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OPTICS LETTERS / Vol. 40, No. 7 / April 1, 2015
Multispectral opto-acoustic tomography of exercised muscle oxygenation Gael Diot,1 Alexander Dima,1 and Vasilis Ntziachristos1,2,* 1
Institute for Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg 85764, Germany 2 Chair for Biological Imaging, Technische Universität München, Ismaninger Str. 22, München 81675, Germany *Corresponding author: [email protected] Received December 12, 2014; revised February 12, 2015; accepted March 3, 2015; posted March 5, 2015 (Doc. ID 230662); published March 27, 2015 Unlike near-infrared spectroscopy, multispectral opto-acoustic tomography (MSOT) has the potential to offer highresolution imaging assessment of hemodynamics and blood saturation levels in muscle. However motion artifacts impede the real-time applications of the technique. We developed fast-MSOT with motion tracking that reduces motion artifacts. We used this algorithm to follow blood oxygenation level changes associated with muscle exercise in the muscle and the skin of healthy volunteers. © 2015 Optical Society of America OCIS codes: (110.5120) Photoacoustic imaging; (110.4234) Multispectral and hyperspectral imaging; (170.2655) Functional monitoring and imaging; (170.3880) Medical and biological imaging. http://dx.doi.org/10.1364/OL.40.001496
Near-infrared spectroscopy (NIRS) has been extensively used to study muscle physiology during exercise [1–3]. Near-infrared light enables sensing of oxygenated and deoxygenated hemoglobin that are essential tissue chromophores indicative of oxygen utilization, metabolism, and muscle function. Complications arising from photon scattering and absorption, however, limit accurate quantification of hemodynamics and have restricted wide dissemination of NIRS technology. NIRS measurement is strongly affected by skin vascularization [4] and can provide misleading results. NIR imaging and tomography has improved the quantification of oxygenation levels [5], but is limited by low resolution, which also compromises quantification. Magnetic resonance imaging (MRI) has been used to perform high-resolution maps of the exercised muscle, but it requires expensive dedicated infrastructure that is not appropriate for handheld use. The long acquisition times may further prevent the use of MRI to assess quick oxygenation changes [6]. Multispectral opto-acoustic measurements can be offered in a portable and cost-effective format. In addition, opto-acoustic imaging does not have a strong dependence on photon scattering by tissues and therefore may be better suited to assess muscle physiology over optical methods. In the context of clinical imaging, multispectral opto-acoustic tomography (MSOT) has shown tissue penetration of several cm in human volunteers [7], implying the ability to assess superficial muscles in the human body. Moreover, the higher spatial resolution achieved by opto-acoustics over NIRS technology may improve measurements from specific tissue components by computing signals corresponding to better defined volumes. Another relevant MSOT advantage is the operation as a fast, video-rate modality [8,9]. Using parallel data acquisition and high pulse-energy laser sources able to tune up to 50 different wavelengths per second, MSOT images can be collected at 5–10-Hz rates. At such fast frame acquisition capacity, the images are largely motion-artifact free and ideal for handheld mode muscle imaging. Nevertheless, for multispectral imaging of physiological changes under exercise, some motion may still be evident on images acquired sequentially at different wavelengths. 0146-9592/15/071496-04$15.00/0
Such motion can limit the resolution and quantification in spectral unmixing operations. In a previous study, MSOT was applied to functional imaging of blood vessels [8], wherein monitoring oxygenation over time required the imaged subject and the imaging device to be fixed in order to avoid motion artifacts. Moreover, oxygenation was measured in vessels within the skin, i.e., rather superficially. To enable measurement of muscle physiology in handheld mode in humans, it was important herein to avoid immobilizing either the tissue or the device. Motion-free imaging and oxygenation measurements of dermal vasculature can be achieved by bringing an MSOT sensor in physical contact with the skin, virtually locking the underlying vasculature in relation to the ultrasound detector. Conversely, deeper seated structures, such as muscle, cannot be immobilized in relation to a handheld detector. In this Letter, we investigated the feasibility of handheld MSOT to image the muscle and study muscle hemodynamics under exercise. We combined fast per-pulse wavelength tuning technology with a motion-compensation approach that enables bulk physiological readings from muscle, offering an alternative to NIRS. Fast-MSOT was employed in handheld mode to monitor oxygenation changes in the muscle of healthy volunteers, following an exercise protocol. MSOT illumination was provided by a tunable pulsed laser (Spitlight 600 OPO, Innolas Laser GmbH, Germany) capable of wavelength tuning (675–1064 nm) on a per pulse basis at 10-Hz repetition rate. Light was coupled into a custom-made fiber bundle (CeramOptec GmbH) with rectangular output size of 40by-1 mm2 so as to create a line illumination. For detection, a multi-element piezoelectric transducer was used with 64 elements placed in a half arc of 80-mm diameter and central frequency of 5 MHz, details can be found in [10]. The transducer was enclosed in an optically and acoustically transparent low-density polyethylene membrane, creating a cavity that was filled with water for acoustic coupling. Signal acquisition was performed with a custom-built analog-to-digital 64-channel converter acquiring in parallel at a sampling rate of 40 MS∕s and 12-bit digital resolution. To enable real-time feedback © 2015 Optical Society of America
April 1, 2015 / Vol. 40, No. 7 / OPTICS LETTERS
during imaging, a delay and sum image reconstruction was implemented on a graphics processing unit (GPU) that rendered frames at 10 Hz. The Vastus Lateralis muscle was measured in three volunteers, using MSOT at 750, 800, and 850 nm. The rest state was measured by MSOT in all volunteers over 30 s, shortly prior to exercise. MSOT acquired cross-sectional images of the Vastus Lateralis, shown in Fig. 1 (see Media 1). Then, the volunteers exercised on a stationary bike, performing steady pedaling in order to enter the aerobic cycle of the muscle. The volunteers maintained constant cycling speed of 60 rpm for 14.5 min, followed by 30 s of increased cycling rate at 100 rpm. At the end of the exercise, MSOT acquired cross-sectional images of the Vastus Lateralis, over 90 s in a similar placement as the one used for the measurements at the rest state. To ensure repeatable placement of the MSOT head onto the same position, we marked the outline of the MSOT head placed on the skin with a pen. To account for laser intensity variations, a light sensor was placed at the out-
Fig. 1. (a) Gray’s anatomy of the human body figure of the right leg, (b) optical absorption intensity image of the Vastus Lateralis at 800 nm, at a resolution of 200 μm, (c) segmented and selected groups of muscle (red) and skin (orange) from the image presented in (b), (d) and (e) pictures of the MSOT handheld device, on a table and in application on the Vastus Lateralis, (f) and (g) extracted data from the skin and the muscle, respectively.
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put of the laser beam and collected a small fraction of the pulse incident on tissue. This reference measurement was used to correct (normalize) MSOT signals at each frame by dividing each opto-acoustic signal collected by the corresponding pulse intensity recorded by the light sensor. Raw MSOT measurements were then processed by applying a bandpass filter from 0.1 to 5 MHz. This operation removed high-spatial-frequency noise and better revealed the muscle mass. Images were reconstructed using numerical model-based inversion [11]. 300 images were reconstructed per exercise protocol. Figure 1(b) shows a typical MSOT image obtained. The image clearly differentiated the muscle from the skin and sub-dermal tissue. A dark interface appears between the muscle and sub-dermal tissue, indicating low optical absorption, suggesting an area of subcutaneous fat and/or the iliotibial band (Maissiat’s band). Physiological parameters were extracted from average signals collected over the muscle area at three wavelengths, in analogy to NIRS measurements. Tissue differentiation and morphology tracking were based on a segmentation algorithm applied herein to avoid manual region-of-interest marking operations. The algorithm automatically separated different tissues based on intensity information. The intensity recorded on the images is dependent on oxygenation-level changes over time and on wavelength, which may affect the segmentation results. To minimize the influence of intensity variations to the segmentation performance, we discretized each image acquired into eight image-intensity values. This discretization step reduced the sensitivity of the algorithm to small intensity variations, for example, variations expected with blood oxygenation-level changes over time. Then, to identify the area of muscle and of skin, a flood fill algorithm was used. Figure 1(c) shows the unique regions of interest identified on the image shown in Fig. 1(b). Essentially, the algorithm allowed for identification of the skin region and of a bulk muscle region allowing an automatic extraction of average intensity values from the two regions. Such task would be otherwise difficult to perform manually due to the large number of images acquired during the measurements. While other segmentation procedures could be applied, this performance was deemed satisfactory for the purpose of the study herein. We visually inspected all processed images to ensure that intensity variations did not affect the segmentation of the muscle mass versus skin. Figures 1(d) and 1(e) show the corresponding images after applying the mask in Fig. 1(c) to the image shown in Fig. 1(b), extracting the skin region and the bulk muscle region, respectively. To extract physiological measurements from the segmented MSOT images, we calculated the mean intensity value of the muscle M λ and skin/sub-dermal tissue S λ for each wavelength λ scanned. To simplify the inspection of the results obtained, we termed the measurements at 750 nm as deoxygenated signal (M deoxy and S deoxy ), since 750 nm is significantly more sensitive to deoxygenated hemoglobin versus oxygenated hemoglobin. We termed the measurements at 800 nm as total blood volume signal (M total and S total ) due to the proximity of the 800-nm signal to the NIR isosbestic point. Finally, we termed the signals at 850 nm as oxygenated signal (M oxy and S oxy ), since the influence from oxygenated hemoglobin on the
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measurements at 850 nm is much stronger that the one from deoxygenated hemoglobin. Figure 2 plots the ratio of oxy- and deoxy-signals, over total blood volume values for muscle and skin. This representation of the data indicates relative changes of the oxy- and deoxy- signals, independent from total blood amount. In particular, Figs. 2(a),2(b) plot the quantities M oxy ∕M total and M deoxy ∕M total , obtained from muscle and Figs. 2(c),2(d) the corresponding, S oxy ∕S total and S deoxy ∕S total for skin. The triangles and circles represent the mean value of the results of all the volunteers, whereas the dotted lines show the standard deviation. The solid lines through the data are the fits using a 4th-degree polynomial equation that yielded root mean square error (RMSE) values below 0.06. The fits assess the general trends of the values plotted. In the muscle, both oxygenated and deoxygenated signals show different values before and after exercise. The deoxygenated blood signal appears higher than baseline immediately after exercise, and it recovers within ∼60 s to baseline measurements. The oxygenated blood appears lower than baseline immediately after exercise and similarly recovers after ∼60 s to baseline measurements. Conversely, the data do not show significant changes over baseline for the skin.
Fig. 2. (a), (b) Relative muscle deoxygenation and oxygenation before and after exercise. The triangles and the circles represent the mean oxygenation and deoxygenation values of the measurements obtained from all the volunteers. The dotted lines indicate the standard deviation and the solid line is the curve fitting with a 4th-degree polynomial equation with a RMSE below 0.06. (c), (d) Corresponding measurements obtained from the skin.
Fig. 3. (a) Blood volume-relative variations over time in the muscle before and after exercise, (b) blood volume-relative variations over time in the skin before and after exercise. Maximum standard deviation of 1% for all the plots.
Figure 3 plots the relative blood volume variations in the muscle and in the skin over time, before and after exercise. The blood volume before exercise remains constant over time. After exercise, the blood volume in the muscle and in the skin is increasing. The results are consistent with the expected aerobic activity in the muscle. The higher value of deoxygenated blood signal immediately after exercise reflects the increased oxygen consumption during the activity, a measurement that is corroborated by the corresponding decrease in the relative oxygenation. The relative blood volume variations after exercise increases over time in the muscle as expected. Indeed, the blood is being chased away from the muscle at every contraction. In the skin, the blood volume also increases as a result of heat and higher heartbeat. The technique can differentiate oxygenation changes in the skin and muscle, since, in contrast to NIRS methods, it accurately resolves depth and separates the signals in the two different layers. Nevertheless, an identified challenge of the method is that the results shown are calculated as average values over skin and muscle areas. Therefore, effects of depthdependent fluence attenuation [12] will be present in the calculated results. These effects are however partially counterbalanced due to the increased sensitivity of the curved detector used in this study toward the center of its radius [13]. Overall, such effects need to be more closely investigated in the future; however, a limitation might be that it is generally difficult to validate MSOT measurements in humans due to the absence of a goldstandard method to visualize tissue oxygenation and deoxygenation. Initial experience with MSOT instead points to opto-acoustics becoming the gold standard in tissue oxygenation studies, after further validation of the technique in phantoms and tissues. We have demonstrated portable fast-MSOT ability to study physiological parameters with depth, after exercise. The muscle depth imaged herein was ∼1–2 cm. High-resolution MSOT at video-rate imaging was used in conjunction with a segmentation algorithm to automatically extract these parameters from an imaging sequence of more than 300 images per volunteer. The
April 1, 2015 / Vol. 40, No. 7 / OPTICS LETTERS
segmentation enabled the comparison of the relative oxygenated and deoxygenated hemoglobin in both muscle, and also skin, the latter employed herein as a reference measurement. This performance is already better than NIRS, since NIRS cannot resolve depth and cannot achieve the depth resolution of MSOT. By mixing contributions from multiple layers, NIRS may offer only partial contributions of the underlying hemodynamics. As a next step, the robustness of tracking unique tissue groups requiring validation under higher movement conditions will be examined, i.e., measurements from a contracting muscle. Overall, we expect that MSOT may be useful as a clinical tool, particularly in applications related to physical therapy, where diagnostic of blood oxygenation level in the muscle is of importance. References 1. M. Kramer, C. Dehner, E. Hartwig, H. U. Völker, J. Sterk, M. Elbel, E. Weikert, H. Gerngroß, L. Kinzl, and C. Willy, Eur. Spine J. 14, 578 (2005). 2. V. Quaresima, R. Lepanto, and M. Ferrari, J. Sports Med. Phys. Fitness 43, 13 (2003).
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3. A.-P. E. Rissanen, H. O. Tikkanen, A. S. Koponen, J. M. Aho, H. Hägglund, H. Lindholm, and J. E. Peltonen, Front. Physiol. 3, 1 (2012). 4. A. Messere and S. Roatta, Physiological Reports 1, e00179 (2013). 5. L. Arakaki, V. Ntziachristos, B. Chance, J. S. Leigh, and J. C. Schotland, Biomedical Optical Spectroscopy and Diagnostics, OSA Trends in Optics and Photonics (Optical Society of America, 2000), paper TuD6. 6. R. S. Richardson, L. R. Frank, and L. J. Haseler, International Journal of Sports Medicine 19, 182 (1998). 7. A. Dima and V. Ntziachristos, Opt. Express 20, 25044 (2012). 8. A. Buehler, M. Kacprowicz, A. Taruttis, and V. Ntziachristos, Opt. Lett. 38, 1404 (2013). 9. A. Buehler, E. Herzog, D. Razansky, and V. Ntziachristos, Opt. Lett. 35, 2475 (2010). 10. A. Dima, N. C. Burton, and V. Ntziachristos, J. Biomed. Opt. 19, 036021 (2014). 11. M. Xu and L. V. Wang, Phys. Rev. E 71, 016706 (2005). 12. B. Cox, J. G. Laufer, S. R. Arridge, and P. C. Beard, J. Biomed. Opt. 17, 061202 (2012). 13. A. Buehler, A. Rosenthal, T. Jetzfellner, A. Dima, D. Razansky, and V. Ntziachristos, Med. Phys. 38, 1694 (2011).
OPTICS LETTERS / Vol. 40, No. 7 / April 1, 2015
Multispectral opto-acoustic tomography of exercised muscle oxygenation Gael Diot,1 Alexander Dima,1 and Vasilis Ntziachristos1,2,* 1
Institute for Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg 85764, Germany 2 Chair for Biological Imaging, Technische Universität München, Ismaninger Str. 22, München 81675, Germany *Corresponding author: [email protected] Received December 12, 2014; revised February 12, 2015; accepted March 3, 2015; posted March 5, 2015 (Doc. ID 230662); published March 27, 2015 Unlike near-infrared spectroscopy, multispectral opto-acoustic tomography (MSOT) has the potential to offer highresolution imaging assessment of hemodynamics and blood saturation levels in muscle. However motion artifacts impede the real-time applications of the technique. We developed fast-MSOT with motion tracking that reduces motion artifacts. We used this algorithm to follow blood oxygenation level changes associated with muscle exercise in the muscle and the skin of healthy volunteers. © 2015 Optical Society of America OCIS codes: (110.5120) Photoacoustic imaging; (110.4234) Multispectral and hyperspectral imaging; (170.2655) Functional monitoring and imaging; (170.3880) Medical and biological imaging. http://dx.doi.org/10.1364/OL.40.001496
Near-infrared spectroscopy (NIRS) has been extensively used to study muscle physiology during exercise [1–3]. Near-infrared light enables sensing of oxygenated and deoxygenated hemoglobin that are essential tissue chromophores indicative of oxygen utilization, metabolism, and muscle function. Complications arising from photon scattering and absorption, however, limit accurate quantification of hemodynamics and have restricted wide dissemination of NIRS technology. NIRS measurement is strongly affected by skin vascularization [4] and can provide misleading results. NIR imaging and tomography has improved the quantification of oxygenation levels [5], but is limited by low resolution, which also compromises quantification. Magnetic resonance imaging (MRI) has been used to perform high-resolution maps of the exercised muscle, but it requires expensive dedicated infrastructure that is not appropriate for handheld use. The long acquisition times may further prevent the use of MRI to assess quick oxygenation changes [6]. Multispectral opto-acoustic measurements can be offered in a portable and cost-effective format. In addition, opto-acoustic imaging does not have a strong dependence on photon scattering by tissues and therefore may be better suited to assess muscle physiology over optical methods. In the context of clinical imaging, multispectral opto-acoustic tomography (MSOT) has shown tissue penetration of several cm in human volunteers [7], implying the ability to assess superficial muscles in the human body. Moreover, the higher spatial resolution achieved by opto-acoustics over NIRS technology may improve measurements from specific tissue components by computing signals corresponding to better defined volumes. Another relevant MSOT advantage is the operation as a fast, video-rate modality [8,9]. Using parallel data acquisition and high pulse-energy laser sources able to tune up to 50 different wavelengths per second, MSOT images can be collected at 5–10-Hz rates. At such fast frame acquisition capacity, the images are largely motion-artifact free and ideal for handheld mode muscle imaging. Nevertheless, for multispectral imaging of physiological changes under exercise, some motion may still be evident on images acquired sequentially at different wavelengths. 0146-9592/15/071496-04$15.00/0
Such motion can limit the resolution and quantification in spectral unmixing operations. In a previous study, MSOT was applied to functional imaging of blood vessels [8], wherein monitoring oxygenation over time required the imaged subject and the imaging device to be fixed in order to avoid motion artifacts. Moreover, oxygenation was measured in vessels within the skin, i.e., rather superficially. To enable measurement of muscle physiology in handheld mode in humans, it was important herein to avoid immobilizing either the tissue or the device. Motion-free imaging and oxygenation measurements of dermal vasculature can be achieved by bringing an MSOT sensor in physical contact with the skin, virtually locking the underlying vasculature in relation to the ultrasound detector. Conversely, deeper seated structures, such as muscle, cannot be immobilized in relation to a handheld detector. In this Letter, we investigated the feasibility of handheld MSOT to image the muscle and study muscle hemodynamics under exercise. We combined fast per-pulse wavelength tuning technology with a motion-compensation approach that enables bulk physiological readings from muscle, offering an alternative to NIRS. Fast-MSOT was employed in handheld mode to monitor oxygenation changes in the muscle of healthy volunteers, following an exercise protocol. MSOT illumination was provided by a tunable pulsed laser (Spitlight 600 OPO, Innolas Laser GmbH, Germany) capable of wavelength tuning (675–1064 nm) on a per pulse basis at 10-Hz repetition rate. Light was coupled into a custom-made fiber bundle (CeramOptec GmbH) with rectangular output size of 40by-1 mm2 so as to create a line illumination. For detection, a multi-element piezoelectric transducer was used with 64 elements placed in a half arc of 80-mm diameter and central frequency of 5 MHz, details can be found in [10]. The transducer was enclosed in an optically and acoustically transparent low-density polyethylene membrane, creating a cavity that was filled with water for acoustic coupling. Signal acquisition was performed with a custom-built analog-to-digital 64-channel converter acquiring in parallel at a sampling rate of 40 MS∕s and 12-bit digital resolution. To enable real-time feedback © 2015 Optical Society of America
April 1, 2015 / Vol. 40, No. 7 / OPTICS LETTERS
during imaging, a delay and sum image reconstruction was implemented on a graphics processing unit (GPU) that rendered frames at 10 Hz. The Vastus Lateralis muscle was measured in three volunteers, using MSOT at 750, 800, and 850 nm. The rest state was measured by MSOT in all volunteers over 30 s, shortly prior to exercise. MSOT acquired cross-sectional images of the Vastus Lateralis, shown in Fig. 1 (see Media 1). Then, the volunteers exercised on a stationary bike, performing steady pedaling in order to enter the aerobic cycle of the muscle. The volunteers maintained constant cycling speed of 60 rpm for 14.5 min, followed by 30 s of increased cycling rate at 100 rpm. At the end of the exercise, MSOT acquired cross-sectional images of the Vastus Lateralis, over 90 s in a similar placement as the one used for the measurements at the rest state. To ensure repeatable placement of the MSOT head onto the same position, we marked the outline of the MSOT head placed on the skin with a pen. To account for laser intensity variations, a light sensor was placed at the out-
Fig. 1. (a) Gray’s anatomy of the human body figure of the right leg, (b) optical absorption intensity image of the Vastus Lateralis at 800 nm, at a resolution of 200 μm, (c) segmented and selected groups of muscle (red) and skin (orange) from the image presented in (b), (d) and (e) pictures of the MSOT handheld device, on a table and in application on the Vastus Lateralis, (f) and (g) extracted data from the skin and the muscle, respectively.
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put of the laser beam and collected a small fraction of the pulse incident on tissue. This reference measurement was used to correct (normalize) MSOT signals at each frame by dividing each opto-acoustic signal collected by the corresponding pulse intensity recorded by the light sensor. Raw MSOT measurements were then processed by applying a bandpass filter from 0.1 to 5 MHz. This operation removed high-spatial-frequency noise and better revealed the muscle mass. Images were reconstructed using numerical model-based inversion [11]. 300 images were reconstructed per exercise protocol. Figure 1(b) shows a typical MSOT image obtained. The image clearly differentiated the muscle from the skin and sub-dermal tissue. A dark interface appears between the muscle and sub-dermal tissue, indicating low optical absorption, suggesting an area of subcutaneous fat and/or the iliotibial band (Maissiat’s band). Physiological parameters were extracted from average signals collected over the muscle area at three wavelengths, in analogy to NIRS measurements. Tissue differentiation and morphology tracking were based on a segmentation algorithm applied herein to avoid manual region-of-interest marking operations. The algorithm automatically separated different tissues based on intensity information. The intensity recorded on the images is dependent on oxygenation-level changes over time and on wavelength, which may affect the segmentation results. To minimize the influence of intensity variations to the segmentation performance, we discretized each image acquired into eight image-intensity values. This discretization step reduced the sensitivity of the algorithm to small intensity variations, for example, variations expected with blood oxygenation-level changes over time. Then, to identify the area of muscle and of skin, a flood fill algorithm was used. Figure 1(c) shows the unique regions of interest identified on the image shown in Fig. 1(b). Essentially, the algorithm allowed for identification of the skin region and of a bulk muscle region allowing an automatic extraction of average intensity values from the two regions. Such task would be otherwise difficult to perform manually due to the large number of images acquired during the measurements. While other segmentation procedures could be applied, this performance was deemed satisfactory for the purpose of the study herein. We visually inspected all processed images to ensure that intensity variations did not affect the segmentation of the muscle mass versus skin. Figures 1(d) and 1(e) show the corresponding images after applying the mask in Fig. 1(c) to the image shown in Fig. 1(b), extracting the skin region and the bulk muscle region, respectively. To extract physiological measurements from the segmented MSOT images, we calculated the mean intensity value of the muscle M λ and skin/sub-dermal tissue S λ for each wavelength λ scanned. To simplify the inspection of the results obtained, we termed the measurements at 750 nm as deoxygenated signal (M deoxy and S deoxy ), since 750 nm is significantly more sensitive to deoxygenated hemoglobin versus oxygenated hemoglobin. We termed the measurements at 800 nm as total blood volume signal (M total and S total ) due to the proximity of the 800-nm signal to the NIR isosbestic point. Finally, we termed the signals at 850 nm as oxygenated signal (M oxy and S oxy ), since the influence from oxygenated hemoglobin on the
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measurements at 850 nm is much stronger that the one from deoxygenated hemoglobin. Figure 2 plots the ratio of oxy- and deoxy-signals, over total blood volume values for muscle and skin. This representation of the data indicates relative changes of the oxy- and deoxy- signals, independent from total blood amount. In particular, Figs. 2(a),2(b) plot the quantities M oxy ∕M total and M deoxy ∕M total , obtained from muscle and Figs. 2(c),2(d) the corresponding, S oxy ∕S total and S deoxy ∕S total for skin. The triangles and circles represent the mean value of the results of all the volunteers, whereas the dotted lines show the standard deviation. The solid lines through the data are the fits using a 4th-degree polynomial equation that yielded root mean square error (RMSE) values below 0.06. The fits assess the general trends of the values plotted. In the muscle, both oxygenated and deoxygenated signals show different values before and after exercise. The deoxygenated blood signal appears higher than baseline immediately after exercise, and it recovers within ∼60 s to baseline measurements. The oxygenated blood appears lower than baseline immediately after exercise and similarly recovers after ∼60 s to baseline measurements. Conversely, the data do not show significant changes over baseline for the skin.
Fig. 2. (a), (b) Relative muscle deoxygenation and oxygenation before and after exercise. The triangles and the circles represent the mean oxygenation and deoxygenation values of the measurements obtained from all the volunteers. The dotted lines indicate the standard deviation and the solid line is the curve fitting with a 4th-degree polynomial equation with a RMSE below 0.06. (c), (d) Corresponding measurements obtained from the skin.
Fig. 3. (a) Blood volume-relative variations over time in the muscle before and after exercise, (b) blood volume-relative variations over time in the skin before and after exercise. Maximum standard deviation of 1% for all the plots.
Figure 3 plots the relative blood volume variations in the muscle and in the skin over time, before and after exercise. The blood volume before exercise remains constant over time. After exercise, the blood volume in the muscle and in the skin is increasing. The results are consistent with the expected aerobic activity in the muscle. The higher value of deoxygenated blood signal immediately after exercise reflects the increased oxygen consumption during the activity, a measurement that is corroborated by the corresponding decrease in the relative oxygenation. The relative blood volume variations after exercise increases over time in the muscle as expected. Indeed, the blood is being chased away from the muscle at every contraction. In the skin, the blood volume also increases as a result of heat and higher heartbeat. The technique can differentiate oxygenation changes in the skin and muscle, since, in contrast to NIRS methods, it accurately resolves depth and separates the signals in the two different layers. Nevertheless, an identified challenge of the method is that the results shown are calculated as average values over skin and muscle areas. Therefore, effects of depthdependent fluence attenuation [12] will be present in the calculated results. These effects are however partially counterbalanced due to the increased sensitivity of the curved detector used in this study toward the center of its radius [13]. Overall, such effects need to be more closely investigated in the future; however, a limitation might be that it is generally difficult to validate MSOT measurements in humans due to the absence of a goldstandard method to visualize tissue oxygenation and deoxygenation. Initial experience with MSOT instead points to opto-acoustics becoming the gold standard in tissue oxygenation studies, after further validation of the technique in phantoms and tissues. We have demonstrated portable fast-MSOT ability to study physiological parameters with depth, after exercise. The muscle depth imaged herein was ∼1–2 cm. High-resolution MSOT at video-rate imaging was used in conjunction with a segmentation algorithm to automatically extract these parameters from an imaging sequence of more than 300 images per volunteer. The
April 1, 2015 / Vol. 40, No. 7 / OPTICS LETTERS
segmentation enabled the comparison of the relative oxygenated and deoxygenated hemoglobin in both muscle, and also skin, the latter employed herein as a reference measurement. This performance is already better than NIRS, since NIRS cannot resolve depth and cannot achieve the depth resolution of MSOT. By mixing contributions from multiple layers, NIRS may offer only partial contributions of the underlying hemodynamics. As a next step, the robustness of tracking unique tissue groups requiring validation under higher movement conditions will be examined, i.e., measurements from a contracting muscle. Overall, we expect that MSOT may be useful as a clinical tool, particularly in applications related to physical therapy, where diagnostic of blood oxygenation level in the muscle is of importance. References 1. M. Kramer, C. Dehner, E. Hartwig, H. U. Völker, J. Sterk, M. Elbel, E. Weikert, H. Gerngroß, L. Kinzl, and C. Willy, Eur. Spine J. 14, 578 (2005). 2. V. Quaresima, R. Lepanto, and M. Ferrari, J. Sports Med. Phys. Fitness 43, 13 (2003).
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3. A.-P. E. Rissanen, H. O. Tikkanen, A. S. Koponen, J. M. Aho, H. Hägglund, H. Lindholm, and J. E. Peltonen, Front. Physiol. 3, 1 (2012). 4. A. Messere and S. Roatta, Physiological Reports 1, e00179 (2013). 5. L. Arakaki, V. Ntziachristos, B. Chance, J. S. Leigh, and J. C. Schotland, Biomedical Optical Spectroscopy and Diagnostics, OSA Trends in Optics and Photonics (Optical Society of America, 2000), paper TuD6. 6. R. S. Richardson, L. R. Frank, and L. J. Haseler, International Journal of Sports Medicine 19, 182 (1998). 7. A. Dima and V. Ntziachristos, Opt. Express 20, 25044 (2012). 8. A. Buehler, M. Kacprowicz, A. Taruttis, and V. Ntziachristos, Opt. Lett. 38, 1404 (2013). 9. A. Buehler, E. Herzog, D. Razansky, and V. Ntziachristos, Opt. Lett. 35, 2475 (2010). 10. A. Dima, N. C. Burton, and V. Ntziachristos, J. Biomed. Opt. 19, 036021 (2014). 11. M. Xu and L. V. Wang, Phys. Rev. E 71, 016706 (2005). 12. B. Cox, J. G. Laufer, S. R. Arridge, and P. C. Beard, J. Biomed. Opt. 17, 061202 (2012). 13. A. Buehler, A. Rosenthal, T. Jetzfellner, A. Dima, D. Razansky, and V. Ntziachristos, Med. Phys. 38, 1694 (2011).
