Asymmetric Sensor
Overview of MetaOx
A unique oxygen consumption monitoring technology instrument, MetaOx is capable of acquiring quantitative measurements of hemoglobin concentration and oxygenation using FDNIRS (Frequency-Domain Near Infrared Spectroscopy) and an index of blood flow using DCS (Diffusive Correlation Spectroscopy). With these parameters and the knowledge of arterial oxygen saturation (from a pulse oximeter), the instrument determines the cerebral oxygen metabolism (CMRO2) index.
Development of the MetaOx technology was funded by an SBIR grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development at NIH and the instrument was developed in collaboration with Prof. Maria Angela Franceschini of the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital and Prof. Arjun Yodh of the University of Pennsylvania.
Notice: Investigational device. Limited by Federal (or United States) law to investigational use. The ISS MetaOx is presently used for research only.
Key Features of MetaOx
Monitors Changes in Metabolic Rate
Fast Measurements Up to 50 Hz
Simultaneous Measurements (FDNIRS & DCS)
How MetaOx Works
MetaOx combines three technologies respectively:
From these measurements the MetaOx determines the cerebral metabolic rate of oxygen extraction, CMRO2.
- FDNIRS, the quantitative frequency-domain near-infrared, capable of providing absolute values of oxy- and deoxy-hemoglobin concentration in tissues
- Pulse oximetry for the measurement of arterial oxygen saturation
- DCS, Diffusive Correlation Spectroscopy, for the measurement of blood flow
Applications of MetaOx
In infants:
- Assess microvascular cerebral blood flow (CBF)
- Development of cerebral metabolic rate of oxygen extraction (CMRO2)
- Blood flow and CMRO2 functional changes
In infants and children after cardiac surgery (when born with heart defects):
- Assess pre-operative hemodynamics
- Monitoring early postoperative changes
- Monitoring the cerebral oxygen metabolism
- Assess cerebrovascular reactivity
- Response to sodium bicarbonate treatment
In adults:
- Measure the impairment of cerebrovascular reactivity in ischemic stroke patients
- Estimate autoregulation and CBF responses in patients with traumatic brain injury and subarachnoid hemorrhage
- Assess cerebrovascular reactivity in response to pharmacologically induced vasodilation in healthy adults and in patients suffering from carotid artery stenosis and/or occlusion
- Risk assessment in patients with steno-occlusive lesions of the internal carotid artery
- Monitor adult patients undergoing carotid endarterectomy
- Assess hemodynamic responses of adults to low-frequency repetitive TMS applications
- Study CBF response variations with age
Product Specifications for MetaOx
Lasers
- NIRS: 660, 690, 705, 730, 760, 785, 810, 830 nm; 5 - 9 mW
- DCS: 850 nm, long coherence length; 50 mW
Detectors
- NIRS: Qty. 4 PMTs, GaAs; computer-controlled gain
- DCS: Qty. 8 APDs; photon counting mode
Acquisition electronics
- NIRS: 4-channel A/D converter
- DCS: 4-channel, 8-channel digital correlator
Sensors
- All Fiber Optic
Computer and Operating System:
- Touch-screen monitor, Windows 11, 64-bit
Power Requirements:
- Universal power input: 110 - 240 V, 250 W
Dimensions (cm)
- 45 x 24 x 44
Weight (kg)
- 19
Product Accessories for MetaOx
Product Resources
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“In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies.” Cheung, C., Culver, J.P., Takahashi, K., Greenberg, J.H. & Yodh, A.G. Physics in Medicine and Biology, 46(8), pp. 2053–2065, 2001, Jul. doi: 10.1088/0031-9155/46/8/302.
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“DCS blood flow index underestimates skeletal muscle perfusion in vivo: rationale and early evidence for the NIRS-DCS perfusion index.” Bartlett, M.F., Oneglia, A.P., Ricard, M.D., Siddiqui, A., Englund, E.K., Buckley, E.M., Hueber, D.M., Nelson, M.D. Journal of Biomedical Optics, 29(02), 2024, Feb.
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“3T 31P/1H calf muscle coil for 1H and 31P MRI/MRS integrated with NIRS data acquisition.” Zhang, B., Lowrance, D., Sarma, M.K., Bartlett, M., Zaha, D., Nelson, M.D. & Henning, A. Magnetic Resonance in Medicine, 91(6), p. 2638–2651, 2024, Jan. doi: 10.1002/mrm.30025.
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“Two-layer analytical model for estimation of layer thickness and flow using Diffuse Correlation Spectroscopy.” Wu, J., Tabassum, S., Brown, W.L., Wood, S., Yang, J., and Kainerstorfer, J.M. PLOS ONE, 17(9), p. e0274258, 2022, Sep.
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“Improving Infant Hydrocephalus Outcomes in Uganda: A Longitudinal Prospective Study Protocol for Predicting Developmental Outcomes and Identifying Patients at Risk for Early Treatment Failure after ETV/CPC.” Vadset, T.A., Rajaram, A., Hsiao, C.-H., Katungi, M.K., Magombe, J., Seruwu, M., Nsubuga, B.K., Vyas, R., Tatz, J., Playter, K., Nalule, E., Natukwatsa, D., Wabukoma, M., Perez, L.E.N., Mulondo, R., Queally, J.T., Fenster, A., Kulkarni, A.V., Schiff, S.J., Grant, P.E., Kabachelor, E.M., Warf, B.C., Sutin, J.D.B. & Lin, P.-Y. Metabolites, 12(1), p. 78, 2022, Jan. doi: 10.3390/metabo12010078.
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“Impact of changes in tissue optical properties on near-infrared diffuse correlation spectroscopy measures of skeletal muscle blood flow.” Bartlett, M.F., Jordan, S.M., Hueber, D.M. & Nelson, M.D. Journal of Applied Physiology, 130(4), pp. 1183–1195, 2021, Apr. doi: 10.1152/japplphysiol.00857.2020.
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“Fluctuations in intracranial pressure can be estimated non-invasively using near-infrared spectroscopy in non-human primates.” Ruesch, A., Schmitt, S., Yang, J., Smith, M.A., Kainerstorfer, J.M. Journal of Cerebral Blood Flow & Metabolism, 40(11), p. 2304–2314, 2019, Nov.
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“Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles.” Quaresima, V., Farzam, P., Anderson, P., Farzam, P.Y., Wiese, D., Carp, S.A., Ferrari, M. & Franceschini, M.A. Journal of Applied Physiology, 127(5), p. 1328–1337, 2019, Nov. doi: 10.1152/japplphysiol.00324.2019.
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“Studies into the determinants of skeletal muscle oxygen consumption: novel insight from near-infrared diffuse correlation spectroscopy.” Tucker, W.J., Rosenberry, R., Trojacek, D., Chamseddine, H.H., Arena-Marshall, C.A., Zhu, Y., Wang, J., Kellawan, J.M., Haykowsky, M.J., Tian, F. & Nelson, M.D. The Journal of Physiology, 597(11), pp. 2887–2901, 2019, Apr. doi: 10.1113/jp277580.
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“The noninvasive simultaneous measurement of tissue oxygenation and microvascular hemodynamics during incremental handgrip exercise.” Hammer, S.M., Alexander, A.M., Didier, K.D., Smith, J.R., Caldwell, J.T., Sutterfield, S.L., Ade, C.J. & Barstow, T.J. Journal of Applied Physiology, 124(3), pp. 604–614, 2018, Mar. doi: 10.1152/japplphysiol.00815.2017.
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“Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects.” Buckley, E.M., Parthasarathy, A.B., Grant, P.E., Yodh, A.G. & Franceschini, M.A. Neurophotonics, 1(1), p. 011009, 2014, Jun. doi: 10.1117/1.nph.1.1.011009.
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“Direct measurement of tissue blood flow and metabolism with diffuse optics.” Mesquita, R.C., Durduran, T., Yu, G., Buckley, E.M., Kim, M.N., Zhou, C., Choe, R., Sunar, U. & Yodh, A.G. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1955), pp. 4390–4406, 2011, Nov. doi: 10.1098/rsta.2011.0232.
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“Combined multi-distance frequency domain and diffuse correlation spectroscopy system with simultaneous data acquisition and real-time analysis.” Carp, S.A., Farzam, P., Redes, N., Hueber, D.M. & Franceschini, M.A. Biomedical Optics Express, 8(9), p. 3993, 2017, Aug. doi: 10.1364/boe.8.003993.
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