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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 2, Iss. 8 — Aug. 1, 2011
  • pp: 2096–2109
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Optimizing probe design for an implantable perfusion and oxygenation sensor

Tony J. Akl, Ruiqi Long, Michael J. McShane, M. Nance Ericson, Mark A. Wilson, and Gerard L. Coté  »View Author Affiliations


Biomedical Optics Express, Vol. 2, Issue 8, pp. 2096-2109 (2011)
http://dx.doi.org/10.1364/BOE.2.002096


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Abstract

In an effort to develop an implantable optical perfusion and oxygenation sensor, based on multiwavelength reflectance pulse oximetry, we investigate the effect of source–detector separation and other source-detector characteristics to optimize the sensor’s signal to background ratio using Monte Carlo (MC) based simulations and in vitro phantom studies. Separations in the range 0.45 to 1.25 mm were found to be optimal in the case of a point source. The numerical aperture (NA) of the source had no effect on the collected signal while the widening of the source spatial profile caused a shift in the optimal source-detector separation. Specifically, for a 4.5 mm flat beam and a 2.4 mm × 2.5 mm photodetector, the optimal performance was found to be when the source and detector are adjacent to each other. These modeling results were confirmed by data collected from in vitro experiments on a liver phantom perfused with dye solutions mimicking the absorption properties of hemoglobin for different oxygenation states.

© 2011 OSA

1. Introduction

According to the Organ Procurement and Transplantation Network (OPTN) and the Scientific Registry of Transplant Recipients (SRTR), in their 2009 OPTN/SRTR report (Table 1.3), between the years 1999 and 2008 the number of registrations on the waitlist for liver transplant in the United States at the end of each year ranged between 14,165 and 18,243. In this same period, the number of transplant recipients per year ranged between 4,498 and 6,363 (their Table 9.4a) [1

1. U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients, “2009 OPTN/SRTR Annual Report: Transplant Data 1999-2008,” U.S. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD.

]. These numbers show a clear need to minimize graft loss, increase chances of transplant success, and maximize transplant rates. Graft loss is mainly due to ischemia and an implantable sensor that monitors the graft status in the 7 to 14 days period post-surgery to ensure adequate perfusion and oxygenation are available to the liver can help prevent organ failure.

Some of the prevailing perfusion monitoring techniques are Doppler based such as laser Doppler flowmetry (LDF) [2

2. A. Humeau, W. Steenbergen, H. Nilsson, and T. Strömberg, “Laser Doppler perfusion monitoring and imaging: novel approaches,” Med. Biol. Eng. Comput. 45(5), 421–435 (2007). [CrossRef] [PubMed]

] and Doppler ultrasound [3

3. T. Kok, M. J. H. Slooff, C. J. P. Thijn, P. M. J. G. Peeters, R. Verwer, C. M. A. Bijleveld, A. P. van den Berg, E. B. Haagsma, and I. J. Klompmaker, “Routine Doppler ultrasound for the detection of clinically unsuspected vascular complications in the early postoperative phase after orthotopic liver transplantation,” Transpl. Int. 11(4), 272–276 (1998). [CrossRef] [PubMed]

]. These techniques measure the Doppler shift caused by any moving objects to deduce flow. This leads to high motion artifacts making them not suitable for implantable sensors [2

2. A. Humeau, W. Steenbergen, H. Nilsson, and T. Strömberg, “Laser Doppler perfusion monitoring and imaging: novel approaches,” Med. Biol. Eng. Comput. 45(5), 421–435 (2007). [CrossRef] [PubMed]

]. Another technique widely used in the clinical setting is thermal diffusion [4

4. P. Vajkoczy, H. Roth, P. Horn, T. Lucke, C. Thomé, U. Hubner, G. T. Martin, C. Zappletal, E. Klar, L. Schilling, and P. Schmiedek, “Continuous monitoring of regional cerebral blood flow: experimental and clinical validation of a novel thermal diffusion microprobe,” J. Neurosurg. 93(2), 265–274 (2000). [CrossRef] [PubMed]

,5

5. M. B. Khot, P. K. Maitz, B. R. Phillips, H. F. Bowman, J. J. Pribaz, and D. P. Orgill, “Thermal diffusion probe analysis of perfusion changes in vascular occlusions of rabbit pedicle flaps,” Plast. Reconstr. Surg. 115(4), 1103–1109 (2005). [CrossRef] [PubMed]

]. This technique, originally introduced by Bowman et al. [6

6. H. F. Bowman and T. A. Balasubramaniam, “A new technique utilizing thermistor probes for the measurement of thermal properties of biomaterials,” Cryobiology 13(5), 572–580 (1976). [CrossRef] [PubMed]

], measures tissue perfusion based on thermal convection. A thermal diffusion probe consists of two thermistors, the proximal thermistor is passive and measures the baseline temperature of the surrounding tissue while the distal thermistor is active and is powered to cause a local increase in temperature (of about 2°C). The power needed to cause and sustain this temperature elevation is used to measure blood perfusion to the tissue. A typical thermal diffusion probe is around 1 mm in diameter [4

4. P. Vajkoczy, H. Roth, P. Horn, T. Lucke, C. Thomé, U. Hubner, G. T. Martin, C. Zappletal, E. Klar, L. Schilling, and P. Schmiedek, “Continuous monitoring of regional cerebral blood flow: experimental and clinical validation of a novel thermal diffusion microprobe,” J. Neurosurg. 93(2), 265–274 (2000). [CrossRef] [PubMed]

,5

5. M. B. Khot, P. K. Maitz, B. R. Phillips, H. F. Bowman, J. J. Pribaz, and D. P. Orgill, “Thermal diffusion probe analysis of perfusion changes in vascular occlusions of rabbit pedicle flaps,” Plast. Reconstr. Surg. 115(4), 1103–1109 (2005). [CrossRef] [PubMed]

]. These probes have been used in multiple microvascular applications ranging from cerebral blood flow [4

4. P. Vajkoczy, H. Roth, P. Horn, T. Lucke, C. Thomé, U. Hubner, G. T. Martin, C. Zappletal, E. Klar, L. Schilling, and P. Schmiedek, “Continuous monitoring of regional cerebral blood flow: experimental and clinical validation of a novel thermal diffusion microprobe,” J. Neurosurg. 93(2), 265–274 (2000). [CrossRef] [PubMed]

] to pedicle flaps [5

5. M. B. Khot, P. K. Maitz, B. R. Phillips, H. F. Bowman, J. J. Pribaz, and D. P. Orgill, “Thermal diffusion probe analysis of perfusion changes in vascular occlusions of rabbit pedicle flaps,” Plast. Reconstr. Surg. 115(4), 1103–1109 (2005). [CrossRef] [PubMed]

]. They also showed success in ex vivo hepatic perfusion monitoring [7

7. R. Ricciardi, B. K. Schaffer, R. D. Kim, S. A. Shah, S. E. Donohue, S. M. Wheeler, S. H. Quarfordt, M. P. Callery, W. C. Meyers, and R. S. Chari, “Protective effects of ischemic preconditioning on the cold-preserved liver are tyrosine kinase dependent,” Transplantation 72(3), 406–412 (2001). [CrossRef] [PubMed]

,8

8. E. Klar, T. Kraus, J. Bleyl, W. H. Newman, H. F. Bowman, W. J. Hofmann, R. Kummer, M. Bredt, and C. Herfarth, “Thermodiffusion for continuous quantification of hepatic microcirculation--validation and potential in liver transplantation,” Microvasc. Res. 58(2), 156–166 (1999). [CrossRef] [PubMed]

]. However, although very suitable to measure tissue perfusion, the principle of thermal diffusion is difficult to apply directly on blood vessels. Another problem with both aforementioned techniques is the lack of oxygenation information that is essential in judging the wellbeing of the graft. Pulse oximetry is the standard for measuring oxygenation in clinics [9

9. J. G. Webster, Design of Pulse Oximeters, Medical Science Series (Institute of Physics Pub., Bristol; Philadelphia, 1997), p. xvi.

,10

10. T. Aoyagi, “Pulse oximetry: its invention, theory, and future,” J. Anesth. 17(4), 259–266 (2003). [CrossRef] [PubMed]

]. It is based on investigating the difference in the optical properties of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbO) using two wavelengths of light on both sides of the hemoglobin isobestic point (805 nm). Reichelt et al. reported an implantable pulse oximeter system [11

11. S. Reichelt, J. Fiala, A. Werber, K. Förster, C. Heilmann, R. Klemm, and H. Zappe, “Development of an implantable pulse oximeter,” IEEE Trans. Biomed. Eng. 55(2), 581–588 (2008). [CrossRef] [PubMed]

] for monitoring blood oxygenation and pulse rate in vessels. This sensor operates in transmission mode and it was found that this mode, with the two selected wavelengths (660 and 940 nm) is not suitable for vessels larger than 5 mm in diameter. The portal vein, the main blood supplier to the liver [12

12. A. C. Guyton, Textbook of Medical Physiology, 8th ed. (Saunders, Philadelphia, 1991), p. xli.

], is 7.8 +/− 1.5 mm in diameter [13

13. C. Tziafalia, M. Vlychou, K. Tepetes, N. Kelekis, and I. V. Fezoulidis, “Echo-Doppler measurements of portal vein and hepatic artery in asymptomatic patients with hepatitis B virus and healthy adults,” J. Gastrointestin. Liver Dis. 15(4), 343–346 (2006). [PubMed]

] and thus, such a sensor is not suitable for monitoring blood supply through the portal vein. Moreover, this system does not detect changes in perfusion and is suitable only for monitoring blood oxygenation in blood vessels and not in tissue.

Our sensor employs a modified version of reflectance pulse oximetry with an added wavelength at the hemoglobin isobestic point (805 nm) to be used to separate the perfusion and oxygenation signals [14

14. M. N. Ericson, M. A. Wilson, G. L. Coté, J. S. Baba, W. Xu, M. Bobrek, C. L. Britton, M. S. Hileman, M. R. Moore, M. S. Emery, and R. Lenarduzzi, “Implantable sensor for blood flow monitoring after transplant surgery,” Minim. Invasive Ther. Allied Technol. 13(2), 87–94 (2004). [CrossRef] [PubMed]

16

16. H. Subramanian, B. L. Ibey, W. J. Xu, M. A. Wilson, M. N. Ericson, and G. L. Coté, “Real-time separation of perfusion and oxygenation signals for an implantable sensor using adaptive filtering,” IEEE Trans. Biomed. Eng. 52(12), 2016–2023 (2005). [CrossRef] [PubMed]

]. Since the sensor operates on diffuse reflection, it can be used to monitor perfusion and oxygenation on both blood vessels and parenchymal tissue. Multiple sensors at the input vessels and the parenchymal tissue will help identify oxygen consumption, blood perfusion and perfusion homogeneity in the liver which are the three parameters needed to detect ischemia. The sensor employs three, time multiplexed, light emitting diodes (735, 805 and 940 nm) modulated to increase the signal to noise ratio. Although our focus is to use this sensor to monitor perfusion and oxygenation to the liver post-transplant, this sensor can be used to monitor perfusion and oxygenation to most visceral organs.

The performance of sensors based on diffuse reflection has been shown to be highly dependent on the probe design and especially on the source to detector separation [17

17. J. L. Reuss and D. Siker, “The pulse in reflectance pulse oximetry: modeling and experimental studies,” J. Clin. Monit. Comput. 18(4), 289–299 (2004). [CrossRef] [PubMed]

21

21. T. J. Akl, T. J. King, R. Long, J. S. Baba, M. J. McShane, M. N. Ericson, M. A. Wilson, and G. L. Cote, ““Optimizing source detector separation for an implantable perfusion and oxygenation sensor,”,” Proc. SPIE 7906, 790605, 790605-5 (2011). [CrossRef]

]. In this work, we investigate the effect of the source to detector separation on the performance of the previously mentioned sensor through Monte Carlo modeling and in vitro phantom studies.

2. Materials and methods

2.1. Monte Carlo simulations

A multilayer photon propagation Monte Carlo simulation program based on the algorithm developed by Wang et al. [22

22. L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995). [CrossRef] [PubMed]

] was developed in MATLAB. The placement of the sensor on the portal vein, the main blood supplier to the liver [12

12. A. C. Guyton, Textbook of Medical Physiology, 8th ed. (Saunders, Philadelphia, 1991), p. xli.

], and the hepatic artery were modeled as three layer structures (Fig. 1
Fig. 1 A schematic of the multilayer model mimicking a blood vessel.
). The first and third layer have the optical properties of liver tissue [23

23. J.-P. Ritz, A. Roggan, C. Isbert, G. Müller, H. J. Buhr, and C.-T. Germer, “Optical properties of native and coagulated porcine liver tissue between 400 and 2400 nm,” Lasers Surg. Med. 29(3), 205–212 (2001). [CrossRef] [PubMed]

] and the middle layer has the optical properties of whole blood and the dimensions of the vessel under investigation (Table 1

Table 1. Diameter and wall thickness of the portal vein [13,27] and hepatic artery [13,28]

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). The optical properties of blood were changed to mimic different oxygenation states [24

24. M. Friebel, J. Helfmann, U. Netz, and M. Meinke, “Influence of oxygen saturation on the optical scattering properties of human red blood cells in the spectral range 250 to 2,000 nm,” J. Biomed. Opt. 14(3), 034001 (2009). [CrossRef] [PubMed]

,25

25. S. Prahl, “Optical absorption of hemoglobin” (www.omlc.org, 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/.

]. Table 2

Table 2. Blood optical properties for two oxygenation states [24,25]

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shows the optical properties of the two oxygenation states of blood at the three wavelengths of interest [25

25. S. Prahl, “Optical absorption of hemoglobin” (www.omlc.org, 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/.

,26

26. D. J. Faber, M. C. G. Aalders, E. G. Mik, B. A. Hooper, M. J. C. van Gemert, and T. G. van Leeuwen, “Oxygen saturation-dependent absorption and scattering of blood,” Phys. Rev. Lett. 93(2), 028102 (2004). [CrossRef] [PubMed]

].

For each run, 1 million photon packets were launched in different directions, with equal probability, within the Numerical Aperture (NA) of the source. The program recorded the collected intensity at the tissue surface and the corresponding penetration depth of each photon collected, producing a photon distribution map indicating at every point of the surface how many photons were collected and the penetration depth of each photon. This feature was used to find the probing depth for every source to detector separation and to separate the background intensity collected from photons that are probing the blood.

To study the effect of the source spatial distribution, the collected intensity was convoluted with the source spatial profile as discussed by Wang et al. [29

29. L. Wang, S. L. Jacques, and L. Zheng, “CONV--convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Methods Programs Biomed. 54(3), 141–150 (1997). [CrossRef] [PubMed]

]. All the modeling results were verified by comparison to MCML and CONV developed by Wang et al. [22

22. L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995). [CrossRef] [PubMed]

,29

29. L. Wang, S. L. Jacques, and L. Zheng, “CONV--convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Methods Programs Biomed. 54(3), 141–150 (1997). [CrossRef] [PubMed]

].

2.2. In vitro setup

To verify the modeling results, an in vitro system was constructed to measure perfusion and oxygenation in a liver phantom at different source - detector separations. The phantoms were designed to imitate the case where the sensor is placed on a blood vessel (PV or HA) at the entrance of the liver to monitor the blood supply.

To avoid handling whole blood that needs to be in a pH and temperature controlled medium, and requires oxygenators and blood analyzers to change and test oxygen levels, we formulated two dye solutions that have optical properties similar to circulating blood in its oxygenated and deoxygenated states [25

25. S. Prahl, “Optical absorption of hemoglobin” (www.omlc.org, 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/.

,26

26. D. J. Faber, M. C. G. Aalders, E. G. Mik, B. A. Hooper, M. J. C. van Gemert, and T. G. van Leeuwen, “Oxygen saturation-dependent absorption and scattering of blood,” Phys. Rev. Lett. 93(2), 028102 (2004). [CrossRef] [PubMed]

]. To mimic the absorption spectrum of whole blood in its deoxygenated state, we used a mixture of 0.165% v/v India ink (Speedball Art, product number 3338) and 2.67% v/v inkjet photo cyan (Macro Enter, cat.# FPB085) dissolved in Phosphate Buffered Solution (PBS 0.01M, pH 7.4). The India ink has an extinction coefficient that covers all the wavelength bands of interest [31

31. P. Di Ninni, F. Martelli, and G. Zaccanti, “The use of India ink in tissue-simulating phantoms,” Opt. Express 18(26), 26854–26865 (2010). [CrossRef] [PubMed]

,32

32. S. J. Madsen, M. S. Patterson, and B. C. Wilson, “The use of India ink as an optical absorber in tissue-simulating phantoms,” Phys. Med. Biol. 37(4), 985–993 (1992). [CrossRef] [PubMed]

] and it was used to create a baseline in the extinction coefficient spectrum that matches the absorption in the near infrared wavelength range. The inkjet photo cyan was used to increase the absorption on the red side of the spectrum, a characteristic of the deoxygenated state of blood. On the other hand, the oxygenated state has a higher absorption in the NIR region and 188.6 mg/L of Epolight 2717 (Epolin, Inc.) was used to increase the absorption in the NIR region along with 0.088% v/v of India ink in a PBS. The extinction coefficient of the two dye solutions compared to that of whole blood is shown in Fig. 3
Fig. 3 A comparison of the absorption spectra of whole blood and the two dye solutions used in the in vitro setup. M1 is the dye mixture mimicking oxygenated blood and is made of Epolight 2717 and India ink. M2 is made of India ink and inkjet photo cyan to mimic the optical properties of deoxygenated blood. The solid lines show the extinction coefficients of the two oxygenation states of whole blood [25,26].
.

The extinction coefficient of the dye solutions matches closely to that of whole blood at the three wavelengths of interest (735, 805 and 940 nm). The isobestic point is red shifted when compared to the data reported by Prahl [25

25. S. Prahl, “Optical absorption of hemoglobin” (www.omlc.org, 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/.

], however, there are inconsistencies in the reported isobestic point between different groups and it ranges between 797 nm [25

25. S. Prahl, “Optical absorption of hemoglobin” (www.omlc.org, 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/.

] and 815 nm [33

33. S. Takatani and M. D. Graham, “Theoretical analysis of diffuse reflectance from a two-layer tissue model,” IEEE Trans. Biomed. Eng. BME-26(12), 656–664 (1979). [CrossRef] [PubMed]

]. Our dye mixtures have an isobestic point, at 807 nm, in between these two extremes. At 735 nm the extinction coefficient of the dye solution M1 does not match exactly that of the oxygenated state of circulating blood. This causes the dynamic range between the two dye solutions at 735 nm to decrease causing the resolution in detecting oxygenation changes to decrease. For the purpose of this paper, this is not problematic since the main goal is to determine the optimal probe design and the same dye solution will be used to compare all of the different designs. At 940 nm, the two solutions match the desired extinction coefficients exactly.

Two peristaltic pumps (Gilson, Minipuls 3) driven using LabVIEW via a data acquisition board (National Instruments, USB 6009) were used to pump the dye solutions through the phantom. Extra care was taken to ensure no air bubbles were circulated. The driver signal used to control the pumps was a square wave. The frequency and the pulse width were set to the desired values that mimic the cardiac pulsatile flow pattern. Each pump can be used to pump a different dye solution corresponding to different oxygenation states and the mixture can mimic any oxygenation state by changing the ratio of the pump speeds.

The probes used are circular (1 inch in diameter) and contain a single can package with four light emitting diodes (epitex, L660/735/805/940-40B42-C-I) and a silicon photodetector (Hamamatsu, S2833-01). Five different LED to photodetector spacings were used ranging from 2 mm to 10 mm (edge to edge separation) with a step of 2 mm. A mechanical arm was used to fix the probe on the surface of the phantom above the hollow cylinder (Fig. 2) with the LED and photodetector aligned along the tube. The three wavelengths are time multiplexed to minimize power consumption and prevent overlap of the optical signal at the photodetector [14

14. M. N. Ericson, M. A. Wilson, G. L. Coté, J. S. Baba, W. Xu, M. Bobrek, C. L. Britton, M. S. Hileman, M. R. Moore, M. S. Emery, and R. Lenarduzzi, “Implantable sensor for blood flow monitoring after transplant surgery,” Minim. Invasive Ther. Allied Technol. 13(2), 87–94 (2004). [CrossRef] [PubMed]

]. Figure 4
Fig. 4 In vitro setup showing the two peristaltic pumps pushing a dye solution through the PV phantom with the optical probe held on top with a mechanical arm. Note the phantom appears light bluish in color to the eye but has the optical properties, in the 735-940 nm wavelength range, of liver.
shows the complete setup with the LabVIEW interface used for data collection and visualization.

3. Results

3.1. Monte Carlo modeling results

The diffuse reflection intensity at the surface decayed rapidly with increasing source-detector separation for all three wavelengths, for both simulated oxygenated and de-oxygenated signals. However, these intensities (the case of oxygenated blood is shown in Fig. 5a
Fig. 5 Left panel; Fluence decay curves for all three wavelengths as a function source to detector separation. These curves correspond to the case of oxygenated blood. The curves indicate a rapid decay in total collected fluence with increasing separation. Right panel: the difference (S = ROXY – RDEOXY) as a function of source to detector separation. The signal S shows a plateau phase with a maximal value followed by a rapid decay for the 735 and 940 nm wavelengths. The 805 nm wavelength carries a low oxygenation signal since it was slightly on the IR side of the isobestic point. The arrows indicate the end of the plateau phase and the start of the rapid decay.
) carry signal from the blood volume as well as background intensity from the surrounding tissue. To separate the signal from background intensity, we looked at the difference between the collected intensity in the cases of oxygenated and deoxygenated blood. This difference eliminates the constant background intensity and is a better indicator of the optimal spacing to collect the highest signal probing the blood volume. The difference showed a plateau phase followed by a quick decay for the 735 and 940 nm wavelengths. This plateau extends to 0.55 and 0.45 mm for the 735 and 940 nm wavelengths respectively (Fig. 5b). However, the 805 nm wavelength, since it is near the isobestic point, carried little information about the oxygenation state. Note that the 805 nm wavelength does have a little bias to the 940 nm side, since in our model 805 nm is on the IR side of the isobestic point. After the peak, the difference signal decayed rapidly to reach 10% of its maximal value at 2.4 and 2.2 mm for the 735 and 940 nm wavelengths respectively. Despite the rapid decay, there was still a relatively long range where the signal is strong enough to have a dynamic difference between the two oxygenation states.

In Fig. 5 the signal is presented in terms of its total reflectance at a specific distance (Fig. 5a for oxygenated simulation) and difference between oxygenated and de-oxygenated reflectance (Fig. 5b). However, the overall intensity decays with increasing separation. This implies that, although signal (S) is decaying, the signal to background ratio (SBR) might be constant or even increasing. To validate this hypothesis, we looked at the normalized intensity that we defined as the ratio of signal difference (S) to the total average intensity (It). Equation (1) shows the defined SBR ratio where Roxy and RDeoxy are the photon fluence at the surface in the oxygenated and deoxygenated state respectively.

SBR=ROxyRDeoxy0.5×(ROxy+RDeoxy)=SIt.
(1)

S is the signal defined as the difference between the collected intensities in the oxygenated and deoxygenated states: S=ROxyRDeoxy

It is the average total intensity in the oxygenated and deoxygenated states:

It=ROxy+RDeoxy2.

Note that It is the average total intensity; thus it comprises both signal and background and the defined SBR is the ratio of the difference signal to the total collected intensity which gives an idea about the signal to background ratio.

Figure 6
Fig. 6 SBR as a function of source to detector separation. SBR starts from 0 and absolute value increases monotonically (Note by definition this means at 735 nm the SBR is positively increasing and at 940 nm the SBR is negatively increasing with separation indicating a decrease in the background to signal ratio. For the 805 nm wavelength, SBR is close to zero with a little bias to the negatively increasing 940 nm side, since in our model 805 nm is on the IR side of the isobestic point.
shows the SBR calculated using (1) as a function of source-detector separation. In the source-detector separation range shown (0.05 to 10 mm) the absolute value of the SBR is monotonically increasing. Past this range, this ratio is very noisy due to the weakness of the collected intensity.

To better understand this phenomenon, we added to our MC algorithm a feature to record the penetration depth of the collected photons on the tissue surface. As shown in Fig. 7(a)
Fig. 7 The distribution of the collected photon fluence on the penetration depth for the 735 nm wavelength. Photons with a penetration depth higher than 0.5 mm are considered as signal while those that have a shallower penetration depth are considered as background since they do not probe the blood volume. Panel (a) shows the data for 5 selected source-detector separations. Panel (b) is a 3D representation of the data.
below, although the total intensity decreased, the ratio of photons collected from the vessel lumen (deeper than 0.5 mm which is the vessel upper limit) increased relatively to the photons probing the shallow tissue as we increased the source to detector separation. However, the total number of collected photons probing the blood layer decreased. This supports the data presented in Figs. 5 and 6 and indicates that the total intensity and signal decrease with increasing source to detector separations but the signal to background ratio increases. These findings are very important to know when designing probes for this type of sensor in order to optimize the use of the dynamic range of the photodetector as discussed in further details in section 4.

Figure 7(b) shows the collected photon fluence as a function of separation and penetration depth of each photon. This figure shows two peaks before and after the vessel lumen boundary at 0.5 mm. The shallower peak corresponds to the background intensity and, although higher in intensity, decays (as a function of source to detector separation) faster than the second peak that corresponds to the signal collected from the vessel lumen. The axes on this graph start at 0.2 mm (depth) and 0.1 mm (source to detector separation) because before these points there is a high background peak (at 0.1 mm depth and 0.1 mm source to detector separation) that is difficult to visualize with the signal peak on the same scale.

One way to maximize both quantities, SBR and S, if weighted equally is to maximize their product (Eq. (2)). Figure 8
Fig. 8 The product of signal (S) and SBR as a function of source to detector separation. The product shows a plateau phase with maximal S*SBR product for the 735 and 940 nm wavelengths. The 805 nm wavelength carries almost no oxygenation signal and the product is stable at nearly zero.
shows the product as a function of source-detector separation.

S*SBR=(ROxyRDeoxy)20.5*(ROxy+RDeoxy).
(2)

As expected, the 805 nm wavelength carries almost no oxygenation information and the product is stable at nearly zero for all spacings. However, for both the 735 and 940 nm wavelengths, the product starts as an increasing function with source to detector separation and plateaus at 0.35 and 0.45 mm respectively. This plateau ends and a rapid decay starts around 1.25 and 1.35 mm, respectively.

To investigate the effect of the source spatial profile, we compared the results of the point source to those of a 4.5 mm cylindrical flat beam mimicking the light emitting diodes used in our probes (Epitex, L660/735/805/940-40B42-C-I) and a photodetector with an active area of 2.4 mm x 2.5 mm (Hamamatsu, S2833-01). Figure 9
Fig. 9 The quantities discussed earlier for a 4.5 mm source and a 2.4 mm x 2.5 mm photodetector. The diffuse reflectance for all three wavelengths was normalized to the reflectance at the closest separation for the 735 nm wavelength. The signal S was normalized similarly.
shows the corresponding results.

3.2. In vitro results

S=(IOxyIOxy805)(IDeoxyIDeoxy805)=(IOxyIDeoxy)ΔI805.
(3)

4. Discussion

To better understand this tradeoff between signal levels and the signal to background ratio, we ran simulations for different oxygenation levels and observed the correlation between the photon fluence values and the oxygenation levels as shown in Figs. 11(a)-(c)
Fig. 11 The change in photon fluence at the surface of the tissue with changing oxygenation levels. The three panels (a)-(c) correspond to three different source-detector spacings indicated in the inset legends. At 0.45 mm separation, the change in diffuse reflection correlates well with the oxygenation changes (R2 = 0.994) and the slope of the linear fit is high (0.00844) indicating a good sensitivity to oxygenation changes. Panel (d) shows the changes of the R2 factor and the slope as a function of the source to detector separation.
. For very short separations, the coefficient of determination, R2, started as an increasing function and peaked at 0.45 mm separation with a peak value of 0.9941. Past this point, R2 showed a decreasing trend and reached 0.9795 (0.7022) at 1.05 mm (10.05 mm) separation. As for the slope of the linear fit, it showed a similar trend with increasing source-detector separation as the signal S which was expected (Fig. 11(d)).

The coefficient R2 shows that, although the slope representing the signal level is highest for the shortest separation (Fig. 11(d)), the optimal spacing where the signal correlates best with changes in the vessel lumen is at 0.45 mm. This is shown in Figs. 11(a)-(c) where the slope and intensity values are the highest in panel (a) (d = 0.15 mm) while the photon fluence doesn’t correlate with oxygenation as closely as in the top right panel (d = 0.45 mm).

Although the principle we used in optimizing the source-detector separation relies on changing the oxygen saturation of the blood and monitoring its effect on the collected diffuse reflectance, the results stand true for any change in the optical properties of the blood whether they were due to oxygenation, perfusion or hematocrit variations.

As mentioned earlier in the introduction, this sensor can be used to monitor perfusion and oxygenation in many other visceral organs. Since the optimal source-detector separation is a function of the optical properties and the dimensions of the vessel under investigation, the results presented in this paper stand true only for the case of the portal vein. However, the same methods employed in this manuscript can be used to define the optimal source-detector separation for similar applications.

5. Conclusions

Acknowledgments

This research was funded by a bioengineering research partnership (BRP) grant from NIH, (#5R01-GM077150). The data and analyses reported in the 2009 Annual Report of the U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients have been supplied by UNOS and Arbor Research under contract with HHS/HRSA. The authors alone are responsible for reporting and interpreting these data; the views expressed herein are those of the authors and not necessarily those of the U.S. Government. The authors would like to thank Travis King, M.S. student in the Optical Biosensing Laboratory, for his helpful discussions.

References and links

1.

U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients, “2009 OPTN/SRTR Annual Report: Transplant Data 1999-2008,” U.S. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD.

2.

A. Humeau, W. Steenbergen, H. Nilsson, and T. Strömberg, “Laser Doppler perfusion monitoring and imaging: novel approaches,” Med. Biol. Eng. Comput. 45(5), 421–435 (2007). [CrossRef] [PubMed]

3.

T. Kok, M. J. H. Slooff, C. J. P. Thijn, P. M. J. G. Peeters, R. Verwer, C. M. A. Bijleveld, A. P. van den Berg, E. B. Haagsma, and I. J. Klompmaker, “Routine Doppler ultrasound for the detection of clinically unsuspected vascular complications in the early postoperative phase after orthotopic liver transplantation,” Transpl. Int. 11(4), 272–276 (1998). [CrossRef] [PubMed]

4.

P. Vajkoczy, H. Roth, P. Horn, T. Lucke, C. Thomé, U. Hubner, G. T. Martin, C. Zappletal, E. Klar, L. Schilling, and P. Schmiedek, “Continuous monitoring of regional cerebral blood flow: experimental and clinical validation of a novel thermal diffusion microprobe,” J. Neurosurg. 93(2), 265–274 (2000). [CrossRef] [PubMed]

5.

M. B. Khot, P. K. Maitz, B. R. Phillips, H. F. Bowman, J. J. Pribaz, and D. P. Orgill, “Thermal diffusion probe analysis of perfusion changes in vascular occlusions of rabbit pedicle flaps,” Plast. Reconstr. Surg. 115(4), 1103–1109 (2005). [CrossRef] [PubMed]

6.

H. F. Bowman and T. A. Balasubramaniam, “A new technique utilizing thermistor probes for the measurement of thermal properties of biomaterials,” Cryobiology 13(5), 572–580 (1976). [CrossRef] [PubMed]

7.

R. Ricciardi, B. K. Schaffer, R. D. Kim, S. A. Shah, S. E. Donohue, S. M. Wheeler, S. H. Quarfordt, M. P. Callery, W. C. Meyers, and R. S. Chari, “Protective effects of ischemic preconditioning on the cold-preserved liver are tyrosine kinase dependent,” Transplantation 72(3), 406–412 (2001). [CrossRef] [PubMed]

8.

E. Klar, T. Kraus, J. Bleyl, W. H. Newman, H. F. Bowman, W. J. Hofmann, R. Kummer, M. Bredt, and C. Herfarth, “Thermodiffusion for continuous quantification of hepatic microcirculation--validation and potential in liver transplantation,” Microvasc. Res. 58(2), 156–166 (1999). [CrossRef] [PubMed]

9.

J. G. Webster, Design of Pulse Oximeters, Medical Science Series (Institute of Physics Pub., Bristol; Philadelphia, 1997), p. xvi.

10.

T. Aoyagi, “Pulse oximetry: its invention, theory, and future,” J. Anesth. 17(4), 259–266 (2003). [CrossRef] [PubMed]

11.

S. Reichelt, J. Fiala, A. Werber, K. Förster, C. Heilmann, R. Klemm, and H. Zappe, “Development of an implantable pulse oximeter,” IEEE Trans. Biomed. Eng. 55(2), 581–588 (2008). [CrossRef] [PubMed]

12.

A. C. Guyton, Textbook of Medical Physiology, 8th ed. (Saunders, Philadelphia, 1991), p. xli.

13.

C. Tziafalia, M. Vlychou, K. Tepetes, N. Kelekis, and I. V. Fezoulidis, “Echo-Doppler measurements of portal vein and hepatic artery in asymptomatic patients with hepatitis B virus and healthy adults,” J. Gastrointestin. Liver Dis. 15(4), 343–346 (2006). [PubMed]

14.

M. N. Ericson, M. A. Wilson, G. L. Coté, J. S. Baba, W. Xu, M. Bobrek, C. L. Britton, M. S. Hileman, M. R. Moore, M. S. Emery, and R. Lenarduzzi, “Implantable sensor for blood flow monitoring after transplant surgery,” Minim. Invasive Ther. Allied Technol. 13(2), 87–94 (2004). [CrossRef] [PubMed]

15.

M. N. Ericson, M. Wilson, G. Cote, C. L. Britton, W. Xu, J. Baba, M. Bobrek, M. Hileman, M. Moore, and S. Frank, “Development of an implantable oximetry-based organ perfusion sensor,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 3, 2235–2238 (2004). [PubMed]

16.

H. Subramanian, B. L. Ibey, W. J. Xu, M. A. Wilson, M. N. Ericson, and G. L. Coté, “Real-time separation of perfusion and oxygenation signals for an implantable sensor using adaptive filtering,” IEEE Trans. Biomed. Eng. 52(12), 2016–2023 (2005). [CrossRef] [PubMed]

17.

J. L. Reuss and D. Siker, “The pulse in reflectance pulse oximetry: modeling and experimental studies,” J. Clin. Monit. Comput. 18(4), 289–299 (2004). [CrossRef] [PubMed]

18.

J. L. Reuss, “Multilayer modeling of reflectance pulse oximetry,” IEEE Trans. Biomed. Eng. 52(2), 153–159 (2005). [CrossRef] [PubMed]

19.

M. Hickey and P. A. Kyriacou, “Optimal spacing between transmitting and receiving optical fibres in reflectance pulse oximetry,” J. Phys. Conf. Ser. 85, 012030 (2007). [CrossRef]

20.

J. L. Reuss, “Arterial pulsatility and the modeling of reflectance pulse oximetry,” in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2003 (IEEE, 2003), pp. 2791–2794.

21.

T. J. Akl, T. J. King, R. Long, J. S. Baba, M. J. McShane, M. N. Ericson, M. A. Wilson, and G. L. Cote, ““Optimizing source detector separation for an implantable perfusion and oxygenation sensor,”,” Proc. SPIE 7906, 790605, 790605-5 (2011). [CrossRef]

22.

L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995). [CrossRef] [PubMed]

23.

J.-P. Ritz, A. Roggan, C. Isbert, G. Müller, H. J. Buhr, and C.-T. Germer, “Optical properties of native and coagulated porcine liver tissue between 400 and 2400 nm,” Lasers Surg. Med. 29(3), 205–212 (2001). [CrossRef] [PubMed]

24.

M. Friebel, J. Helfmann, U. Netz, and M. Meinke, “Influence of oxygen saturation on the optical scattering properties of human red blood cells in the spectral range 250 to 2,000 nm,” J. Biomed. Opt. 14(3), 034001 (2009). [CrossRef] [PubMed]

25.

S. Prahl, “Optical absorption of hemoglobin” (www.omlc.org, 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/.

26.

D. J. Faber, M. C. G. Aalders, E. G. Mik, B. A. Hooper, M. J. C. van Gemert, and T. G. van Leeuwen, “Oxygen saturation-dependent absorption and scattering of blood,” Phys. Rev. Lett. 93(2), 028102 (2004). [CrossRef] [PubMed]

27.

P. J. Wang, W. C. Li, G. M. Xi, H. Q. Wang, Z. H. Zhang, B. C. Yao, W. Tang, Z. H. Deng, and X. H. Zhang, “Biomechanical study of hepatic portal vein in humans and pigs and its value in liver transplantation,” Transplant. Proc. 41(5), 1906–1910 (2009). [CrossRef] [PubMed]

28.

A. P. Avolio, “Multi-branched model of the human arterial system,” Med. Biol. Eng. Comput. 18(6), 709–718 (1980). [CrossRef] [PubMed]

29.

L. Wang, S. L. Jacques, and L. Zheng, “CONV--convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Methods Programs Biomed. 54(3), 141–150 (1997). [CrossRef] [PubMed]

30.

R. Long, T. King, T. Akl, M. N. Ericson, M. A. Wilson, G. L. Coté, and M. J. McShane, “Opto-fluidic phantom mimicking optical properties of porcine livers,” Biomed. Opt. Express 2(7), 1877–1892 (2011). [CrossRef]

31.

P. Di Ninni, F. Martelli, and G. Zaccanti, “The use of India ink in tissue-simulating phantoms,” Opt. Express 18(26), 26854–26865 (2010). [CrossRef] [PubMed]

32.

S. J. Madsen, M. S. Patterson, and B. C. Wilson, “The use of India ink as an optical absorber in tissue-simulating phantoms,” Phys. Med. Biol. 37(4), 985–993 (1992). [CrossRef] [PubMed]

33.

S. Takatani and M. D. Graham, “Theoretical analysis of diffuse reflectance from a two-layer tissue model,” IEEE Trans. Biomed. Eng. BME-26(12), 656–664 (1979). [CrossRef] [PubMed]

OCIS Codes
(170.1470) Medical optics and biotechnology : Blood or tissue constituent monitoring
(170.3660) Medical optics and biotechnology : Light propagation in tissues
(290.1990) Scattering : Diffusion
(300.6340) Spectroscopy : Spectroscopy, infrared
(280.1415) Remote sensing and sensors : Biological sensing and sensors

ToC Category:
Optics of Tissue and Turbid Media

History
Original Manuscript: May 12, 2011
Revised Manuscript: June 27, 2011
Manuscript Accepted: June 28, 2011
Published: June 29, 2011

Citation
Tony J. Akl, Ruiqi Long, Michael J. McShane, M. Nance Ericson, Mark A. Wilson, and Gerard L. Coté, "Optimizing probe design for an implantable perfusion and oxygenation sensor," Biomed. Opt. Express 2, 2096-2109 (2011)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-2-8-2096


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References

  1. U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients, “2009 OPTN/SRTR Annual Report: Transplant Data 1999-2008,” U.S. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD.
  2. A. Humeau, W. Steenbergen, H. Nilsson, and T. Strömberg, “Laser Doppler perfusion monitoring and imaging: novel approaches,” Med. Biol. Eng. Comput. 45(5), 421–435 (2007). [CrossRef] [PubMed]
  3. T. Kok, M. J. H. Slooff, C. J. P. Thijn, P. M. J. G. Peeters, R. Verwer, C. M. A. Bijleveld, A. P. van den Berg, E. B. Haagsma, and I. J. Klompmaker, “Routine Doppler ultrasound for the detection of clinically unsuspected vascular complications in the early postoperative phase after orthotopic liver transplantation,” Transpl. Int. 11(4), 272–276 (1998). [CrossRef] [PubMed]
  4. P. Vajkoczy, H. Roth, P. Horn, T. Lucke, C. Thomé, U. Hubner, G. T. Martin, C. Zappletal, E. Klar, L. Schilling, and P. Schmiedek, “Continuous monitoring of regional cerebral blood flow: experimental and clinical validation of a novel thermal diffusion microprobe,” J. Neurosurg. 93(2), 265–274 (2000). [CrossRef] [PubMed]
  5. M. B. Khot, P. K. Maitz, B. R. Phillips, H. F. Bowman, J. J. Pribaz, and D. P. Orgill, “Thermal diffusion probe analysis of perfusion changes in vascular occlusions of rabbit pedicle flaps,” Plast. Reconstr. Surg. 115(4), 1103–1109 (2005). [CrossRef] [PubMed]
  6. H. F. Bowman and T. A. Balasubramaniam, “A new technique utilizing thermistor probes for the measurement of thermal properties of biomaterials,” Cryobiology 13(5), 572–580 (1976). [CrossRef] [PubMed]
  7. R. Ricciardi, B. K. Schaffer, R. D. Kim, S. A. Shah, S. E. Donohue, S. M. Wheeler, S. H. Quarfordt, M. P. Callery, W. C. Meyers, and R. S. Chari, “Protective effects of ischemic preconditioning on the cold-preserved liver are tyrosine kinase dependent,” Transplantation 72(3), 406–412 (2001). [CrossRef] [PubMed]
  8. E. Klar, T. Kraus, J. Bleyl, W. H. Newman, H. F. Bowman, W. J. Hofmann, R. Kummer, M. Bredt, and C. Herfarth, “Thermodiffusion for continuous quantification of hepatic microcirculation--validation and potential in liver transplantation,” Microvasc. Res. 58(2), 156–166 (1999). [CrossRef] [PubMed]
  9. J. G. Webster, Design of Pulse Oximeters, Medical Science Series (Institute of Physics Pub., Bristol; Philadelphia, 1997), p. xvi.
  10. T. Aoyagi, “Pulse oximetry: its invention, theory, and future,” J. Anesth. 17(4), 259–266 (2003). [CrossRef] [PubMed]
  11. S. Reichelt, J. Fiala, A. Werber, K. Förster, C. Heilmann, R. Klemm, and H. Zappe, “Development of an implantable pulse oximeter,” IEEE Trans. Biomed. Eng. 55(2), 581–588 (2008). [CrossRef] [PubMed]
  12. A. C. Guyton, Textbook of Medical Physiology, 8th ed. (Saunders, Philadelphia, 1991), p. xli.
  13. C. Tziafalia, M. Vlychou, K. Tepetes, N. Kelekis, and I. V. Fezoulidis, “Echo-Doppler measurements of portal vein and hepatic artery in asymptomatic patients with hepatitis B virus and healthy adults,” J. Gastrointestin. Liver Dis. 15(4), 343–346 (2006). [PubMed]
  14. M. N. Ericson, M. A. Wilson, G. L. Coté, J. S. Baba, W. Xu, M. Bobrek, C. L. Britton, M. S. Hileman, M. R. Moore, M. S. Emery, and R. Lenarduzzi, “Implantable sensor for blood flow monitoring after transplant surgery,” Minim. Invasive Ther. Allied Technol. 13(2), 87–94 (2004). [CrossRef] [PubMed]
  15. M. N. Ericson, M. Wilson, G. Cote, C. L. Britton, W. Xu, J. Baba, M. Bobrek, M. Hileman, M. Moore, and S. Frank, “Development of an implantable oximetry-based organ perfusion sensor,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 3, 2235–2238 (2004). [PubMed]
  16. H. Subramanian, B. L. Ibey, W. J. Xu, M. A. Wilson, M. N. Ericson, and G. L. Coté, “Real-time separation of perfusion and oxygenation signals for an implantable sensor using adaptive filtering,” IEEE Trans. Biomed. Eng. 52(12), 2016–2023 (2005). [CrossRef] [PubMed]
  17. J. L. Reuss and D. Siker, “The pulse in reflectance pulse oximetry: modeling and experimental studies,” J. Clin. Monit. Comput. 18(4), 289–299 (2004). [CrossRef] [PubMed]
  18. J. L. Reuss, “Multilayer modeling of reflectance pulse oximetry,” IEEE Trans. Biomed. Eng. 52(2), 153–159 (2005). [CrossRef] [PubMed]
  19. M. Hickey and P. A. Kyriacou, “Optimal spacing between transmitting and receiving optical fibres in reflectance pulse oximetry,” J. Phys. Conf. Ser. 85, 012030 (2007). [CrossRef]
  20. J. L. Reuss, “Arterial pulsatility and the modeling of reflectance pulse oximetry,” in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2003 (IEEE, 2003), pp. 2791–2794.
  21. T. J. Akl, T. J. King, R. Long, J. S. Baba, M. J. McShane, M. N. Ericson, M. A. Wilson, and G. L. Cote, ““Optimizing source detector separation for an implantable perfusion and oxygenation sensor,”,” Proc. SPIE 7906, 790605, 790605-5 (2011). [CrossRef]
  22. L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995). [CrossRef] [PubMed]
  23. J.-P. Ritz, A. Roggan, C. Isbert, G. Müller, H. J. Buhr, and C.-T. Germer, “Optical properties of native and coagulated porcine liver tissue between 400 and 2400 nm,” Lasers Surg. Med. 29(3), 205–212 (2001). [CrossRef] [PubMed]
  24. M. Friebel, J. Helfmann, U. Netz, and M. Meinke, “Influence of oxygen saturation on the optical scattering properties of human red blood cells in the spectral range 250 to 2,000 nm,” J. Biomed. Opt. 14(3), 034001 (2009). [CrossRef] [PubMed]
  25. S. Prahl, “Optical absorption of hemoglobin” ( www.omlc.org , 1999), retrieved January 20, 2011, http://omlc.ogi.edu/spectra/hemoglobin/ .
  26. D. J. Faber, M. C. G. Aalders, E. G. Mik, B. A. Hooper, M. J. C. van Gemert, and T. G. van Leeuwen, “Oxygen saturation-dependent absorption and scattering of blood,” Phys. Rev. Lett. 93(2), 028102 (2004). [CrossRef] [PubMed]
  27. P. J. Wang, W. C. Li, G. M. Xi, H. Q. Wang, Z. H. Zhang, B. C. Yao, W. Tang, Z. H. Deng, and X. H. Zhang, “Biomechanical study of hepatic portal vein in humans and pigs and its value in liver transplantation,” Transplant. Proc. 41(5), 1906–1910 (2009). [CrossRef] [PubMed]
  28. A. P. Avolio, “Multi-branched model of the human arterial system,” Med. Biol. Eng. Comput. 18(6), 709–718 (1980). [CrossRef] [PubMed]
  29. L. Wang, S. L. Jacques, and L. Zheng, “CONV--convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Methods Programs Biomed. 54(3), 141–150 (1997). [CrossRef] [PubMed]
  30. R. Long, T. King, T. Akl, M. N. Ericson, M. A. Wilson, G. L. Coté, and M. J. McShane, “Opto-fluidic phantom mimicking optical properties of porcine livers,” Biomed. Opt. Express 2(7), 1877–1892 (2011). [CrossRef]
  31. P. Di Ninni, F. Martelli, and G. Zaccanti, “The use of India ink in tissue-simulating phantoms,” Opt. Express 18(26), 26854–26865 (2010). [CrossRef] [PubMed]
  32. S. J. Madsen, M. S. Patterson, and B. C. Wilson, “The use of India ink as an optical absorber in tissue-simulating phantoms,” Phys. Med. Biol. 37(4), 985–993 (1992). [CrossRef] [PubMed]
  33. S. Takatani and M. D. Graham, “Theoretical analysis of diffuse reflectance from a two-layer tissue model,” IEEE Trans. Biomed. Eng. BME-26(12), 656–664 (1979). [CrossRef] [PubMed]

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