(5), 1262–1275 (2000). T. Kume, T. Akasaka, T. Kawamoto, H. Okura, N. Watanabe, E. Toyota, Y. Neishi, R. Sukmawan, Y. Sadahira, and K. Yoshida, “Measurement of the thickness of the fibrous cap by optical coherence tomography,” Am. Heart J. 152(4), 755e1–755e4 (2006). G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. Express 4(7), 1014–1030 (2013). E. Regar, M. Gnanadesigan, A. F. Van der Steen, and G. van Soest, “Quantitative optical coherence tomography tissue-type imaging for lipid-core plaque detection,” JACC Cardiovasc. Interv. 6(8), 891–892 (2013). C. P. Fleming, J. Eckert, E. F. Halpern, J. A. Gardecki, and G. J. Tearney, “Depth resolved detection of lipid using spectroscopic optical coherence tomography,” Biomed. Opt. Express 4(8), 1269–1284 (2013). K. Jansen, M. Wu, A. F. W. van der Steen, and G. van Soest, “Photoacoustic imaging of human coronary atherosclerosis in two spectral bands,” Photoacoustics 2(1), 12–20 (2014). U. Sharma, E. W. Chang, and S. H. Yun, “Long-wavelength optical coherence tomography at 1.7 µm for enhanced imaging depth,” Opt. Express 16(24), 19712–19723 (2008). H. Kawagoe, S. Ishida, M. Aramaki, Y. Sakakibara, E. Omoda, H. Kataura, and N. Nishizawa, “Longwavelength optical coherence tomography at 1.7 μm for enhanced imaging depth,” Biomed. Opt. Express 16(24), 932–943 (2014). S. Ishida and N. Nishizawa, “Quantitative comparison of contrast and imaging depth of ultrahigh-resolution optical coherence tomography images in 800-1700 nm wavelength region,” Biomed. Opt. Express 3(2), 282–294 (2012). E. J. Jung, J. H. Lee, B. S. Rho, M. J. Kim, S. H. Hwang, W. J. Lee, J. J. Song, M. Y. Jeong, and C. S. Kim, “Spectrally Sampled OCT Imaging Based on 1.7-μm Continuous-Wave Supercontinuum Source,” IEEE J. Sel. Top. Quantum Electron. 18(3), 282–294 (2012). C. Xu, D. L. Marks, M. N. Do, and S. A. Boppart, “Separation of absorption and scattering profiles in spectroscopic optical coherence tomography using a least-squares algorithm,” Opt. Express 12(20), 4790–4803 (2004). M. Hirano, S. Tonosaki, T. Ueno, M. Tanaka, and T. Hasegawa, “Improved method to visualize lipid distribution within arterial vessel walls by 1.7 μm spectroscopic spectral-domain optical coherence tomography,” Proc. SPIE 8935, 893517 (2014). G. J. Tearney, S. A. Boppart, B. E. Bouma, M. E. Brezinski, N. J. Weissman, J. F. Southern, and J. G. Fujimoto, “Scanning single-mode fiber optic catheter-endoscope for optical coherence tomography,” Opt. Lett. 21(7), 543– 545 (1996).
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6645
14. R. Leitgeb, C. K. Hitzenberger, and A. F. Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography,” Opt. Express 11(8), 889–894 (2003). 15. M. Tanaka, M. Hirano, T. Hasegawa, and I. Sogawa, “Lipid distribution imaging in in-vitro artery model by 1.7μm spectroscopic spectral-domain optical coherence tomography,” Proc. SPIE 8565, 85654F (2013). 16. T. Kume, H. Okura, R. Yamada, T. Kawamoto, N. Watanabe, Y. Neishi, Y. Sadahira, T. Akasaka, and K. Yoshida, “Frequency and Spatial Distribution of Thin-Cap Fibroatheroma Assessed by 3-Vessel Intravascular Ultrasound and Optical Coherence Tomography: An Ex Vivo Validation and an Initial In Vivo Feasibility Study,” Circ. J. 73(6), 1086–1091 (2009).
1. Introduction Ischemic heart disease due to coronary arteriosclerosis is a major cause of death in developed countries. Intravascular imaging modalities using imaging catheters are powerful tools for detailed diagnosis of plaque lesions in coronary arteries. Among them, intravascular ultrasound (IVUS) has been the major modality for more than 20 years. However, its axial resolution as low as 100 µm is not sufficient to image a thin-cap fibroatheroma (TCFA) with cap thickness less than 65 µm, which is considered as a vulnerable plaque because its rapture can lead to acute coronary events [1]. On the other hand, OCT is recently attracting much attention, because its axial resolution as high as 10-15 μm enables accurate detection of the fibrous caps [2]. Vascular plaques are classified by its material composition into several types such as lipidic, calcified and fibrous. It is desirable to choose the optimum treatment for each plaque type. However, the conventional 1.3-µm OCTs take images of plaques based on scattering and attenuation within the tissue, so that material composition can be guessed from the OCT image only indirectly. As a result, it is sometimes difficult for physicians to judge the plaque type especially when the physician is not much experienced in reading OCT images. Several methods to classify plaque types automatically based on analysis of OCT image [3] and signal attenuation [4] have been reported. However, since 1.3-µm OCT signal reflects material composition only indirectly, accuracy of classification of plaque types could be limited. Spectroscopy is an effective method to characterize material composition, and there have been several spectroscopic methods proposed for imaging of vascular plaques. A spectroscopic OCT using conventional 1.3-μm wavelength [5] is proposed to image distribution of material composition. However, it needs many reference data and much training because there is relatively only small difference in absorption characteristics at 1.3 μm among compositions. Another attractive method is a photo-acoustic imaging using two wavelengths in 1.7-µm band [6]. However, since IVUS is necessary for detection of photoacoustic signal in addition to a laser delivery fiber, the catheter tends to be thick and expensive compared to the conventional IVUS and OCT. We propose a spectroscopic OCT using 1.7-μm band for imaging distribution of lipid which is an important constituent in vascular plaque. Since spectroscopic OCT is based on analysis of spectrum of interference light, it can extract information on distribution of composition in addition to conventional OCT image. Moreover, since spectral contrast of lipid is higher in 1.7-µm wavelength than in 1.3 µm, the 1.7-μm spectroscopic OCT would be more capable of detecting lipid than the conventional 1.3-µm one. In this paper, we report the results of fabrication of a spectroscopic SD-OCT system at 1.7-μm. The previous reports about the 1.7-μm OCT uses a swept light source [7] and super-continuum light sources [8– 10], whereas our system uses a SLD-based light source at 1.7-μm. We also demonstrate imaging of lipid distribution in an in vitro artery model using the 1.7-μm spectroscopic SDOCT system. 2. 1.7-μm spectroscopic OCT for lipid distribution imaging While conventional intravascular OCTs utilize light with a wavelength band at 1.3 μm which has low absorption by water, our spectroscopic OCT is based on a wavelength band of 1.7 μm. Figure 1 shows the attenuated reflection spectra over the wavelength range from 1.2 μm to 1.9 μm of purified lard (Megmilk snow brand, Tokyo, Japan), porcine carotid artery (Tokyo Shibaura Zoki, Tokyo, Japan), solid cholesterol made by melting and re-solidifying
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6646
cholesterol powder (Wako Pure Chemical Industries, Ltd., Osaka, Japan) and water, measured using NIR hyper spectral camera (Compovision, Sumitomo Electric Industries, Yokohama, Japan). The cholesterol and lard are chosen as representative material of lipid. While the spectra of lipids are not significantly different in spectral peaks from that of artery at 1.3 μm, they have significant spectral peaks at 1.7 μm due to C-H bonds. Thus lipid can be quantitatively detected by extracting the characteristic spectral attenuation in the 1.7µm OCT interference signal. Moreover, the lipid distribution can be visualized by analyzing dependence of the attenuation on depth using the spectroscopic OCT technique described below.
Fig. 1. Infrared spectra of lipids (lard and cholesterol), normal artery, and water.
The schematic diagram of 1.7-μm spectroscopic OCT is shown in Fig. 2. In the first step, 1.7-µm OCT interference spectrum is taken by the OCT hardware described later. In the second step, conventional OCT image representing distribution of scattering is constructed by Fourier transformation of the interference spectra, which is taken continuously while rotating the imaging core of the catheter. In the third step, in order to construct the image of lipid distribution, a set of sub-band tomographic signals as a function of depth and wavelength are calculated by dividing the interference spectra into several sub-band spectra and applying Fourier transformation to them. Then the axial distribution model of lipid content is fitted to the sub-band tomographic signals, based on the spectroscopic OCT method using least square analysis proposed by Xu et al. [11]. The axial distribution profiles of lipid are calculated while rotating the imaging core of the catheter, resulting in an image representing distribution of lipid. Post-processing methods such as spatial low-pass filtering are effective to suppress false positive detection around the borders of the vessel [12] and lipid distribution image is generated by distribution of lipid contents over a decision threshold after spatial filtering. 3. 1.7-μm SD-OCT system with intravascular imaging catheter Figure 3 shows an overview and the schematic diagram of the 1.7-μm SD-OCT system fabricated in order to demonstrate imaging of lipid distribution within blood vessel. The system contains, an optical unit composed of a light source, a fiber interferometer and spectrometers and a control system, and is integrated in a movable cabinet. The system also includes a motor drive unit for rotating and transferring light from/to the imaging core of the OCT catheter. The light source is custom-built by combining three SLDs emitting lights around 1.7 μm using couplers so as to output in a single-mode fiber. Figure 4(a) shows the output spectra of the light source of the respective SLDs and that of the combined output. The peak wavelengths of the three SLDs are 1653 nm, 1689 nm and 1740 nm, respectively. The total output power, the 3-dB bandwidth and the spectral flatness over spectrometer band are 19.6mW, 128 nm and 8 dB, respectively. As a reference, Fig. 4 also shows a typical absorption spectrum of lipid. The wavelength band covered by the light source has both of low and high absorption by lipid to cause large differences among the sub-band images.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6647
Fig. 2. The schematic diagram of intravascular image processing by 1.7-μm spectroscopic SDOCT. Interference spectra of 1.7-µm OCT is processed for constructing both conventional OCT image and lipid distribution image. The former is constructed by usual Fourier transform (FT), and the latter is constructed by sub-band Fourier-transform and calculation to extract lipid content.
Fig. 3. An overview (a) and the schematic diagram (b) of the 1.7-μm SD-OCT system. SLD: super-luminescent diode, CP: coupler, VOA: variable optical attenuator, CIR: circulator, MR: mirror on a moving stage, OFRJ: optical fiber rotary joint, PBS: polarization beam splitter, LS: line sensor, GT: grating, L: optical lens
In the following fiber interferometer, the broadband light from the light source is divided into a measurement arm and a reference one by a 90:10 fiber coupler (CP1). The light in the reference arm goes through a variable optical attenuator (VOA) for tuning input power to spectrometers and a dynamic polarization controller (PC) for controlling balance of input power between two spectrometers. The reference light makes a round trip between a fiber circulator (CIR1) and a mirror on a moving stage (MR) for tuning the optical length of the reference arm and couples into another fiber coupler (CP2) with coupling ratio of 20%. On the other hand, the light in the measurement arm goes through another circulator (CIR2), an optical fiber rotary joint (OFRJ) in the motor drive unit and the OCT catheter. The OCT catheter contains a rotatable imaging core with optical fiber and GRIN lens for side emitting on the tip enclosed inside a transparent outer sheath, similarly to the catheter proposed by
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6648
Tearney et al. [13]. The lens is designed to emit collimated beam with a beam divergence of 4.4 degree full width at 1.7 μm in order to re-couple the light scattered from the target to the fiber efficiently. The scattered light from target goes through OFRJ and CIR2 again and combines with the reference light at CP2 to make interference. Since the fiber interferometer consists of fiber optic products for telecommunications, not necessarily optimized for 1.7-µm band, the current system has a limitation that the transmission losses of the both arms increase at longer wavelength as shown in Fig. 4(b). The wavelength dependent loss over the spectrometer band is 4.7 dB and 4.0 dB in the reference and the sample arm, respectively. The interference light is divided into two orthogonal polarization axes and received by two spectrometers to obtain constant interference regardless of the polarization state of the measurement light. The spectrometer includes an extended InGaAs line sensor (LS) (Sensors Unlimited, USA) with 1024 pixels and the sensitivity up to 2.2 μm wavelength, a grating (GT) and a collimation and imaging lens (L1/L2). It is optimally designed to obtain a bandwidth needed for lipid distribution imaging while maintaining wavelength resolution. The maximum A-scan rate of the spectrometers is 47 kHz which is equal to the line rate of the InGaAs line sensors. Figure 4(c) shows the characteristics of wavelength and its resolution on two spectrometers. These are obtained by receiving a broadband light through an etalon filter and measuring the position and width of periodical beam spots projected in each spectrometer. In both of spectrometers, the wavelength range is 1608-1767 nm and the averaged wavelength resolution is 0.25 nm. Regarding the theoretical values estimated from these measured values, the image depth is 4.6 mm, the axial resolution is 18 μm and the 6-dB roll-off depth is 2.6 mm in air.
Fig. 4. (a) Output spectra of the 1.7-μm SLD-based light source. The three thin solid lines and the thick solid line designate the spectra of the three respective SLDs and the combined output. The driving currents of the SLDs are optimized to maximize the bandwidth. A broken line means the absorption spectrum of lipid as a reference. (b) Transmission losses of reference and measurement arms in the interferometer (except insertion loss of catheter). (c) Characteristics of center wavelength and wavelength resolution of two spectrometers (filled circles: #1 for P polarization, open circles: #2 for S polarization) as functions of pixel number of the line camera. The wavelength characteristics on two spectrometers are almost identical.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6649
4. Results and discussion 4.1 Performance of 1.7-μm SD-OCT system Firstly, we verify performance of the 1.7-μm SD-OCT system by a fiber partial reflector with a variable optical attenuator and the same optical length as the OCT catheter instead of the catheter. Figure 5(a) shows measured OCT A-scan profiles with varying the length of the reference arm by 0.5-mm step. A fiber partial reflector having 55-dB attenuation is used as the sample to be measured. Figure 5(a) also shows axial resolutions obtained from full width at half maximum (FWHM) at the reflection peaks in OCT profiles. From the result, the imaging depth, the averaged axial resolution and the 6-dB roll-off depth are determined to be 4.6 mm, 18 μm and 2.9 mm in air, respectively. These are well consistent with the theoretical values derived from the characteristics of the light source and the spectrometers described above. Figure 5(b) shows measured system sensitivities at the A-scan rate of 47 kHz with different reference light intensities, as shown by filled circles. The system sensitivity is defined as total attenuation of the fiber partial reflector when the reflection strength is the same as the noise floor level from the OCT profile. Figure 5(b) also shows theoretical curves with readout noise, shot noise, an excess noise and sum of these noises calculated according to the formula of system sensitivity of SD-OCT, derived by Leitgeb [14]. The measured system sensitivity is 104 dB in maximum, which is well consistent with the theoretical curve. As a reference, the system sensitivities using a supercontinuum (SC) light source at the Ascan rate of 0.96 kHz in our previous preliminary study [15] are also shown by open circles in Fig. 5(b). The measured system sensitivity is 108 dB in maximum but the optimal intensity is lower even at low A-scan rate. It indicates the excess noise by the SC light is dominant in the system sensitivity. On the other hand, the A-scan rate in this study is improved by 49 times with the system sensitivity kept over 100 dB owing to moderate excess noise of the SLDbased light source. This performance would be almost comparable to that of commercial intravascular OCT systems for clinical use. However, the maximum sensitivity is by 7 dB less than the shot noise limit (111 dB) as shown in the coarse broken line of Fig. 5(b) and there are some rooms for further improvement in readout noise by line sensor and excess noise by SLD modules.
Fig. 5. (a) Measured OCT profiles with different lengths with 0.5-mm step in the reference arm and 55-dB attenuation in the fiber reflector. Filled circles mean axial resolution measured from the OCT profiles. (b) System sensitivities in different reference input power. The filled and open circles mean measured system sensitivities in SLD-based and SC-based light source, respectively. The dotted, fine broken, coarse broken and solid lines mean theoretical values with readout noise, shot noise, excess noise and sum of these noises, respectively. The measured sensitivity in SLD-based light source is 104 dB in maximum and it is consistent to the theoretical values with all noises.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6650
4.2 Demonstration of lipid distribution imaging by an in-vitro artery model In order to demonstrate imaging of lipid distribution, we use an in-vitro artery model as shown in Fig. 6. The artery model consists of a piece of porcine carotid artery (Tokyo Shibaura Zoki, Tokyo, Japan) and a nylon tube filled with purified lard (Megmilk snow brand, Tokyo, Japan) as lipid material. The carotid artery is separated from surrounding fat tissue, and cut open in order to manage the placement of the lard-filled tube and the OCT catheter. As shown in the right of Fig. 6, the OCT catheter is inserted into the artery model under phosphate buffered saline (PBS) and pulled back by 53 mm length at the rate of 20 mm/s with rotation at 5660 rpm. The A-scan rate is 47 kHz. A set of 500 A-scan lines is used to construct an OCT image, resulting in 249 frames in a single pull-back scan.
Fig. 6. A photograph of the in-vitro artery model with a piece of porcine carotid artery and lipid (left) and a schematic cross-sectional drawing of the model in the measurement setup (right).
Figure 7 shows three frames (#71, #128 and #178) of cross-sectional OCT images of the artery model by 1.7-μm SD-OCT system. As shown in frame #71, the layer structure of OCT catheter and the shape of carotid artery and plaque phantom can be observed. However, frame #128 shows several black lines radially from the catheter as designated by the solid arrows in Fig. 7. These lines are due to motion artifacts where fringe washout occurs by instantaneous phase fluctuation during exposure of the line sensor. It is confirmed that the black lines are mitigated by setting shorter exposure time in the line sensor. Frame #178 shows so-called tangential artifact in the direction of 1 and 9 o’clock (open circles) where the angle of the surface of artery to the axial direction is small and OCT signal is attenuated. The white circle close to the OCT catheter as shown by a dotted arrow in Fig. 7 is an artifact due to aliasing image of inner reflection within the OCT catheter. It is confirmed that it moves to the opposite direction by a change of optical length in the reference arm. Although suppression of these artifacts will be necessary for future work, the capability to image distribution of lipid is demonstrated as shown below avoiding the influence by these artifacts. In the following, we verify lipid distribution imaging from the spectral data of artery model measured by 1.7-μm SD-OCT system. As Fig. 8, we select artery and lipid areas (Sartery, Slipid) at one frame of artery model data which is different from that in Fig. 7 as a training set and calculate lipid contents at all pixels (c(p)) without spatial filtering in both areas and the sensitivity (SE) and specificity (SP) at a decision threshold (cth). SE is defined as the ratio of number of pixels in the lipid areas (STP) which have lipid contents with more than the threshold and SP is defined as the ratio of number of pixels in the artery area (STN) which estimate one with less than the threshold, as shown in Eq. (1).
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6651
Fig. 7. Conventional cross-sectional OCT images of the artery model by 1.7-μm SD-OCT system. The solid and dotted arrows and open circles mean the image artifacts.
Fig. 8. Selection of artery and lipid areas (Sartery, Slipid) for training and distribution of estimated lipid contents (c) in both areas.
STP = { p ∈ Slipid | c( p ) ≥ cth } STN = { p ∈ S artery | c( p ) < cth } SE = card(STP ) / card(Slipid )
(1)
SP = card(S TN ) / card(Sartery ). For optimization, we seek the optimal values in all parameters to obtain maximum in the product of SE and SP at the optimal threshold. Figure 9 shows the receiver operating characteristics (ROC) curve in the training at best parameter sets, which is obtained by plotting a relation between true positive ( = SE [%]) and false positive ( = 100−SP [%]) with moving the threshold. The SE is 87% and the SP is 90% at the optimal threshold, respectively, which would be sufficient for diagnostic applications.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6652
Fig. 9. The ROC curve in pixel-by-pixel differentiation between artery and lipid areas at the parameters optimized by the training data and the sensitivity (SE) and specificity (SP) at the optimal threshold from the ROC curve.
Next, in order to evaluate the algorithm based on the optimal parameters determined as a result of training, we apply it to the artery model data used in Fig. 7 for a validation test. Figure 10 shows three frames (#168, #172 and #178) of lipid distribution images avoiding frames with artifacts as validation data, which are superimposed to conventional crosssectional ones in Fig. 7. All of these frames show that red areas with detection of lipid almost coincide with the plaque phantom. Moreover, note that the tangential artifact seen in frame #178 does not affect the detection of lipid, presumably because the artifact have different spectral characteristic from that of lipid. Figure 11 shows the ROC curves at the same frames, where both of SE and SP at the optimal threshold obtained by training are more than 90% in all of the three frames. The ability of differentiation is better than that in detection of TCFA by conventional OCT [16] which has sensitivity of 90% and specificity of 79%, respectively. It indicate the possibility for lipid distribution imaging by 1.7-μm spectroscopic SD-OCT.
Fig. 10. Three frames (#168, #172 and #178) of lipid distribution images of the validation data used in Fig. 7. These are superimposed to conventional cross-sectional images. The red area means the one with high lipid contents.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6653
Fig. 11. Validation areas of artery (green) and lipid (red) and ROC curves at the same frames (#168, #172 and #178) as that in Fig. 9. (The ROC curve in Fig. 9 is also plotted as a dotted line.) The SE and SP at the optimal threshold obtained by training are more than 90% in all of three frames.
The sensitivity and specificity for the validation data shown in Fig. 11 are over 91%, which are higher than the corresponding values 86.6% and 89.5% for the training data shown in Fig. 9. Although it might appear unusual that the accuracy for validation data are higher than those for the training data, the lower accuracy of the training data is caused by false negative pixels in the far edge of the lipid region, as shown in Fig. 12. As shown in Fig. 12, false negative tends to appear in the far edge of the lipid region especially in case there is low signal (vessel lumen filled with saline) region adjacent to the far edge of the lipid. We assume this false negative at the far edge of lipid is caused by spatial filtering and numerical derivative in the calculation to extract lipid. Detailed investigation into this effect is a target for future work.
Fig. 12. Example lipid distribution images to show false positve in the far edge of the lipid region adjacent to low signal saline region. The white arrows show lipid region and the gray allows false negative region. (a) test data as used in Fig. 8, (b,c) example frames having low signal region adjacent to the far edge of the lipid region, and (d) the validation data same as that shown in Fig. 10.
#228621 - $15.00 USD © 2015 OSA
Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6654
5. Conclusion We have developed the 1.7-µm spectroscopic OCT for imaging lipid distribution within blood vessel. For demonstration of the spectroscopic OCT, the 1.7-μm SD-OCT system is fabricated by customized super-luminescent diodes (SLDs) and extended InGaAs line sensors, and it is proved that the system sensitivity is over 100 dB at the A-scan rate of 47 kHz, which is almost comparable to that of commercial intravascular OCT systems for clinical use. And then we also demonstrate lipid distribution imaging of the in-vitro artery model by the 1.7-μm SD-OCT system. The OCT image have sufficient quality to evaluate blood vessel. Regarding imaging of lipid distribution, the sensitivity and specificity in pixel-by-pixel differentiation between artery and lipid are 87% and 90% in the training, respectively, and the validation test also shows accuracy over 90% in detection of lipid.
#228621 - $15.00 USD © 2015 OSA
Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6655
Abstract: We have developed a spectroscopic optical coherence tomography (OCT) for imaging lipid distribution within blood vessel in order to detect coronary artery plaque. A 1.7-μm spectral-domain OCT with A-scan rate of 47 kHz is fabricated using a broadband light source based on super-luminescent diodes and spectrometers based on extended InGaAs line sensors. We demonstrate imaging of lipid distribution in an in vitro artery model with lipid. The sensitivity and specificity in the differentiation between artery and lipid are 87% and 90% in the training, respectively. The validation test also shows detection of lipid with an accuracy over 90%. ©2015 Optical Society of America OCIS codes: (110.4500) Optical coherence tomography; (300.6340) Spectroscopy, infrared.
References and links 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
R. Virmani, F. D. Kolodgie, A. P. Burke, A. Farb, and S. M. Schwartz, “Lessons from sudden coronary death: A comprehensive morphological classification scheme for atherosclerotic lesions,” Arterioscler. Thromb. Vasc. Biol. 20(5), 1262–1275 (2000). T. Kume, T. Akasaka, T. Kawamoto, H. Okura, N. Watanabe, E. Toyota, Y. Neishi, R. Sukmawan, Y. Sadahira, and K. Yoshida, “Measurement of the thickness of the fibrous cap by optical coherence tomography,” Am. Heart J. 152(4), 755e1–755e4 (2006). G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. Express 4(7), 1014–1030 (2013). E. Regar, M. Gnanadesigan, A. F. Van der Steen, and G. van Soest, “Quantitative optical coherence tomography tissue-type imaging for lipid-core plaque detection,” JACC Cardiovasc. Interv. 6(8), 891–892 (2013). C. P. Fleming, J. Eckert, E. F. Halpern, J. A. Gardecki, and G. J. Tearney, “Depth resolved detection of lipid using spectroscopic optical coherence tomography,” Biomed. Opt. Express 4(8), 1269–1284 (2013). K. Jansen, M. Wu, A. F. W. van der Steen, and G. van Soest, “Photoacoustic imaging of human coronary atherosclerosis in two spectral bands,” Photoacoustics 2(1), 12–20 (2014). U. Sharma, E. W. Chang, and S. H. Yun, “Long-wavelength optical coherence tomography at 1.7 µm for enhanced imaging depth,” Opt. Express 16(24), 19712–19723 (2008). H. Kawagoe, S. Ishida, M. Aramaki, Y. Sakakibara, E. Omoda, H. Kataura, and N. Nishizawa, “Longwavelength optical coherence tomography at 1.7 μm for enhanced imaging depth,” Biomed. Opt. Express 16(24), 932–943 (2014). S. Ishida and N. Nishizawa, “Quantitative comparison of contrast and imaging depth of ultrahigh-resolution optical coherence tomography images in 800-1700 nm wavelength region,” Biomed. Opt. Express 3(2), 282–294 (2012). E. J. Jung, J. H. Lee, B. S. Rho, M. J. Kim, S. H. Hwang, W. J. Lee, J. J. Song, M. Y. Jeong, and C. S. Kim, “Spectrally Sampled OCT Imaging Based on 1.7-μm Continuous-Wave Supercontinuum Source,” IEEE J. Sel. Top. Quantum Electron. 18(3), 282–294 (2012). C. Xu, D. L. Marks, M. N. Do, and S. A. Boppart, “Separation of absorption and scattering profiles in spectroscopic optical coherence tomography using a least-squares algorithm,” Opt. Express 12(20), 4790–4803 (2004). M. Hirano, S. Tonosaki, T. Ueno, M. Tanaka, and T. Hasegawa, “Improved method to visualize lipid distribution within arterial vessel walls by 1.7 μm spectroscopic spectral-domain optical coherence tomography,” Proc. SPIE 8935, 893517 (2014). G. J. Tearney, S. A. Boppart, B. E. Bouma, M. E. Brezinski, N. J. Weissman, J. F. Southern, and J. G. Fujimoto, “Scanning single-mode fiber optic catheter-endoscope for optical coherence tomography,” Opt. Lett. 21(7), 543– 545 (1996).
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6645
14. R. Leitgeb, C. K. Hitzenberger, and A. F. Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography,” Opt. Express 11(8), 889–894 (2003). 15. M. Tanaka, M. Hirano, T. Hasegawa, and I. Sogawa, “Lipid distribution imaging in in-vitro artery model by 1.7μm spectroscopic spectral-domain optical coherence tomography,” Proc. SPIE 8565, 85654F (2013). 16. T. Kume, H. Okura, R. Yamada, T. Kawamoto, N. Watanabe, Y. Neishi, Y. Sadahira, T. Akasaka, and K. Yoshida, “Frequency and Spatial Distribution of Thin-Cap Fibroatheroma Assessed by 3-Vessel Intravascular Ultrasound and Optical Coherence Tomography: An Ex Vivo Validation and an Initial In Vivo Feasibility Study,” Circ. J. 73(6), 1086–1091 (2009).
1. Introduction Ischemic heart disease due to coronary arteriosclerosis is a major cause of death in developed countries. Intravascular imaging modalities using imaging catheters are powerful tools for detailed diagnosis of plaque lesions in coronary arteries. Among them, intravascular ultrasound (IVUS) has been the major modality for more than 20 years. However, its axial resolution as low as 100 µm is not sufficient to image a thin-cap fibroatheroma (TCFA) with cap thickness less than 65 µm, which is considered as a vulnerable plaque because its rapture can lead to acute coronary events [1]. On the other hand, OCT is recently attracting much attention, because its axial resolution as high as 10-15 μm enables accurate detection of the fibrous caps [2]. Vascular plaques are classified by its material composition into several types such as lipidic, calcified and fibrous. It is desirable to choose the optimum treatment for each plaque type. However, the conventional 1.3-µm OCTs take images of plaques based on scattering and attenuation within the tissue, so that material composition can be guessed from the OCT image only indirectly. As a result, it is sometimes difficult for physicians to judge the plaque type especially when the physician is not much experienced in reading OCT images. Several methods to classify plaque types automatically based on analysis of OCT image [3] and signal attenuation [4] have been reported. However, since 1.3-µm OCT signal reflects material composition only indirectly, accuracy of classification of plaque types could be limited. Spectroscopy is an effective method to characterize material composition, and there have been several spectroscopic methods proposed for imaging of vascular plaques. A spectroscopic OCT using conventional 1.3-μm wavelength [5] is proposed to image distribution of material composition. However, it needs many reference data and much training because there is relatively only small difference in absorption characteristics at 1.3 μm among compositions. Another attractive method is a photo-acoustic imaging using two wavelengths in 1.7-µm band [6]. However, since IVUS is necessary for detection of photoacoustic signal in addition to a laser delivery fiber, the catheter tends to be thick and expensive compared to the conventional IVUS and OCT. We propose a spectroscopic OCT using 1.7-μm band for imaging distribution of lipid which is an important constituent in vascular plaque. Since spectroscopic OCT is based on analysis of spectrum of interference light, it can extract information on distribution of composition in addition to conventional OCT image. Moreover, since spectral contrast of lipid is higher in 1.7-µm wavelength than in 1.3 µm, the 1.7-μm spectroscopic OCT would be more capable of detecting lipid than the conventional 1.3-µm one. In this paper, we report the results of fabrication of a spectroscopic SD-OCT system at 1.7-μm. The previous reports about the 1.7-μm OCT uses a swept light source [7] and super-continuum light sources [8– 10], whereas our system uses a SLD-based light source at 1.7-μm. We also demonstrate imaging of lipid distribution in an in vitro artery model using the 1.7-μm spectroscopic SDOCT system. 2. 1.7-μm spectroscopic OCT for lipid distribution imaging While conventional intravascular OCTs utilize light with a wavelength band at 1.3 μm which has low absorption by water, our spectroscopic OCT is based on a wavelength band of 1.7 μm. Figure 1 shows the attenuated reflection spectra over the wavelength range from 1.2 μm to 1.9 μm of purified lard (Megmilk snow brand, Tokyo, Japan), porcine carotid artery (Tokyo Shibaura Zoki, Tokyo, Japan), solid cholesterol made by melting and re-solidifying
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6646
cholesterol powder (Wako Pure Chemical Industries, Ltd., Osaka, Japan) and water, measured using NIR hyper spectral camera (Compovision, Sumitomo Electric Industries, Yokohama, Japan). The cholesterol and lard are chosen as representative material of lipid. While the spectra of lipids are not significantly different in spectral peaks from that of artery at 1.3 μm, they have significant spectral peaks at 1.7 μm due to C-H bonds. Thus lipid can be quantitatively detected by extracting the characteristic spectral attenuation in the 1.7µm OCT interference signal. Moreover, the lipid distribution can be visualized by analyzing dependence of the attenuation on depth using the spectroscopic OCT technique described below.
Fig. 1. Infrared spectra of lipids (lard and cholesterol), normal artery, and water.
The schematic diagram of 1.7-μm spectroscopic OCT is shown in Fig. 2. In the first step, 1.7-µm OCT interference spectrum is taken by the OCT hardware described later. In the second step, conventional OCT image representing distribution of scattering is constructed by Fourier transformation of the interference spectra, which is taken continuously while rotating the imaging core of the catheter. In the third step, in order to construct the image of lipid distribution, a set of sub-band tomographic signals as a function of depth and wavelength are calculated by dividing the interference spectra into several sub-band spectra and applying Fourier transformation to them. Then the axial distribution model of lipid content is fitted to the sub-band tomographic signals, based on the spectroscopic OCT method using least square analysis proposed by Xu et al. [11]. The axial distribution profiles of lipid are calculated while rotating the imaging core of the catheter, resulting in an image representing distribution of lipid. Post-processing methods such as spatial low-pass filtering are effective to suppress false positive detection around the borders of the vessel [12] and lipid distribution image is generated by distribution of lipid contents over a decision threshold after spatial filtering. 3. 1.7-μm SD-OCT system with intravascular imaging catheter Figure 3 shows an overview and the schematic diagram of the 1.7-μm SD-OCT system fabricated in order to demonstrate imaging of lipid distribution within blood vessel. The system contains, an optical unit composed of a light source, a fiber interferometer and spectrometers and a control system, and is integrated in a movable cabinet. The system also includes a motor drive unit for rotating and transferring light from/to the imaging core of the OCT catheter. The light source is custom-built by combining three SLDs emitting lights around 1.7 μm using couplers so as to output in a single-mode fiber. Figure 4(a) shows the output spectra of the light source of the respective SLDs and that of the combined output. The peak wavelengths of the three SLDs are 1653 nm, 1689 nm and 1740 nm, respectively. The total output power, the 3-dB bandwidth and the spectral flatness over spectrometer band are 19.6mW, 128 nm and 8 dB, respectively. As a reference, Fig. 4 also shows a typical absorption spectrum of lipid. The wavelength band covered by the light source has both of low and high absorption by lipid to cause large differences among the sub-band images.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6647
Fig. 2. The schematic diagram of intravascular image processing by 1.7-μm spectroscopic SDOCT. Interference spectra of 1.7-µm OCT is processed for constructing both conventional OCT image and lipid distribution image. The former is constructed by usual Fourier transform (FT), and the latter is constructed by sub-band Fourier-transform and calculation to extract lipid content.
Fig. 3. An overview (a) and the schematic diagram (b) of the 1.7-μm SD-OCT system. SLD: super-luminescent diode, CP: coupler, VOA: variable optical attenuator, CIR: circulator, MR: mirror on a moving stage, OFRJ: optical fiber rotary joint, PBS: polarization beam splitter, LS: line sensor, GT: grating, L: optical lens
In the following fiber interferometer, the broadband light from the light source is divided into a measurement arm and a reference one by a 90:10 fiber coupler (CP1). The light in the reference arm goes through a variable optical attenuator (VOA) for tuning input power to spectrometers and a dynamic polarization controller (PC) for controlling balance of input power between two spectrometers. The reference light makes a round trip between a fiber circulator (CIR1) and a mirror on a moving stage (MR) for tuning the optical length of the reference arm and couples into another fiber coupler (CP2) with coupling ratio of 20%. On the other hand, the light in the measurement arm goes through another circulator (CIR2), an optical fiber rotary joint (OFRJ) in the motor drive unit and the OCT catheter. The OCT catheter contains a rotatable imaging core with optical fiber and GRIN lens for side emitting on the tip enclosed inside a transparent outer sheath, similarly to the catheter proposed by
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6648
Tearney et al. [13]. The lens is designed to emit collimated beam with a beam divergence of 4.4 degree full width at 1.7 μm in order to re-couple the light scattered from the target to the fiber efficiently. The scattered light from target goes through OFRJ and CIR2 again and combines with the reference light at CP2 to make interference. Since the fiber interferometer consists of fiber optic products for telecommunications, not necessarily optimized for 1.7-µm band, the current system has a limitation that the transmission losses of the both arms increase at longer wavelength as shown in Fig. 4(b). The wavelength dependent loss over the spectrometer band is 4.7 dB and 4.0 dB in the reference and the sample arm, respectively. The interference light is divided into two orthogonal polarization axes and received by two spectrometers to obtain constant interference regardless of the polarization state of the measurement light. The spectrometer includes an extended InGaAs line sensor (LS) (Sensors Unlimited, USA) with 1024 pixels and the sensitivity up to 2.2 μm wavelength, a grating (GT) and a collimation and imaging lens (L1/L2). It is optimally designed to obtain a bandwidth needed for lipid distribution imaging while maintaining wavelength resolution. The maximum A-scan rate of the spectrometers is 47 kHz which is equal to the line rate of the InGaAs line sensors. Figure 4(c) shows the characteristics of wavelength and its resolution on two spectrometers. These are obtained by receiving a broadband light through an etalon filter and measuring the position and width of periodical beam spots projected in each spectrometer. In both of spectrometers, the wavelength range is 1608-1767 nm and the averaged wavelength resolution is 0.25 nm. Regarding the theoretical values estimated from these measured values, the image depth is 4.6 mm, the axial resolution is 18 μm and the 6-dB roll-off depth is 2.6 mm in air.
Fig. 4. (a) Output spectra of the 1.7-μm SLD-based light source. The three thin solid lines and the thick solid line designate the spectra of the three respective SLDs and the combined output. The driving currents of the SLDs are optimized to maximize the bandwidth. A broken line means the absorption spectrum of lipid as a reference. (b) Transmission losses of reference and measurement arms in the interferometer (except insertion loss of catheter). (c) Characteristics of center wavelength and wavelength resolution of two spectrometers (filled circles: #1 for P polarization, open circles: #2 for S polarization) as functions of pixel number of the line camera. The wavelength characteristics on two spectrometers are almost identical.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6649
4. Results and discussion 4.1 Performance of 1.7-μm SD-OCT system Firstly, we verify performance of the 1.7-μm SD-OCT system by a fiber partial reflector with a variable optical attenuator and the same optical length as the OCT catheter instead of the catheter. Figure 5(a) shows measured OCT A-scan profiles with varying the length of the reference arm by 0.5-mm step. A fiber partial reflector having 55-dB attenuation is used as the sample to be measured. Figure 5(a) also shows axial resolutions obtained from full width at half maximum (FWHM) at the reflection peaks in OCT profiles. From the result, the imaging depth, the averaged axial resolution and the 6-dB roll-off depth are determined to be 4.6 mm, 18 μm and 2.9 mm in air, respectively. These are well consistent with the theoretical values derived from the characteristics of the light source and the spectrometers described above. Figure 5(b) shows measured system sensitivities at the A-scan rate of 47 kHz with different reference light intensities, as shown by filled circles. The system sensitivity is defined as total attenuation of the fiber partial reflector when the reflection strength is the same as the noise floor level from the OCT profile. Figure 5(b) also shows theoretical curves with readout noise, shot noise, an excess noise and sum of these noises calculated according to the formula of system sensitivity of SD-OCT, derived by Leitgeb [14]. The measured system sensitivity is 104 dB in maximum, which is well consistent with the theoretical curve. As a reference, the system sensitivities using a supercontinuum (SC) light source at the Ascan rate of 0.96 kHz in our previous preliminary study [15] are also shown by open circles in Fig. 5(b). The measured system sensitivity is 108 dB in maximum but the optimal intensity is lower even at low A-scan rate. It indicates the excess noise by the SC light is dominant in the system sensitivity. On the other hand, the A-scan rate in this study is improved by 49 times with the system sensitivity kept over 100 dB owing to moderate excess noise of the SLDbased light source. This performance would be almost comparable to that of commercial intravascular OCT systems for clinical use. However, the maximum sensitivity is by 7 dB less than the shot noise limit (111 dB) as shown in the coarse broken line of Fig. 5(b) and there are some rooms for further improvement in readout noise by line sensor and excess noise by SLD modules.
Fig. 5. (a) Measured OCT profiles with different lengths with 0.5-mm step in the reference arm and 55-dB attenuation in the fiber reflector. Filled circles mean axial resolution measured from the OCT profiles. (b) System sensitivities in different reference input power. The filled and open circles mean measured system sensitivities in SLD-based and SC-based light source, respectively. The dotted, fine broken, coarse broken and solid lines mean theoretical values with readout noise, shot noise, excess noise and sum of these noises, respectively. The measured sensitivity in SLD-based light source is 104 dB in maximum and it is consistent to the theoretical values with all noises.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6650
4.2 Demonstration of lipid distribution imaging by an in-vitro artery model In order to demonstrate imaging of lipid distribution, we use an in-vitro artery model as shown in Fig. 6. The artery model consists of a piece of porcine carotid artery (Tokyo Shibaura Zoki, Tokyo, Japan) and a nylon tube filled with purified lard (Megmilk snow brand, Tokyo, Japan) as lipid material. The carotid artery is separated from surrounding fat tissue, and cut open in order to manage the placement of the lard-filled tube and the OCT catheter. As shown in the right of Fig. 6, the OCT catheter is inserted into the artery model under phosphate buffered saline (PBS) and pulled back by 53 mm length at the rate of 20 mm/s with rotation at 5660 rpm. The A-scan rate is 47 kHz. A set of 500 A-scan lines is used to construct an OCT image, resulting in 249 frames in a single pull-back scan.
Fig. 6. A photograph of the in-vitro artery model with a piece of porcine carotid artery and lipid (left) and a schematic cross-sectional drawing of the model in the measurement setup (right).
Figure 7 shows three frames (#71, #128 and #178) of cross-sectional OCT images of the artery model by 1.7-μm SD-OCT system. As shown in frame #71, the layer structure of OCT catheter and the shape of carotid artery and plaque phantom can be observed. However, frame #128 shows several black lines radially from the catheter as designated by the solid arrows in Fig. 7. These lines are due to motion artifacts where fringe washout occurs by instantaneous phase fluctuation during exposure of the line sensor. It is confirmed that the black lines are mitigated by setting shorter exposure time in the line sensor. Frame #178 shows so-called tangential artifact in the direction of 1 and 9 o’clock (open circles) where the angle of the surface of artery to the axial direction is small and OCT signal is attenuated. The white circle close to the OCT catheter as shown by a dotted arrow in Fig. 7 is an artifact due to aliasing image of inner reflection within the OCT catheter. It is confirmed that it moves to the opposite direction by a change of optical length in the reference arm. Although suppression of these artifacts will be necessary for future work, the capability to image distribution of lipid is demonstrated as shown below avoiding the influence by these artifacts. In the following, we verify lipid distribution imaging from the spectral data of artery model measured by 1.7-μm SD-OCT system. As Fig. 8, we select artery and lipid areas (Sartery, Slipid) at one frame of artery model data which is different from that in Fig. 7 as a training set and calculate lipid contents at all pixels (c(p)) without spatial filtering in both areas and the sensitivity (SE) and specificity (SP) at a decision threshold (cth). SE is defined as the ratio of number of pixels in the lipid areas (STP) which have lipid contents with more than the threshold and SP is defined as the ratio of number of pixels in the artery area (STN) which estimate one with less than the threshold, as shown in Eq. (1).
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6651
Fig. 7. Conventional cross-sectional OCT images of the artery model by 1.7-μm SD-OCT system. The solid and dotted arrows and open circles mean the image artifacts.
Fig. 8. Selection of artery and lipid areas (Sartery, Slipid) for training and distribution of estimated lipid contents (c) in both areas.
STP = { p ∈ Slipid | c( p ) ≥ cth } STN = { p ∈ S artery | c( p ) < cth } SE = card(STP ) / card(Slipid )
(1)
SP = card(S TN ) / card(Sartery ). For optimization, we seek the optimal values in all parameters to obtain maximum in the product of SE and SP at the optimal threshold. Figure 9 shows the receiver operating characteristics (ROC) curve in the training at best parameter sets, which is obtained by plotting a relation between true positive ( = SE [%]) and false positive ( = 100−SP [%]) with moving the threshold. The SE is 87% and the SP is 90% at the optimal threshold, respectively, which would be sufficient for diagnostic applications.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6652
Fig. 9. The ROC curve in pixel-by-pixel differentiation between artery and lipid areas at the parameters optimized by the training data and the sensitivity (SE) and specificity (SP) at the optimal threshold from the ROC curve.
Next, in order to evaluate the algorithm based on the optimal parameters determined as a result of training, we apply it to the artery model data used in Fig. 7 for a validation test. Figure 10 shows three frames (#168, #172 and #178) of lipid distribution images avoiding frames with artifacts as validation data, which are superimposed to conventional crosssectional ones in Fig. 7. All of these frames show that red areas with detection of lipid almost coincide with the plaque phantom. Moreover, note that the tangential artifact seen in frame #178 does not affect the detection of lipid, presumably because the artifact have different spectral characteristic from that of lipid. Figure 11 shows the ROC curves at the same frames, where both of SE and SP at the optimal threshold obtained by training are more than 90% in all of the three frames. The ability of differentiation is better than that in detection of TCFA by conventional OCT [16] which has sensitivity of 90% and specificity of 79%, respectively. It indicate the possibility for lipid distribution imaging by 1.7-μm spectroscopic SD-OCT.
Fig. 10. Three frames (#168, #172 and #178) of lipid distribution images of the validation data used in Fig. 7. These are superimposed to conventional cross-sectional images. The red area means the one with high lipid contents.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6653
Fig. 11. Validation areas of artery (green) and lipid (red) and ROC curves at the same frames (#168, #172 and #178) as that in Fig. 9. (The ROC curve in Fig. 9 is also plotted as a dotted line.) The SE and SP at the optimal threshold obtained by training are more than 90% in all of three frames.
The sensitivity and specificity for the validation data shown in Fig. 11 are over 91%, which are higher than the corresponding values 86.6% and 89.5% for the training data shown in Fig. 9. Although it might appear unusual that the accuracy for validation data are higher than those for the training data, the lower accuracy of the training data is caused by false negative pixels in the far edge of the lipid region, as shown in Fig. 12. As shown in Fig. 12, false negative tends to appear in the far edge of the lipid region especially in case there is low signal (vessel lumen filled with saline) region adjacent to the far edge of the lipid. We assume this false negative at the far edge of lipid is caused by spatial filtering and numerical derivative in the calculation to extract lipid. Detailed investigation into this effect is a target for future work.
Fig. 12. Example lipid distribution images to show false positve in the far edge of the lipid region adjacent to low signal saline region. The white arrows show lipid region and the gray allows false negative region. (a) test data as used in Fig. 8, (b,c) example frames having low signal region adjacent to the far edge of the lipid region, and (d) the validation data same as that shown in Fig. 10.
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Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6654
5. Conclusion We have developed the 1.7-µm spectroscopic OCT for imaging lipid distribution within blood vessel. For demonstration of the spectroscopic OCT, the 1.7-μm SD-OCT system is fabricated by customized super-luminescent diodes (SLDs) and extended InGaAs line sensors, and it is proved that the system sensitivity is over 100 dB at the A-scan rate of 47 kHz, which is almost comparable to that of commercial intravascular OCT systems for clinical use. And then we also demonstrate lipid distribution imaging of the in-vitro artery model by the 1.7-μm SD-OCT system. The OCT image have sufficient quality to evaluate blood vessel. Regarding imaging of lipid distribution, the sensitivity and specificity in pixel-by-pixel differentiation between artery and lipid are 87% and 90% in the training, respectively, and the validation test also shows accuracy over 90% in detection of lipid.
#228621 - $15.00 USD © 2015 OSA
Received 27 Nov 2014; revised 16 Jan 2015; accepted 19 Jan 2015; published 3 Mar 2015 9 Mar 2015 | Vol. 23, No. 5 | DOI:10.1364/OE.23.006645 | OPTICS EXPRESS 6655
