Development of a Tissue Oxygen Saturation Detection System for Improving Surgical Training

Transcription

1 Development of a Tissue Oxygen Saturation Detection System for Improving Surgical Training by Kunj Bhaveshkumar Upadhyaya A thesis submitted in conformity with the requirements for the degree of Master of Health Science in Clinical Engineering Institute of Biomaterials and Biomedical Engineering (IBBME) University of Toronto Copyright by Kunj Bhaveshkumar Upadhyaya 2016

2 Development of a Tissue Oxygen Saturation Detection System for Improving Surgical Training Abstract Kunj Upadhyaya Master of Health Science in Clinical Engineering Institute of Biomaterials and Biomedical Engineering (IBBME) University of Toronto 2016 Delicate tissue encountered in surgery is prone to ischemic damage from grasping and retracting especially by novice surgeons. Currently, there are no existing techniques to quantitatively assess tissue health during surgical maneuver. A transmission and reflectance mode tissue oxygenation (StO2) sensor was developed and integrated into a standard laparoscopic tool and custom forceps to continuously measure tissue oxygenation during surgery. Numerous wavelengths including 470nm, 500nm, 510nm, 560nm, 570nm, 586nm, 660nm and 940nm were tested in reflection mode while 660nm and 940nm were tested in transmission mode. StO2 sensor successfully detected oxygenation changes on the finger and during ex vivo experiment conducted on arterial and venous blood samples. StO2 sensor was unable to monitor changes when grasping small intestine and liver using surgical instruments. Various factors including lack of hemoglobin at the site of measurement, tissue thickness changes during grasps, and motion artifacts limited the use of this technology. ii

3 Acknowledgments First and foremost, I would like to thank my supervisors Dr.Farhat and Dr.Drake for giving me an opportunity to work on this project. I would like to thank my committee members Dr.Koyle, Dr.Finelli, Dr.Genov and Dr.Naguib for their guidance. Thank you Thomas for letting me bounce ideas off you when I needed to. Thank you Rob for patiently showing me how to use lab equipment. Justin thank you for helping me with FPCB. I appreciated all the feedback and motivation I received from the lab members. iii

4 Table of Contents Acknowledgments... iii Table of Contents... iv List of Tables... vii List of Figures... viii List of Abbreviations... xiii Introduction Surgical Techniques Open Surgery Conventional Laparoscopy Robotic Surgery using da Vinci Surgical System Research Motivation...4 Background Tissue Oxygen Saturation Optical Properties of Tissue Beer-Lambert Law Existing Techniques to Monitor StO Blood Gas Analyzers Transcutaneous Oximetry Near-Infrared Spectroscopy Visible Light Spectroscopy Pulse-Oximetry like Technique for measuring StO Three Charged-Coupled Device (CCD) Camera Comparing Techniques...14 iv

5 2.6 Research Goal Research Objectives Constraints...16 Transmission Mode Sensor Design StO2 Sensor Design Optoelectronic Selection (LEDs and Photodiode) Integrating Sensor with Laparoscopic Tools Circuit Design Algorithm nm and 940nm in Transmission Mode Testing In vivo Experiment 1 - Proof of Concept In vivo Experiment 2 Grasping Bowel In vivo Experiment 3 Measuring Beside Tissue In vivo Experiment 4 Occluding Arterial Supply In vivo Experiment 5 Grasped Finger nm and 940nm in Transmission Mode - Discussion...31 Exploring Visible Spectrum Visible Light Advantages Wavelength Selection for Reflectance Measurement Design of Reflectance StO2 Sensor Design In vivo Experiment 6 Reflection Mode Test protocol Theoretically Expected Results In vivo Experiment 6 Reflection Mode Results In vivo Experiment 6 Reflection Mode Discussion...48 Controlled Volume Experiments Ex vivo Experiment 1 - Controlled Volume Method...49 v

6 5.2 Ex vivo Experiment 1 Controlled Volume Results Ex vivo Experiment 1 Discussion...57 Conclusion...59 Future Direction Determining Resolution Alternatives to Measuring Hemoglobin with Graspers...60 References...62 vi

7 List of Tables Table 1: Area under relative output intensity versus wavelength curve for HbO2, Hb and expected changes in signal with decrease in oxygenation Table 2: Comparing theoretical and experimental results for reflection mode experiment Table 3: Comparing theoretical versus experimental results for controlled volume experiment. 56 Table 4: Comparing percentage differences in between reflection mode testing and controlled volume testing vii

8 List of Figures Figure 1: Open surgery showing a large incision. Surgeon is able to interact with tissues with their hand... 1 Figure 2: Conventional laparoscopic schematic showing three ports for surgical instrument and one port for laparoscope [6]... 2 Figure 3: da Vinci Surgical System. The three main components of the system: surgeon s console, patient side cart and vision cart. Surgeon sits at the surgical console, has 3D view of the surgical environment and operates on the patient [12] Figure 4: Schematic of oxygenated and deoxygenated hemoglobin Figure 5: Absorption coefficient spectra of tissue chromophores. HbO2 - red line, Hb blue line, water (80% by tissue volume) black line, lipid (20% by tissue volume) brown line, lipid pink line, melanin black dashed line, collagen green line and elastin yellow line [31]... 7 Figure 6: Molar extinction coefficient of oxygenated and deoxygenated hemoglobin within nm. Figure recreated using data compiled by Scott Prahl [33]... 9 Figure 7: Depth of penetration of oxygenated and deoxygenated hemoglobin within nm assuming 150g/L hemoglobin concentration and g/mol molar mass of hemoglobin Figure 8: Schematic of NIRS where light source and detector are typically placed 4-8cm apart to ensure the light penetrates through the skull. Light travels through banana-shaped region Figure 9: Comparing junction size of different LEDs Figure 10: LEDs and photodiodes will be integrated in the cut-out to ensure the tool-functionality is not compromised Figure 11: LEDs and photodiode integrated with FPCB Figure 12: Tissue oxygen sensor (comprised of LEDs, photodiode, FPCB, and microcontroller) integrated with standard bowel grasper Figure 13: Custom forceps with tissue oxygenation sensor integrated viii

9 Figure 14: Voltage follower circuit to drive LED Figure 15: Trans-impedance amplifier with 11x gain Figure 16: Blood pressure cuff placed on the forearm and custom tool placed on index finger to measure oxygenation changes on the finger as forearm is occluded Figure 17: Monitoring response of red and near-ir signal at index finger after placing blood pressure cuff on the forearm and slowly increasing cuff pressure. Forearm was occluded at 15 seconds and released at 105 seconds Figure 18: Grasping small intestine with sensorized laparoscopic grasper Figure 19: Monitoring red and near-ir signal after grasping with low forces. Tissue was grasped at 30 seconds and release at 90 seconds Figure 20: Monitoring red and near-ir signal after grasping with high forces. Tissue was grasped at 15 seconds and released at 75 seconds Figure 21: Blood displacement after grasping bowel (left) and liver (right) with low forces Figure 22: Monitoring tissue oxygenation changes beside the site of grasping. Liver is clamped with ratcheted forceps and tissue oxygenation is measured with custom forceps Figure 23: Monitoring response of red and near-ir light beside the site of grasping. Tissue was grasped at 20 seconds and released at 120 seconds Figure 24: Clamping mesenteric arteries with bulldog clamp to cut-off blood supply to the bowel Figure 25: Measuring red and near-ir signal with custom forceps after cutting off mesenteric artery. Arteries were clamped at 30 seconds Figure 26: Grasping finger with custom forceps and measuring changes at the site of grasping. 30 Figure 27: Monitoring response of red and near-ir light after grasping finger with custom forceps ix

10 Figure 28: Molar Extinction Coefficient of oxygenated and deoxygenated hemoglobin within visible light range. Black lines indicate wavelengths with the largest difference between molar extinction coefficient of oxygenated and deoxygenated hemoglobin. Yellow lines indicated wavelengths with identical coefficients of oxygenated and deoxygenated hemoglobin Figure 29: Illustration of transmittance (left) and reflectance (right). In transmission mode, source and detector are placed across each other. In reflection mode, source and detector are placed beside each other Figure 30: Reflectance set-up showing LEDs and photodiodes placed beside each other Figure 31: Reflectance set-up showing LED and photodiode with the jig to reduce unnecessary photodiode and LED interference Figure 32: Comparison of baseline reading with and without jig Figure 33: Relative intensity versus wavelength for peak wavelength of 500nm approximated. 38 Figure 34: Overlaying 500nm peak LED spectral distribution with molar extinction coefficient of hemoglobin curve Figure 35: Relative output intensity versus wavelength Figure 36: Photodiode spectral sensitivity as function of wavelength Figure 37: Relative output intensity versus wavelength adjusted for LED and photodiode intensity Figure 38: Monitoring response of LED with peak intensity at 470nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 39: Monitoring response of LED with peak intensity at 500nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 40: Monitoring response of LED with peak intensity at 510nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds x

11 Figure 41: Monitoring response of LED with peak intensity at 560nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 42: Monitoring response of LED with peak intensity at 570nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 43: Monitoring response of LED with peak intensity at 586nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 44: Monitoring response of LED with peak intensity at 660nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 45: Monitoring response of LED with peak intensity at 940nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds Figure 46: Molar extinction coefficient versus wavelength data from Takatani et al. overlaid onto best data available [45] Figure 47: Arterial blood (75-95% oxygenation) and venous blood (5-15% oxygenation) in a 1 ml syringe Figure 48: Syringe holder set-up Figure 49: Comparing photodiode response from arterial blood and venous blood with 470nm peak LED Figure 50: Comparing photodiode response from arterial blood and venous blood with 500nm peak LED Figure 51: Comparing photodiode response from arterial blood and venous blood with 510nm peak LED Figure 52: Comparing photodiode response from arterial blood and venous blood with 560nm peak LED Figure 53: Comparing photodiode response from arterial blood and venous blood with 570nm peak LED xi

12 Figure 54: Comparing photodiode response from arterial blood and venous blood with 586nm peak LED Figure 55: Comparing photodiode response from arterial blood and venous blood with 660nm peak LED Figure 56: Comparing photodiode response from arterial blood and venous blood with 940nm peak LED xii

13 List of Abbreviations 3D Three-dimensional CCD Charged-Coupled Device CL Conventional Laparoscopy DOF Degrees of Freedom FPCB Flexible Printed Circuit Board HbO2 Oxygenated Hemoglobin Hb Deoxygenated Hemoglobin LED Light Emitting Diode MIS Minimally Invasive Surgery NADH Nicotinamide Adenine Dinucleotide NIRS Near Infrared Spectroscopy Near-IR Near-Infrared OR Operating Room PaO2 Partial Pressure of Oxygen in Arterial Blood RAL Robotic Assisted Laparoscopy SNR Signal-to-Noise Ratio StO2 Tissue Oxygenation Saturation SaO2 Arterial Tissue Oxygenation SpO2 Peripheral Oxygenation Saturation xiii

14 UV Ultra-Violet VLS Visible Light Spectroscopy xiv

15 Introduction 1.1 Surgical Techniques Open Surgery Traditionally, surgeries have been performed within large open incisions where hand-held surgical instruments are used to complete various surgical tasks. In open surgery, the anatomy of interest is directly under the surgeon s view. In addition, surgeons are able to touch and feel the tissues of interest with their hands [1]. Due to the immediate tactile feedback they receive when interacting with tissues and organs, surgeons are able to apply proper forces and are less likely to damage tissues. Direct view of the surgical environment also allows tissue health to be easily assessed [2]. Figure 1: Open surgery showing a large incision. Surgeon is able to interact with tissues with their hand Despite these advantages, open surgery has many shortcomings. Open surgery is associated with increased hospital stay, increased post-operative pain, and increased risk of infection due to the larger incision [3]. Due to the invasive nature of open surgery, patients lose more blood and take a longer time to return to daily activities. In addition, patients will have aesthetically displeasing scarring for the remainder of their lives [4]. 1

16 Conventional Laparoscopy Minimally invasive surgery (MIS) was developed to address the limitations of open surgery. Conventional laparoscopy (CL) is a type of MIS performed using laparoscopic instruments [1]. In CL, the surgeon typically makes three or four small incisions, usually 5-10mm in length, in the abdominal wall. Surgeons insert two or three long, rigid instruments and a laparoscope through these incisions as shown in Figure 2. The laparoscope provides a two-dimensional magnified view of the surgical environment [5]. Development of MIS has resulted in reduced postoperative discomfort, decreased hospital stay, reduced anesthesia time, and improved cosmetic results due to the smaller incisions. After laparoscopic surgeries, patients are able to return to their daily activities sooner. The exposure of internal organs to the external environment is reduced which lowers the chances of acquiring an infection [4]. Figure 2: Conventional laparoscopic schematic showing three ports for surgical instrument and one port for laparoscope [6] There are various limitations to CL surgery. Surgeons working with CL instruments have a range of motion that is limited to four degrees of freedom (DOF). CL instruments are essentially a long rod with one joint, providing very little dexterity in comparison to open surgery. Natural tremors, experienced by even skilled surgeons, are amplified at the tip of the instrument due to the rigidness of the tools. Due to the fulcrum effect, the instrument tips move in the opposite direction to those of the surgeon s hands creating counterintuitive movements. CL requires nonergonomic positions for the surgeons, which can lead to physical and mental fatigue. The use of

17 3 laparoscopic instruments has also been associated with nerve injuries for the surgeons. Loss of three-dimensional (3D) vision results in the loss of hand-eye coordination and depth perception as the surgeon must rely solely on the 2D image on the screen [7] Robotic Surgery using da Vinci Surgical System Minimally invasive surgeries performed using surgical robots are known as robotic assisted laparoscopy (RAL). The da Vinci Surgical System integrates 3D endoscopy, robotic technology, and intuitive motion control allowing the surgeons to perform surgeries while sitting at an ergonomically designed workstation [8]. In RAL, the surgeon controls three robotic arms: two arms hold surgical instruments and one arm holds an endoscopic camera offering 3D view of the surgical field. The motions of the surgeon s hands on the master controller are translated to the motion of the robotic tools inside the patients [9]. Endo-wrist instruments provide seven degrees of freedom (DOF), which greatly improves tip dexterity and range of motion, offering movement comparable to that of the human wrist [10]. MIS performed using robotic surgical systems overcome the challenges of CL by offering 3D visualization, increased DOF, intuitive movements, tremor filtering, motion scaling, optical magnification, and ergonomic positioning for the surgeon [11]. Figure 3: da Vinci Surgical System. The three main components of the system: surgeon s console, patient side cart and vision cart. Surgeon sits at the surgical console, has 3D view of the surgical environment and operates on the patient [12].

18 4 1.2 Research Motivation MIS performed with CL or RAL offer many advantages over open surgery; however, surgeons lose the ability to accurately judge forces increasing their chances of applying excessive forces. This lack of feedback has been shown to increase errors causing tissue damage by a factor of three [13]. It has been shown that even experienced surgeons training with robot assisted surgery often damage delicate tissues [9]. The grasper can potentially damage abdominal organs such as the liver, bowels, and ureter. Moreover, stress injuries from the grasper may not be detected until the patient has been discharged from the operating room (OR) since surgeons lose their ability to assess tissue health through palpation when performing MIS. These injuries may result in pathological scar tissue formation, bleeding, adhesions, the loss of bowel mobility, and increased risk of infection [7]. Pediatric tissues are more likely to be delicate; hence they are at a greater risk of being damaged during surgery. Many researchers have developed force feedback and haptic perception systems to assist with force measurement [13] [21]. Monitoring forces applied to tissue is not an ideal assessment tool for tissue health as it is an indirect assessment; measuring tissue oxygenation provides a much better alternative. Surgeons may damage tissue not only due to the application of excessive forces but also from grasping the tissues for long durations or in a manner that would impact normal blood circulation to the surrounding tissue. Heijnsdijk et al. performed an analysis of colectomies and cholecystectomies performed laparoscopically by experienced surgeons and residents with less than one year of experience. In colectomies, the colon was clamped for longer than three minutes on three occasions per surgery with maximum grasping duration of seven minutes. In cholecystectomy, the gall bladder was clamped for longer than three minutes on three occasions per surgery with maximum grasping duration of 55 minutes. Residents with less than one year of experience used significantly more actions with a grasping time of more than three minutes compared to expert surgeons (4.8 versus 2.7; p = 0.010) [22]. Reduction in blood flow due to long duration of grasps may increase the chances of tissue ischemia, which results from an imbalance of oxygen supply and demand at the cellular level. This can ultimately lead to cellular or organ damage if it is maintained overtime [23], [24].

19 5 Assessing tissue health qualitatively requires years of practice and cannot easily be taught to an inexperienced surgeon. By measuring tissue health in real-time, it may be possible to limit tissue damage by alerting the surgeon in time. Background 2.1 Tissue Oxygen Saturation Red blood cells contain a protein called hemoglobin which binds to oxygen in the lungs to form oxygenated hemoglobin (HbO2), as shown in Figure 4 [25]. Oxygenated hemoglobin is responsible for supplying the cells with adequate oxygen required for survival. After the cells use HbO2 for metabolism, it is converted into deoxygenated hemoglobin (Hb). Proper circulation of blood is required throughout the body to ensure adequate oxygen is supplied to cells for survival; when the body is deprived of oxygen, it can lead to cell death, organ failure, or even death. Oxygenated Hemoglobin Deoxygenated Hemoglobin Figure 4: Schematic of oxygenated and deoxygenated hemoglobin. Tissue oxygen saturation provides an indication of tissue health; low StO2 maintained overtime can lead ischemic damage. StO2 measures the local oxygen saturation in the microcirculation where oxygen is exchanged, wherein microcirculation consists of arterioles, capillaries and venules. StO2 measurement provides a weighted average of oxygen saturation in the microvessels. For example, in the brain cortex, 60-80% of the blood is from venules, 15-20% from arterioles and the remaining blood is from capillaries. StO2 is defined as the ratio between HbO2 to total hemoglobin, where total hemoglobin is comprised of HbO2 and Hb [26]. (1) Tissue Oxygen Saturation (StO 2 ) = HbO 2 HbO 2 +Hb StO2 differs from the peripheral oxygenation saturation (SpO2) measurement, which is an estimate of arterial oxygen saturation (SaO2) and is measured by a pulse oximeter [27]. SpO2 provides a good indication of lung function, but fails to indicate the oxygen uptake at the level of

20 6 tissue or organs. Local changes in StO2 are unlikely to affect systemic circulation measurements provided by pulse oximeters. In surgery, it is important to assess tissue health at the site where surgical maneuvers are being performed. Even with normal pulse oximeter readings, there is possibility of local damage that can impact patient health and lead to adverse outcomes. For example, pulse oximeter reading may be normal despite drastic local changes in the oxygenation of the small intestine. Furthermore, SpO2 measurements require pulsatile arterial flow for reliable measurements [28]. This condition becomes difficult to maintain as many surgical maneuvers require the tissue to be grasped, thus potentially compromising pulsatile blood flow or, conversely, the site of grasping may only have non-pulsatile venous or capillary blood. SpO2 measurement is not a feasible mode of assessing tissue health intra-operatively. By measuring local StO2, it is possible to quantitatively assess tissue health at the site of surgical maneuvers. 2.2 Optical Properties of Tissue Optical properties of biological tissue can be exploited to determine tissue composition. Applied light beam interacts with multiple tissue components which include water, lipids, melanin, HbO2, Hb, collagen and elastin. Absorption of light depends on chromophores because each chromophore has an absorption spectrum in which the extinction coefficient is dependent on the wavelength. Figure 5 shows the wavelength dependent absorption coefficient for absorbers including water, lipids, melanin, HbO2, Hb, collagen and elastin [29]. Water, lipids, elastin, collagen and melanin will show constant absorption effects for a given tissue, while absorption effects due to concentration of HbO2 and Hb will change with changes in oxygen. Contributions from water, lipids, elastin and collagen on total attenuation in the region of interest ( nm) can be considered to be negligible because these chromophores have significantly lower extinction coefficients compared to HbO2 and Hb. Melanin is typically found in skin; hence it is unlikely to affect measurement taken in other soft-tissues. Therefore, changes in absorbance will only be due to changes in concentration of HbO2 and Hb. HbO2 and Hb have distinct optical properties because HbO2 differs in parts of its absorption pattern from Hb [30].

21 7 Figure 5: Absorption coefficient spectra of tissue chromophores. HbO2 - red line, Hb blue line, water (80% by tissue volume) black line, lipid (20% by tissue volume) brown line, lipid pink line, melanin black dashed line, collagen green line and elastin yellow line [31] 2.3 Beer-Lambert Law To determine StO2 the concentrations of HbO2 and Hb need to be known. Optical properties of tissues are exploited to calculate concentration of Hb and HbO2. As seen in Figure 6, the molar extinction coefficient of HbO2 and Hb is significantly dependent on the wavelength. Light attenuation is dependent on the molar absorption coefficient according to the Beer-Lambert Law. Beer-Lambert law states that the intensity of transmitted light (I) through a solution can be determined given that the intensity of incident light (Io), optical path length (d), concentration of substance (c), and wavelength dependent extinction coefficient (ϵ(λ)) of the substance are known [32]. The absorbance (A) of the light is given as (2) I = I o 10 ε(λ)cd (3) A = log ( I I o ) = ε(λ)cd

22 8 In the presence of multiple absorbers, the total absorption is given by the superposition of absorption from each specie [31]. To determine StO2, the concentrations of HbO2 and Hb are of interest. By measuring the light transmitted through the tissue of interest at two different wavelengths, StO2 can be determined using Equation 4 and Equation 5. (4) A λ 1 (5) A λ 2 = log ( I I o ) λ 1 = ε HbO2 (λ 1 )c HbO2 d + ε Hb (λ 1 )c Hb d = log ( I I o ) λ 2 = ε HbO2 (λ 2 )c HbO2 d + ε Hb (λ 2 )c Hb d By selecting two unique wavelengths where extinction coefficient of HbO2 is higher than extinction coefficient of Hb and another where the relationship is vice versa (e.g. 660nm and 940nm or 435nm and 470nm), it is possible to monitor changes in tissue oxygenation using the Beer-Lambert law. Additional wavelengths can be selected for determining concentrations of additional species. It is important to select wavelengths on either side of the isosbestic point (crossover point where HbO2 and Hb have identical extinction coefficient) to ensure changes are due to changes in concentration of HbO2 and Hb rather than factors such as lose connection or changes in blood volume. Concentration of Hb and HbO2 (c Hb and c HbO2 ) can be determined since the wavelegnth dependent molar extinction coefficients (ϵ HbO2 (λ 1 ), ϵ Hb (λ 1 ), ϵ HbO2 (λ 2 ) and ϵ Hb (λ 2 )) are known, intensity of incident light (I o and I λ 1 o ) can be obtained from datasheet for the light λ 2 source and incident of trasnmitted light can be measured using photodiode (I λ1 and I λ2 ). For this equation, it has to be assumed the optical distance travelled by two different wavlegnths is identical. Beer-Lambert law does not take scattering into account hence it cannot be used strictly to determine StO2. A calibration technique needs to be developed to determine absolute tissue oxygen saturation.

23 9 Figure 6: Molar extinction coefficient of oxygenated and deoxygenated hemoglobin within nm. Figure recreated using data compiled by Scott Prahl [33] 2.4 Existing Techniques to Monitor StO Blood Gas Analyzers Blood gas analyzers are considered to be the gold standard for measuring blood gas composition. They can provide information about ph, partial pressure of carbon dioxide, partial pressure of oxygen, and oxygen saturation. Blood gas analysis requires blood to be extracted from large vessels for measurement. Large vessels need to intubated to obtain blood samples for blood gas analysis making it unfeasible to perform continuous measurement. Furthermore, oxygen saturation from large vessels does not provide an indication of StO2 [26] Transcutaneous Oximetry Transcutaneous oximetry (tcpo 2) is a local, non-invasive technique for measuring partial pressure of oxygen at the skin surface. It is typically used for wound evaluation, plastic surgery, amputation level determination, and peripheral vascular assessment. It provides information about supply and delivery of oxygen to the microcirculation beneath the epidermis which

24 10 directly affects tissue perfusion. It is able to provide continuous information about the body s ability to deliver oxygen to the tissue [34],[35]. Normally, the measured po2 is close to zero on the skin surface. When an electrode heats the underlying tissue to approximately degree Celsius, vasodilation intensifies the blood perfusion and increasing the local oxygen pressure close to that of partial pressure of oxygen in arterial blood (PaO2). When blood flow is adequate, tcpo2 measurements follow the arterial oxygen level. When blood flow is inadequate, but the PaO2 is normal, the tcpo2 measurements follow blood flow. When PaO2 and blood flow are affected, tcpo2 measurements follow tissue oxygen consumption [35]. TcpO2 measurements requires skin surface preparation which include skin to be shaved and removal of top layer of epidermis to remove cornified cells to reduce the diffusion resistance. It also requires an electrode to be placed on the chest to provide as a central reference point, while additional electrodes are placed the site to be monitored. It takes about 10 to 20 minutes for the skin to warm to the required temperature. Furthermore, it is not recommended to monitor using this technique to longer than 6 hours [35] Near-Infrared Spectroscopy Near infrared spectroscopy (NIRS) is a non-invasive light based technique to measure StO2. NIRS is typically used to measure StO2 of the brain because near-ir beam is able to penetrate bones which is required for transcranial cerebral oximetry [30]. NIRS uses a wavelength of approximately 700nm to 1000nm which penetrate deeply due to transparency of biologic tissue to near-ir spectrum. From Figure 7, it can be seen that light between nm has high penetration depth; penetration depth is defined as depth at which light intensity is reduced to about 37% of the original intensity. The propagation of light through tissue depends on reflection, absorption and scattering; reflection is determined by angle of the light beam relatively to tissue surface and scattering is deviation of light beam from straight trajectory due to particulate matter in the sample. Accordingly, NIRS measurements can be made in two ways: 1) Transmission mode - light source and light detector are placed across each other. 2) Reflection mode - where light source and detector are placed in an angular arrangement on the same side. Use of transmission mode is limited to infants where it is feasible to place light source and

25 11 detector across each other without losing signal intensity; reflection mode can be applied more generally on any individual [29]. Figure 7: Depth of penetration of oxygenated and deoxygenated hemoglobin within nm assuming 150g/L hemoglobin concentration and g/mol molar mass of hemoglobin Due to the low extinction coefficient, near-ir light travels a few centimeters through tissue [36]. If the source-detector are placed too close to each other, the detector will not detect enough light since NIRS requires the long tissue paths to generate a reliable and measureable absorption [37]. Also, it is important to place the source and detector at an adequate distance to achieve desired depth of penetration. For example, if the source and detector are placed too close together in cerebral oximetry, extra-cerebral tissue will contribute to light attenuation rather than cerebral tissue which is of interest. There exists a proportional relationship between source-detector distance and depth from which majority of light is received: depth of penetration is approximately 1/3 of the distance between source and detector (e.g. if the source and detector are

26 12 3 cm apart, the penetration depth from which majority of signal is received is 1 cm); the region that light passes through is assumed to be banana shaped [38]. There are various types of NIRS monitors available including continuous wave, frequency domain, time resolved and spatially-resolved spectroscopy. Most research and clinical systems use continuous wave NIRS instruments to calculate changes in chromophore concentration. Commercial systems typically use four wavelengths to resolve for concentration of four chromophores to improve the accuracy of StO2 measurements. Most systems use semiconductor laser diodes as light source and photomultipliers or silicon photodiodes for light detection with typical distance of greater than 2.5cm between source and detector [29]. Figure 8: Schematic of NIRS where light source and detector are typically placed 4-8cm apart to ensure the light penetrates through the skull. Light travels through bananashaped region Visible Light Spectroscopy Visible light spectroscopy (VLS) uses visible light between nm to monitor microvascular StO2 in thin and small tissue volumes. Light within nm has shallow penetration depth due to high extinction coefficient of HbO2 and Hb in this range. This limits the usage of VLS oximeters to measurements few millimeters from the surface to sites such as buccal mucosa, gastrointestinal mucosa, esophagus, and skin surface monitoring. VLS tissue oximeters measure oxygen saturation in volumes that are typically 125μL or less compared to 30mL or more measured by NIRS [37], [39]. Due to the shallow penetration depth, VLS usage is limited to reflection mode (i.e. it is not feasible to measure in transmission mode). In reflection mode using VLS, light source and detector need to be placed within few millimeters of each other for obtaining measurements.

27 13 White light source is used for illuminating the tissue and a fiber optic bundle transmits information back to the monitor for real-time results. Two types of light sources are typically used for VLS: 1) High-intensity fiber-coupled halogen light source or 2) White light LED source for integration directly into probe for low transmission losses. Custom charged-coupled device (CCD) spectrophotometers, sensitive to visible light, are used for detecting the light; spectrophotometers are capable of measuring light intensities as a function of wavelength. Sensing components for VLS have been integrated into 6x200nm hand-held wand, 2mm endoscopic catheter, clip-on buccal probe, 27-Gauge needle probe, 5 mm diameter esophageal monitoring catheter, and 12mm diameter flexible colonic probe for monitoring tissue oxygenation at various sites [37] Pulse-Oximetry like Technique for measuring StO2 Pulse oximeters are commonly used to non-invasively measure peripheral oxygenation (SpO2). Two wavelengths of light, typically in the red and near-ir region are passed through the tissue. This allows measurements in tissue thicknesses ranging from 8-25mm [40]. HbO2 absorbs more infrared and allows more red light to pass through, and Hb absorbs more red light and allows more infrared light to pass through making it possible to detect changes in oxygenation saturation by monitoring the trends. Most commonly, pulse oximeter works on transmission based technique where the light source and detector are located opposite of each other. Transmitted light from the source is detected at the photodiode after being attenuated by the tissue. Pulse oximeters apply signal processing algorithms to filter out DC component of the signal from capillaries, veins and tissues measuring only the pulsating arterial contribution. It has been shown that pulse-oximeter like technique can be used to measure tissue oxygenation by exploiting the non-pulsating signal. Bipolar/bicolor LED (red and near-ir) and photodiode were integrated onto distal tip of surgical retractors including Deaver and Babcock retractor. Using two different wavelengths of light and monitoring their attenuation relative to each other gives a value proportional to the oxygen saturation. The algorithm used is similar to that of pulse-oximeters; however, rather than detecting only the arterial component, the measurements are based on attenuation of light through the arteries, arterioles, vein, venules and capillaries. The device was designed in transmission mode and reflection mode for the different types of retractors. The signal-to-noise ratio (SNR) is much better when transmission mode is used. When

28 14 reflection mode is used, light source and detector need to be slightly angle towards each other; but significant angles can impact the normal function of the tool [23], [41] Three Charged-Coupled Device (CCD) Camera A method to assess kidney health has been proposed using 3-CCD camera used in laparoscopic surgery. White light (~ nm) source is used in all laparoscopy cases to illuminate the working area; this light is essential to obtaining view of the surgical field because it is used to produce the image seen by the surgeon on the monitor. 3-CCD camera is used to capture images seen by the surgeon; three separate CCDs are used to take separate measurements from red, green, or blue light forming an image. Individual frames of the video can be converted into an image that can be processed. Using image processing algorithms, the intensity of red, green and blue light can be determined for each pixel of the image. The blue CCD response is subtracted from the red CCD response, and the difference can be correlated with oxygenation. HbO2 is expected to absorb more blue light, while Hb is absorbs more red light. With changes in oxygenation, intensity of the red, blue and green light change [42]. This technique could be expanded to measure tissue oxygenation during laparoscopic surgery in a more general area. Using this technique, tissue oxygenation can only be measured few millimeters below the surface. 2.5 Comparing Techniques As described in the previous section, various techniques can be used to monitor StO2. Each technique has its advantages and limitations that need to be considered prior to selecting a technique to integrate with surgical tools. Blood gas analyzers: Even though blood gas analyzers are considered as the gold standard, they are very invasive. They require blood to be drawn from large vessels to obtain a reading which may not be feasible and justified during surgery. In addition, blood gas analyzers are not suitable for continuous reading Transcutaneous oximetry: Although transcutaneous oximeters provide continuous measurements, it takes approximately minutes to heat the tissue to obtain measurement. In addition, tcpo2 is only suitable for measurements on the skin which limits its application for other soft tissue.

29 15 NIRS: When using NIRS, light source and detector need to be few centimeters apart for reliable measurements. Due to the restrictions in available workspace at the tip of a laparoscopic instruments, it is not practical to keep the source and detector a few centimeters apart. In addition, the cost of the NIRS box is approximately $16000 with $150 for the sensor [43]. VLS: Existing VLS systems have various probe designs that maybe suitable for monitoring tissue oxygenation in laparoscopic surgery; however, these probes would interfere with the surgical workflow as the surgeon would need to remove the tool and insert the sensor through the trocar for reliable measurement. This would add time to the surgical procedure, and continuous monitoring would not be possible. Available VLS oximeters use fiber optics for guiding the light source which makes the integration with laparoscopic instruments significantly more complex. Finally, VLS monitoring systems cost approximately $21,500 for the monitoring box and $400 for a disposable sensor [43]. 3-CCD camera: For the purpose of this application, the interest is in monitoring oxygenation changes at the site of grasping. This is not feasible with the 3-CCD camera technique because surgical instruments would not permit grasped tissue to be imaged. Pulse-oximeter like technique: Pulse-oximeter like technique uses LEDs as light source and photodiodes as a light detector. This technique is transmission based; hence the source and detector can be placed opposite to each other making it simpler to integrate with the laparoscopic instrument. Pulse oximeters are effective for finger thickness of 8mm to 26mm [40]; it can be assumed that transmission based pulse-oximeter like technique would be effective for similar tissue thicknesses. Furthermore, the pulse-oximeter technique is simpler and offers a costeffective solution in comparison to NIRS and VLS. LEDs and photodiodes are compact, easily available, low cost, and provide the possibility of a disposable sensor compared to a laser, fiber optics or narrow-band optical filters which are much more difficult to integrate and cost significantly more [23]. Existing tools are too bulky to be inserted through standard trocars because they have wires running along the shaft of the instrument. In addition, the tool tip needs to be drastically modified to integrate the large sensing components. Also, existing tools have only tested LEDs with peak wavelength of 660nm and 940nm. Furthermore, the tool s ability to sense StO2 with grasped tissue has not been tested.

30 Research Goal To identify suitable wavelengths for continuously measuring tissue oxygen saturation (StO2) while performing surgical maneuvers using laparoscopic instruments. 2.7 Research Objectives 1) Design, build, and test a StO2 sensor for continuous monitoring by testing various wavelengths in reflectance and transmittance mode using light emitting diodes (LEDs) as light source and photodiodes as light detector. 2) Miniaturize and integrate the StO2 sensor onto laparoscopy tools without compromising the tool functionality. 2.8 Constraints The following requirements should be satisfied by the sensor: Sensor needs to be integrated with surgical tools without compromising the tool functionality, Sensor provides continuous reading for the tissue being manipulated by the surgeon, Sensor needs to fit through a standard 5mm trocar, Sensing components of the sensor should be at the distal end of the instruments where the tissue is being grasped, and Sensor should not interfere with normal surgical workflow.

31 17 Transmission Mode Sensor Design 3.1 StO2 Sensor Design Optoelectronic Selection (LEDs and Photodiode) A StO2 sensor was developed using pulse-oximeter like technique in transmission mode with LEDs as light source and a photodiode as light detector. Optoelectronic components were carefully selected as the performance of the sensor largely depends on their selection. LEDs with peak wavelength of 660nm (red region) and 940nm (near-ir region) are desired to achieve the required depth of penetration of 8-25mm which covers thickness of various abdominal organs including liver, small intestine, large intestine, kidney and gallbladder. The selected photodiode needs to detect wavelengths at 660nm and 940nm. It was also preferred to use a single photodiode to detect the two desired wavelengths rather than two separate photodiodes for each wavelength to simplify the circuit, and reduce the sensing components required at the tool tip. To integrate the sensing components at the tool tip without compromising tool functionality and ensuring the sensor can fit through a standard trocar, the selected sensing components were small as practically possible. While small LEDs are available (0201 package size, 0.65mm x 0.35mm), it is important to ensure the selected LEDs provide the desired peak wavelength and enough light intensity to penetrate through the tissues of interest; LEDs in 0201 package are not available at 660nm and 940nm. Output light intensity of a LED is dependent on the junction size. After comparing the junction size LEDs in 0402 package (1.00mm x 0.50mm) and 0603 package (1.6mm x 0.8mm) as seen in Figure 9, it was evident the junction size was approximately the same indicating similar output light intensity. Since LEDs in 0402 and 0603 package size provide the same output intensity, 0402 package was preferred due to its smaller size making it easier to integrate with laparoscopic tool. Selected red and near-ir LEDs have peak wavelengths of 650nm (SML-LX0402DRC-TR, Lumex) and 940nm (SFH 4043, OSRAM), respectively; difference between the desired wavelength of 660nm and practically achievable wavelength of 650nm will not affect the performance of the sensor.

32 18 The smallest available photodiode has package size of 0805 (2.0mm x 1.25mm); hence photodiode with this package size was selected. Selected photodiode (TEMD7000X01, Vishay) has working range from 400nm-1100nm; 660nm and 940nm are within this range. Figure 9: Comparing junction size of different LEDs Integrating Sensor with Laparoscopic Tools StO2 sensor s sensing components were integrated within the tool tip for continuous monitoring. This allows tissue health to be monitored while the tissue is maneuvered by the surgeon. Optoelectronic components were integrated within the cut-out present (Figure 10) in many graspers such that they do not interfere the tool s performance or the surgical workflow. The available workspace within the cut-out is approximately 20.00mm x 2.00mm x 1.40mm; the selected optoelectronic components can be fitted into this space. Figure 10: LEDs and photodiodes will be integrated in the cut-out to ensure the toolfunctionality is not compromised.

33 19 Flexible printed circuit boards (FPCBs) were used to integrate the optoelectronic components within the laparoscopic tool. FPCB provides a reliable alternative to running wires along the instruments because FPCBs are less likely to be damaged due to friction with the trocar and provide a stronger signal. FPCB have the added benefit of being waterproof, moisture proof and corrosion resistant, while adding negligible weight to the surgical instrument. Furthermore, FPCB can be customized to meet the required application due to the elastic nature of the FPCB which makes it possible to place them around edges and folds. In addition, they add minimal thickness of 0.1mm to the tool allowing the tool to fit through standard trocars. As shown in Figure 11, the red and near-ir LEDs were placed adjacent to each other on a FPCB, while the photodiode is placed on another FPCB. FPCB with LEDs was placed on one side of the grasper and FPCB with photodiode was placed on the opposite side allowing the sensor to work in transmission mode. Transmission mode provides better signal, as well as allowing the StO2 to be measured for the entire tissue thickness. FPCBs were custom designed such that the LED junctions can be placed directly across the sensing junction of the photodiode to maximize the light received by the photodiode. Figure 11: LEDs and photodiode integrated with FPCB. The integration of tissue oxygenation sensor with standard bowel grasper is shown in Figure 12. The FPCB runs along the shaft of the instrument; the LEDs and photodiodes were placed at the tip of the instrument. Microcontroller was placed near the handle and remains outside the trocar. The microcontroller is very light and does not interfere with the normal usage of the tool. StO2 sensor was also integrated with custom 3D-printed forceps shown in Figure 13 for ease of testing.

34 20 Figure 12: Tissue oxygen sensor (comprised of LEDs, photodiode, FPCB, and microcontroller) integrated with standard bowel grasper Figure 13: Custom forceps with tissue oxygenation sensor integrated Circuit Design An Arduino Nano was used as a micro-controller. This micro-controller was selected due to its small size, robustness, programmability, USB communication and convenience as it significantly reduced hardware and software development. A voltage follower circuit, also called a buffer amplifier, was used to drive the LEDs as shown in Figure 14. The output voltage of an ideal voltage follower directly follows the input voltage. This was achieved as the operational amplifier (op-amp) has no resistance in its feedback loop; hence the output voltage will be the same as the input voltage. Op-amps typically have very high input impedance which means they draw very little current according to Ohm s law. Since the LED demands high current due to its low impedance, it can cause damage to the Arduino if connected directly. Thus the voltage follower acts as an isolation buffer to prevent disturbances in the circuit. Resistance values of 120 ohms and 50 ohms were chosen for the current-limiting resistors for the red LED and near-ir LED, respectively. This ensured maximum current was

35 21 supplied to each LED. A low-voltage rail-to-rail output op-amp (TLV 341, Texas Instruments) was used. Figure 14: Voltage follower circuit to drive LED A trans-impedance amplifier was used to convert current to voltage. A photodiode converts light into current when photons are absorbed by the photodiode; the current needs to be converted to voltage for measurement. When the current output of the photodiode was tested on a multi-meter with 660nm and 940nm LEDs, the photodiode typically produced current in the order of a few hundred nano-amps. As seen in Figure 15, a 2MΩ resistor was used to convert this current into a voltage in the order of magnitude of a few hundred micro-volts. This voltage was amplified by a factor of eleven using 10 kω and 1kΩ resistors to maximize voltage range. Figure 15: Trans-impedance amplifier with 11x gain

36 Algorithm The following algorithm was used for measuring changes in StO2: 1) Red LED was turned on for 20 milliseconds. 2) Photodiode reading was taken. 3) Red LED was turned off. 4) Near-IR LED was turned on 20 milliseconds. 5) Photodiode reading was taken. 6) Near-IR LED was turned off. 7) Both LEDs were off for 20 milliseconds. Ambient light reading was measured. 8) Ambient signal was subtracted from red and near-ir LED reading. LEDs were pulsed at constant intensity and amplitude, and changes were measured for each light intensity individually. Due to the pulsing, single photodiode could be used for measuring response of both LEDs. Changes in light intensity can be related to changes in concentration of hemoglobin. Other alternatives such as frequency and amplitude modulations exist; however, they significantly complicated the circuit. Ambient light was subtracted from red and near-ir reading to improve the signal reliability nm and 940nm in Transmission Mode Testing In vivo Experiment 1 - Proof of Concept In vivo proof of concept experiments were conducted to prove the effectiveness of 660nm and 940nm wavelengths in detecting changes in StO2 using transmission mode. For ease of testing, the StO2 sensor was integrated with custom forceps as shown in Figure 13. A standard blood pressure cuff was placed on the forearm, and the index finger was gently grasped with custom forceps to monitor changes in StO2 as shown in Figure 16. A baseline reading was collected for 15 seconds. Pressure was increased from 0 to 260mmHg and maintained for 90 seconds. Pressure was reduced to 0mmHg and changes were monitored for 30 seconds after the release.

37 23 Figure 16: Blood pressure cuff placed on the forearm and custom tool placed on index finger to measure oxygenation changes on the finger as forearm is occluded The results from the experiment are shown in Figure 17. As the pressure was increased at 15 seconds, a reduction in red and near-ir signal occurs because blood from the forearm is forced into the hand: due to the local increase in blood volume, more light is attenuated as expected from the Beer-Lambert Law since the distance light travels through increases. A new baseline is established after the pressure has been increased to 260mmHg. As HbO2 is converted to Hb, the red light signal decreases and near-ir light signal increases which is the theoretically expected result. The changes in oxygenation levels were detected immediately after the blood pressure cuff was increased to 260mmHg. As the cuff pressure is reduced at 105 seconds, the tissue oxygen levels approach the baseline readings. 660nm and 940nm wavelengths were determined to be sensitive enough based on the in vivo proof-of-concept conducted on the finger. Based on the results, StO2 was tested using transmittance technique in vivo on pigs.

38 24 Red Response (V) IR Response (V) Time (s) Figure 17: Monitoring response of red and near-ir signal at index finger after placing blood pressure cuff on the forearm and slowly increasing cuff pressure. Forearm was occluded at 15 seconds and released at 105 seconds In vivo Experiment 2 Grasping Bowel In this experiment, the goal was to test the StO2 sensor s ability to detect changes in oxygenation in vivo while grasping a tissue. Small intestine was grasped with relatively low and high force using laparoscopic tool as shown in Figure 18. A baseline reading was obtained for 15 seconds. After the baseline reading, the small intestine was grasped with relatively low force for 60 seconds; elastic bands were used to control the forces applied by the grasper. This was repeated at a higher force.

39 25 Figure 18: Grasping small intestine with sensorized laparoscopic grasper When tissue was grasped with relatively low forces, there is an initial increase in red and near-ir signal as shown in Figure 19. The initial increase in the photodiode response is due to the reduction in distance between LED and photodiode, and the local reduction of blood volume from squeezing the tissue. This increase in the signal is expected based on the Beer-Lambert Law because the distance light travels before reaching the photodiode is reduced. From the proof of concept conducted on the finger, the intensity of the red light was expected to decrease and the intensity of the near-ir light was expected to increase as HbO2 gets converted to Hb during the grasp. Based on Figure 19, no changes in tissue oxygenation were detected after grasping for 60 seconds. Similarly, when high grasping force was used, no changes were observed in the LED intensity as shown in Figure 20. The only difference between low and high grasping force was the initial increase in the signal at the time of grasping. The signal rises to a higher value when higher forces are applied because the LEDs and photodiodes are closer together, as well a larger volume of blood is forced out of the tissue compared to when lower forces are applied.

40 IR Response (V) Red Response (V) Red Response (V) IR Response (V) Time (s) Figure 19: Monitoring red and near-ir signal after grasping with low forces. Tissue was grasped at 30 seconds and release at 90 seconds Time (s) Figure 20: Monitoring red and near-ir signal after grasping with high forces. Tissue was grasped at 15 seconds and released at 75 seconds.

41 In vivo Experiment 3 Measuring Beside Tissue During in vivo experiment 2, it was evident that relatively low forces are required to displace the blood from the site of grasping as shown in Figure 21. A pale region is clearly evident beside the grasper indicating blood displacement even when the liver and small intestine were grasped with relatively low forces. Figure 21: Blood displacement after grasping bowel (left) and liver (right) with low forces Based on the observations and results from the previous in vivo experiment, the StO2 was measured beside the site of grasping rather than at the site of grasping as shown in Figure 22. The goal was to indirectly show how different grasping forces affect tissue oxygenation: if the tissue is grasped with different forces, different changes in tissue oxygenation should be expected beside the site of grasping. Figure 22: Monitoring tissue oxygenation changes beside the site of grasping. Liver is clamped with ratcheted forceps and tissue oxygenation is measured with custom forceps

42 28 The results from this experiment are shown in Figure 23. Liver was grasped with custom forceps and baseline reading was obtained for 20 seconds. Ratcheted forceps were used to grasp the liver beside the sensor with high forces. The trends in red and near-ir signal between seconds are identical indicating no changes in oxygenation. These affects are most likely due to changes in blood volume beside the grasped site; with reduction in StO2, intensity of red signal would decrease and intensity of near-ir signal would increase. Forceps compressing the liver was released at 120 seconds. Figure 23: Monitoring response of red and near-ir light beside the site of grasping. Tissue was grasped at 20 seconds and released at 120 seconds In vivo Experiment 4 Occluding Arterial Supply During in vivo experiment 2 and 3, no changes were observed in the StO2 levels when StO2 was measured at or beside the grasped location. A potential reason for not observing any changes maybe that the tool is not be sensitive enough to detect in vivo changes. To determine the in vivo tool sensitivity, the mesenteric artery was occluded to induce large changes in StO2. A baseline reading was obtained for 20 seconds. The mesenteric arteries supplying the small intestine were clamped to cut-off oxygenated blood as shown in Figure 24 using a bulldog clamp at 20 seconds. The changes in oxygenation levels were measured using the custom forceps.

43 29 Figure 24: Clamping mesenteric arteries with bulldog clamp to cut-off blood supply to the bowel As shown in Figure 25, a net decrease in red signal and increase in near-ir signal is evident after the clamp was applied at 30 seconds which indicates decrease in oxygenation This experiment rules out the in vivo tool sensitivity as a potential problem. The fluctuations in the signal are due to the motion artifacts associated with the addition of the bulldog clamps Figure 25: Measuring red and near-ir signal with custom forceps after cutting off mesenteric artery. Arteries were clamped at 30 seconds

44 In vivo Experiment 5 Grasped Finger In vivo experiment 4 confirmed the StO2 sensor was capable of detecting in vivo oxygenation changes; however, when organs were grasped in vivo, no changes were measured. To better understand the physiological effects, further experiments were repeated where the finger was clamped similarly to tissue for three minutes using the custom forceps and the changes in StO2 were measured at the site of grasp as shown in Figure 26. Figure 26: Grasping finger with custom forceps and measuring changes at the site of grasping The changes in StO2 are shown in Figure 27. The finger was grasped with custom forceps at approximately 5 seconds. After grasping, there is an initial rise in red and near-ir signal because LEDs and photodiodes are brought closer together, and blood is forced out from the site of measurement. After the grasp, it took approximately 60 seconds before any changes in StO2 are detected which is indicated by a decrease in the red signal and increase in the near-ir signal; it took approximately 60 seconds before physiological changes start to occur due to grasping.

45 31 Figure 27: Monitoring response of red and near-ir light after grasping finger with custom forceps nm and 940nm in Transmission Mode - Discussion There are two major differences in the results between the two in vivo experiments conducted on the finger (In vivo experiment 1 and 5). In the proof of concept experiment, the drop in StO2 was seen immediately after increasing the pressure to 260mmHg, whereas it took approximately 60 seconds before changes were detected in the grasped finger experiment. In the proof of concept experiment, the changes in signal from tissue oxygenation are approximately a magnitude higher than changes detected in the grasped finger experiment. Smaller changes occur in StO2 when the finger is grasped rather than occluding the blood flow to the finger. This could be due to lower blood volume at the site of measurement which results in less light attenuation. Furthermore, when the forearm is occluded, HbO2 is almost completely blocked off from reaching the hand; however, when finger is grasped, there is possibility of some blood flow to the grasped site. When StO2 changes were monitored by grasping the bowel, StO2 sensor did not detect changes in oxygenation. In this experiment, tissue was grasped reducing the thickness to few millimeters and pushing away the blood from site of measurement. Approximate hydrostatic capillary pressure (P) is approximately 25mmHg (3.3kPa). If we assume an area (A) of 1 cm x 1cm, it

46 32 would take approximately 0.33N of force (F) to displace the blood from capillaries; 0.33N of force equates to 36 grams assuming 9.81m/s 2 gravitational constant (g). (6) F = P A (7) F = m g (8) m = P A g Typical grasping force used during surgical grasps is about 8.52 ± 2.77 N [46]. This is over an order of magnitude higher than the force required to displace the blood from capillaries. The type of grasper used can greatly influence the pressure applied to the tissue. Smaller grasping tips magnify the pressure at the tip; too much pressure is likely to cause tissue damage. Larger jaws provide better grip due to more friction, as well for a given force larger jaws reduce the pressure applied to the tissue. Unfenestrated jaw designs rely solely on friction to hold the tissue in place which increases the chances of applying excessive pressure. Fenestrated design which incorporate open area provide safer grip because they enclose portion of tissue within the window preventing it from slipping [47]. The use of StO2 sensor while grasping tissue may be limited to graspers that apply minimal pressure to the tissue. Furthermore, thinner tissues are likely to be more delicate in comparison to thicker tissue as thicker tissue is likely to have redundant blood supply making it less prone to ischemic damage. This may put restrictions on the tissue thickness the sensor can provide reliable readings from. StO2 sensor successfully detected changes in StO2 where the arterial supply was occluded. In this experiment, tissue was genteelly grasped without reducing the thickness (approximate small intestine thickness was ~1cm) and blood was not forced out. From these results, it is evident that 660nm and 940nm wavelengths are unable to detect oxygenation changes in thin tissue or areas with low hemoglobin. Wavelengths in the red and near-ir region need long path lengths for reliable measurements due to their relatively low extinction coefficient limiting its use to only monitoring large, homogenous volume of tissue. During normal surgical maneuvers, soft tissue thickness is likely to be reduced to few millimeters during grasps and hemoglobin is likely to be forced out; hence 660nm and 940nm cannot be used in transmission mode to detect StO2 changes while tissue is maneuvered.

47 33 Exploring Visible Spectrum 4.1 Visible Light Advantages From the previous in vivo experiments, it is apparent that additional wavelengths need to be explored for reliable measurements in thin tissues or areas with low hemoglobin. Ideally, larger and more noticeable changes in signal intensity should be expected. Based on Figure 6, focus should be on visible light in the wavelength range from nm which have very high extinction coefficient. Wavelengths within nm have shorter path lengths because they have higher frequency. High extinction coefficient and shorter optical path lengths make it possible to perform reliable measurement in small tissue volume using visible light due to its shallow penetration depth. 4.2 Wavelength Selection for Reflectance Measurement Specific wavelengths within nm were selected for determining changes in tissue oxygenation. To robustly detect changes in tissue oxygenation, wavelengths where the difference between molar extinction coefficient of oxygenated and deoxygenated hemoglobin is the largest were used. Based on Figure 28, LEDs with peak wavelength of 435nm (QTLP600CBTR, Everlight), 475nm (APTD1608VBC/D, Kingbright), 510nm (SM1204UPGC, Bivar) and 560nm (LP T655-Q1R2-25-Z, OSRAM) were desired due to the large difference in the molar extinction coefficient between HbO2 and Hb. In addition, wavelengths at isosbestic points were selected where oxygenated and deoxygenated hemoglobin have identical extinction coefficient. Theoretically, these wavelengths can provide estimation of total hemoglobin content because signal at isosbestic wavelength is not expected to vary with changes in oxygenation; rather, the signal would only be impacted by an overall increase or decrease in the total hemoglobin content. Based on Figure 28 LEDs with peak wavelength of 500nm (SM1204PGC, Bivar), 570nm (HSME-C150, Avago Technologies) and 588nm (LTST-C930KSKT, Lite-On) were selected. LEDs at 660nm (AA3528SRS, Kingbright) and 940nm (SFH 4243-Z, OSRAM) were also tested in the reflectance mode; these wavelengths should provide reliable results on the finger which is relatively thick.

48 34 Figure 28: Molar Extinction Coefficient of oxygenated and deoxygenated hemoglobin within visible light range. Black lines indicate wavelengths with the largest difference between molar extinction coefficient of oxygenated and deoxygenated hemoglobin. Yellow lines indicated wavelengths with identical coefficients of oxygenated and deoxygenated hemoglobin Due to the shallow penetration depth, visible light cannot be used in transmission mode where the light source and detector are placed across each other as shown in Figure 29. Majority of the light will be attenuated within 1-2mm; no light will pass the first few millimeters making transmission mode unsuitable to obtain a reliable reading. Instead, it is necessary to use reflectance mode where the light source and detector are placed beside each other. In reflection mode as shown in Figure 29, the distance between light source and detector determines the depth of penetration; higher source and detector distance allow deeper depth of penetration. There exists a physical limit for each wavelength on the distance between the source and detector. If they are placed too far apart, light will be attenuated before reaching the detector.

49 35 Figure 29: Illustration of transmittance (left) and reflectance (right). In transmission mode, source and detector are placed across each other. In reflection mode, source and detector are placed beside each other. 4.3 Design of Reflectance StO2 Sensor Design LEDs and photodiode are placed beside each other. In reflection mode, it is important to eliminate the interference between LED and photodiode that is evident in Figure 30. Light exciting the photodiode directly needs to be blocked to prevent light that does not pass through the tissue from skewing the measurements. Also, the height of the LED and photodiode is typically different, making it difficult to obtain a consistent baseline reading. To overcome these issues, the jig in Figure 31 was designed to minimize the intensity of light directly reaching the photodiode, as well as addressing the height differential. Black color was chosen to block maximum amount of light from directly reaching the photodiode. Figure 30: Reflectance set-up showing LEDs and photodiodes placed beside each other.

50 36 Figure 31: Reflectance set-up showing LED and photodiode with the jig to reduce unnecessary photodiode and LED interference. Finger was placed on the set-up seen in Figure 30 and Figure 31 to obtain a baseline reading for 100 seconds. Consistent contact with the sensor was ensured using custom forceps to maintain the pressure. By comparing the two trends in Figure 32, it is clear the jig eliminates the jump in readings that are present at 15 seconds and 82 seconds in the blue trend. When the jig is not used, the sensor is much more sensitive to motion. Use of the jig provides a much more consistent baseline. Figure 32: Comparison of baseline reading with and without jig

51 37 In the current design, center to center distance between LED and photodiode is approximately 2.7mm; this distance was constrained by the size of the LED and photodiode. In order to vary the distance, different size LEDs and photodiode are required and custom mounting board needs to be fabricated. The distance between LED and photodiode specifies the depth of penetration as. According to a study performed by Liu et al., spacing of mm between light source and detector did not have an impact on StO2 readings [44]. For ease of integration into tool, same photodiode (SFH 2701, OSRAM) was used with all the LEDs. Photodiode was selected to provide largest possible sensitivity for the selected LEDs. Photodiode used in this application has a working range from 400nm-1100nm; 435nm, 470nm, 500nm, 510nm, 560nm, 570nm, 586nm, 660nm and 940nm are within this range. Surface mount LEDs were preferred due to their compact size. It was attempted to use similar sized LEDs in order to maintain the set 2.7mm distance between the LED and photodiode. The circuit design and algorithm used were identical to transmission mode. 4.4 In vivo Experiment 6 Reflection Mode Test protocol StO2 sensor was tested in reflection mode in vivo. A standard blood pressure cuff was placed on the upper arm, and the index finger was gently grasped with custom forceps to monitor changes in StO2. A baseline reading was collected for 30 seconds. Pressure was increased from 0 to 260mmHg and maintained for 90 seconds. Pressure was released and changes were monitored for 60 seconds. 4.5 Theoretically Expected Results Theoretically expected increase or decrease in signal intensity with changes in StO2 needs to be determined to compare experimental results with theoretically expected results. For a monochromatic light source, which produces light of a single wavelength, this relationship is determined by comparing the molar extinction coefficient of HbO2 and Hb. According to the Beer-Lambert Law, if the molar extinction coefficient of HbO2 is high, relative to Hb, output light intensity will decrease with decreasing oxygenation. Conversely, if the molar extinction coefficient of Hb is higher than HbO2, the output light intensity will increase with decreasing oxygenation. The molar extinction coefficient versus wavelength curve shown in Figure 6 can be subdivided into ten segments using the crossover points to divide the regions ( nm,

52 nm, nm, nm, nm, nm, nm, nm, nm and nm). At the crossover points oxygenated and deoxygenated hemoglobin have identical extinction coefficients. Within nm, nm, nm, nm, and nm molar extinction coefficient of deoxygenated hemoglobin is higher than the molar extinction coefficient of oxygenated hemoglobin. With decrease in oxygenation, the output intensity will also decrease. Within nm, nm, nm, nm and nm molar extinction coefficient of deoxygenated hemoglobin is lower than the molar extinction coefficient of oxygenated hemoglobin. With decrease in oxygenation, the output intensity will increase. In practice, imperfect light sources which are not monochromatic, such as LEDs, are often implemented to measure tissue oxygenation. These sources produce light with wavelengths distributed over a small region of the spectrum. For example, consider the relative intensity versus wavelength distribution plot depicted in Figure 33. This data was approximated using the datasheet for a 500 nm LED (SM1204PGC, BIVAR). The 500 nm LED has its peak intensity at 500 nm; however, it emits over a range of approximately nm. Figure 33: Relative intensity versus wavelength for peak wavelength of 500nm approximated

53 39 The wavelength distribution of LEDs introduces some complexity when predicting the relationship between changes in signal with changes in oxygenation. These challenges are evident when the spectral distribution of an LED is overlaid on the molar extinction coefficient versus wavelength curve, as shown in Figure 34. Here, the spectral distribution covers multiple regions separated by isosbestic points. Therefore, the expected trends in signal cannot be strictly obtained by comparing molar extinction coefficient for HbO2 and Hb at a given wavelength. The spectral distribution over which the LED emits needs to be considered. Figure 34: Overlaying 500nm peak LED spectral distribution with molar extinction coefficient of hemoglobin curve To address this problem, first the relative output intensity for each wavelength, using the Beer- Lambert Law needs to be determined. To complete this calculation, tabulated values for wavelength dependent extinction coefficient (ϵ(λ)) are available in [33]. A hemoglobin concentration (c) of 15.0 g/l can be assumed as it is within the normal range for an adult male and female. The molar mass of hemoglobin is 64,500 grams per mole, and an arbitrary value of 1 mm can be assumed for the distance (d) that light passes through. Further, to determine relative intensity, the incident light intensity (I o ) is scaled to one. Relative output intensity (I o ) versus wavelength is plotted in Figure 35. Relative output intensity within nm is much smaller

54 40 compared to relative output intensity between nm as a result of the much higher extinction coefficient between nm. Figure 35: Relative output intensity versus wavelength The LED has a different intensity output for each wavelength (Figure 33), and also because the photodiode has a different spectral sensitivity for each wavelength (Figure 36). The wavelength dependent relative intensity and spectral sensitivity are used to adjust the output intensity by multiplying output intensity with relative intensity and spectral sensitivity for a given wavelength. The results for the adjusted output of a 500 nm LED is plotted in Figure 37.

55 41 Figure 36: Photodiode spectral sensitivity as function of wavelength Figure 37: Relative output intensity versus wavelength adjusted for LED and photodiode intensity

56 42 From Figure 37, the relative output contribution for each wavelength is known. The area under the relative intensity versus wavelength curve is used to determine the expected results. If the area under the HbO2 curve (100% oxygenation) is greater than the area under the Hb curve (0% oxygenation), signal intensity is expected to decrease with a relative decrease in oxygenation for the specific LED. If the area under the HbO2 curve is less than the area under the Hb curve, signal intensity will increase with a decrease in oxygenation for the specific LED. Areas under the curve for HbO2, Hb, and the expected trends with a decrease in oxygen are depicted in Table 1 for each LED. Table 1: Area under relative output intensity versus wavelength curve for HbO2, Hb and expected changes in signal with decrease in oxygenation Peak Wavelength (nm) Area under HbO2 curve Area Under Hb Curve Increase/Decrease in Signal Intensity with Decrease in Oxygenation E E-04 Increase E E-04 Increase E E-04 Increase E E-04 Increase E E-07 Decrease E E-05 Decrease E E-04 Decrease E E+00 Decrease E E+01 Increase

57 In vivo Experiment 6 Reflection Mode Results A 30 seconds baseline reading was collected for each LED. Average signal intensity was calculated for 25 seconds; this value was used to normalize the data such that baseline reading was approximately 1. According Figure 38, Figure 39, Figure 40, Figure 43 and Figure 45 there is an upward trend in the signal after the pressure in the cuff is increased to 260mmHg; LEDs with peak wavelength of 470nm, 500nm, 510nm, 586nm and 940nm saw an increase in signal with decrease in oxygenation. The increase was followed by a decrease when the pressure was released. From Figure 44, there is a downward trend in signal after the pressure in the cuff is increased to 260mmg; LED with peak wavelength of 660nm saw a decrease in signal with decrease in oxygenation. The decrease was followed by an increase when the pressure was released. Figure 41 and Figure 42 drift upwards for the duration of the experiment; hence the results for LEDs with peak wavelength of 560nm and 570nm are inconclusive. Figure 38: Monitoring response of LED with peak intensity at 470nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds.

58 44 Figure 39: Monitoring response of LED with peak intensity at 500nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds. Figure 40: Monitoring response of LED with peak intensity at 510nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds.

59 45 Figure 41: Monitoring response of LED with peak intensity at 560nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds. Figure 42: Monitoring response of LED with peak intensity at 570nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds.

60 46 Figure 43: Monitoring response of LED with peak intensity at 586nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds. Figure 44: Monitoring response of LED with peak intensity at 660nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds.

61 47 Figure 45: Monitoring response of LED with peak intensity at 940nm. Pressure in the cuff is increased at 30 seconds and released at 120 seconds. Table 2 summarizes the experimental results and compares them with theoretical results. According to Table 2, LEDs with peak wavelengths of 470nm, 500nm, 510nm, 660nm and 940nm behave as predicted. LEDs with peak wavelength of 560nm, 570nm and 586nm do not behave as predicted. LED with peak wavelength of 435nm was not tested due to the large spectral distribution ranging from nm. Table 2: Comparing theoretical and experimental results for reflection mode experiment Peak Wavelength (nm) Theoretical Results Experimental Results Agreement (Yes/No) 435 Increase Not Tested N/A 470 Increase Increase Yes 500 Increase Increase Yes 510 Increase Increase Yes

62 Decrease Inconclusive N/A 570 Decrease Inconclusive N/A 586 Decrease Increase No 660 Decrease Decrease Yes 950 Increase Increase Yes 4.7 In vivo Experiment 6 Reflection Mode Discussion There are several reasons that could explain the unexpected results. LEDs distributions are typically given at a specific temperature and current because the wavelength an LED emits varies slightly at different currents and temperatures. Junction temperature cannot be easily measured making it difficult to control. There are likely to be fluctuations in the current supplied to LED due to noise in the electrical system. LEDs also have variation in peak intensity from the manufacturing processes; a spectrophotometer would be required to characterize the output intensity of a specific LED. For the LEDs in this project, values given in the datasheet were assumed to be correct. The molar extinction coefficient versus wavelength curve for HbO2 and Hb is an average taken from multiple studies. As seen in Figure 46, there are large variations between the two datasets from difference sources especially between nm. In addition, the curve is likely to differ slightly for each individual. Within nm, the difference between extinction coefficient of HbO2 and Hb is small. LEDs may not be suitable light source and photodiodes may not be suitable detector to measuring changes within this region. Monochromatic light sources such as lasers and better detectors such as spectrophotometers might be required to adequately measure changes in this region.

63 49 Figure 46: Molar extinction coefficient versus wavelength data from Takatani et al. overlaid onto best data available [45] In order to maintain StO2 sensor contact with the index finger, pressure must be applied. This pressure may displace blood from the capillaries at the site of contact. Since the LEDs between nm have very shallow penetration depth, there might not be enough blood on the skin surface to measure changes in oxygenation. The extinction coefficient of hemoglobin is very high within nm, even small changes in blood volume would contribute to an increase in signal intensity. As mentioned before, it takes approximately 0.33N (35grams) force to displace blood from capillaries. The forces used to maintain constant contact may be adequate to force blood out from the surface of the skin. Controlled Volume Experiments 5.1 Ex vivo Experiment 1 - Controlled Volume Method To isolate the effects of blood volume, controlled blood volume testing was performed. Also, it is possible to determine the expected changes in signal intensity with changes in oxygenation. Arterial and venous blood was drawn from anesthetized rats into two separate 1mL heparinized syringes to prevent blood from clotting. The arterial blood is bright red in comparison to venous

64 50 blood which is much darker as seen in Figure 47. Typical oxygenation of arterial blood ranged from 75-95%, while the typical oxygenation of venous blood ranged from 5-15%; oxygen saturation values of arterial and venous blood were measured using a blood gas analyzer (ABL 800 Flex, Radiometer). Oxygen saturation readings of arterial and venous blood were taken before and after experiments to ensure the oxygen saturation did not vary more than 5% over the course of the experiment. Approximate difference between arterial and venous oxygen saturation is 70-85%. Figure 47: Arterial blood (75-95% oxygenation) and venous blood (5-15% oxygenation) in a 1 ml syringe Syringe holder in Figure 48 was designed to consistently position the syringe, as well as block ambient light to ensure reliable measurements. The syringe with arterial blood was placed in the syringe holder and a reading was taken for 10 seconds. After 10 seconds, the syringe with arterial blood was removed. The syringe with venous blood was placed in the syringe holder and a reading was taken for 10 seconds The average and standard deviation for each trial were calculated based on this 10 seconds reading. Seven trials were completed for each LED. Syringes were positioned to ensure the markings on each syringe did not affect the measurements. Trials 1-3 have higher standard deviation compared to trials 4-7 due to noise in the power supply. This was rectified using a DC power supply for the remainder of trials. It was not feasible to take multiple readings with each LED due to time constraints: blood would clot or change oxygenation due to long term air exposure.

65 51 Figure 48: Syringe holder set-up 5.2 Ex vivo Experiment 1 Controlled Volume Results For each trial, the intensities of arterial and venous blood were normalized by the intensity of arterial blood (i.e. arterial intensity was set to 1 for each trial) for easier relative comparison between LEDs as each LED has a different output intensity. Table 3 summarizes the experimental results and compares theoretical versus experimental results. 470nm and 940nm: As seen in Figure 49 and Figure 56 for wavelengths 470nm and 940nm respectively, arterial blood has lower mean photodiode response compared to venous blood for each trial. This result is expected based on theoretical predictions. 500nm and 510nm: As seen in Figure 51 and Figure 52 for wavelengths 500nm and 510nm respectively, arterial blood has lower mean photodiode response compared to venous blood for trials 1,2,4,5, and 6. In trials 3 and 7, arterial blood has higher mean photodiode response compared to venous blood for each trial. Based on theoretical predictions, arterial blood is expected to have lower mean photodiode response compared to venous blood. 560nm and 570nm: The results are shown in Figure 53 and Figure 54 for wavelengths 560nm and 570nm, respectively. Intensities of these lights were not enough for reliable measurements. 586nm and 660nm: As seen in Figure 55 and Figure 56 for wavelengths 586nm and 660nm respectively, arterial blood has higher mean photodiode response compared to venous blood for each trial. This result is expected based on theoretical predictions.