“hDOS”: An automated hybrid diffuse optical device for real-time non-invasive tissue monitoring—precision andin vivovalidation

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Atif Yaqub , View ORCID Profile Lorenzo Cortese , View ORCID Profile Mauro Buttafava , Jacqueline Martínez García , View ORCID Profile Caterina Amendola , Talyta Carteano , View ORCID Profile Lorenzo Frabasile , Diego Sanoja Garcia , Claudia Nunzia Guadagno , Tijl Houtbeckers , View ORCID Profile Umut Karadeniz , View ORCID Profile Michele Lacerenza , View ORCID Profile Marco Pagliazzi , Shahrzad Parsa , Tessa Wagenaar , Luc Demarteau , Jakub Tomanik , View ORCID Profile Alberto Tosi , Udo M. Weigel , View ORCID Profile Sanathana Konugolu Venkata Sekar , View ORCID Profile Alessandro Torricelli , View ORCID Profile Davide Contini , View ORCID Profile Jaume Mesquida , View ORCID Profile Turgut Durduran doi: https://doi.org/10.1101/2025.06.03.25328859 Marta Zanoletti a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marta Zanoletti For correspondence: marta.zanoletti{at}icfo.eu M. Atif Yaqub a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for M. Atif Yaqub Lorenzo Cortese a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lorenzo Cortese Mauro Buttafava b PIONIRS s.r.l. , Via Timavo 24, 20124 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mauro Buttafava Jacqueline Martínez García a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caterina Amendola c Politecnico di Milano, Dipartimento di Fisica , Piazza Leonardo Da Vinci 32, 20133 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Caterina Amendola Talyta Carteano d ASPHALION s.l. , Carrer de Tarragona 151-157, 08014 Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lorenzo Frabasile c Politecnico di Milano, Dipartimento di Fisica , Piazza Leonardo Da Vinci 32, 20133 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lorenzo Frabasile Diego Sanoja Garcia d ASPHALION s.l. , Carrer de Tarragona 151-157, 08014 Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claudia Nunzia Guadagno e BioPixS Ltd – Biophotonics Standards , IPIC, Tyndall National Institute , Lee Maltings Complex, T23 HW11 Cork, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tijl Houtbeckers f SPLENDO , Marineweg 5, 2241 TX Wassenaar, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Umut Karadeniz a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Umut Karadeniz Michele Lacerenza b PIONIRS s.r.l. , Via Timavo 24, 20124 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michele Lacerenza Marco Pagliazzi a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marco Pagliazzi Shahrzad Parsa g Hemophotonics s.l., Avinguda Carl Friedrich Gauss 3 , 08860 Castelldefels, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tessa Wagenaar f SPLENDO , Marineweg 5, 2241 TX Wassenaar, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luc Demarteau f SPLENDO , Marineweg 5, 2241 TX Wassenaar, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jakub Tomanik f SPLENDO , Marineweg 5, 2241 TX Wassenaar, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alberto Tosi h Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria , Via Giuseppe Ponzio 34, 20133 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alberto Tosi Udo M. Weigel g Hemophotonics s.l., Avinguda Carl Friedrich Gauss 3 , 08860 Castelldefels, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sanathana Konugolu Venkata Sekar e BioPixS Ltd – Biophotonics Standards , IPIC, Tyndall National Institute , Lee Maltings Complex, T23 HW11 Cork, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sanathana Konugolu Venkata Sekar Alessandro Torricelli c Politecnico di Milano, Dipartimento di Fisica , Piazza Leonardo Da Vinci 32, 20133 Milano, Italy i Consiglio Nazionale delle Ricerche, Istituto di Fotonica e Nanotecnologie, Piazza Leonardo Da Vinci 32 , 20133 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alessandro Torricelli Davide Contini c Politecnico di Milano, Dipartimento di Fisica , Piazza Leonardo Da Vinci 32, 20133 Milano, Italy j Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico , 20122 Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Davide Contini Jaume Mesquida k Critical Care Department, Parc TaulÍ Hospital Universitari. Institut D’Investigació i Innovació Parc TaulÍ I3PT , Plaça Torre de l’Aigua, s/n, 08208 Sabadell, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jaume Mesquida Turgut Durduran a ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , 08860 Castelldefels (Barcelona), Spain l Institució Catalana de Recerca i Estudis Avançats (ICREA) , 08015 Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Turgut Durduran Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Significance A new platform/device is presented that advances hybrid diffuse optical monitors closed to clinical practice, bridging the gap between research-grade optical systems and practical bedside applications. Traditional devices often lack automation, multi-parameter functionality, and operator independence, hence, limiting their usability in demanding clinical environments. By offering automation, user-friendly operation, and overcoming the typical limitations of continuous-wave near-infrared spectroscopy, the hybrid diffuse optical platform (hDOS) provides a more accurate and reliable assessment of both oxygenation and perfusion. This innovation is particularly valuable for monitoring critically ill patients, where precise real-time measurements can directly influence patient management and outcomes. Aim To design, validate, and characterize the platform hDOS that integrates time-domain near-infrared spectroscopy, diffuse correlation spectroscopy, and a pulse oximeter with an automated vascular occlusion test (VOT). The platform aims to support continuous monitoring and the assessment of peripheral microvascular, and metabolic functions in both clinical and field settings. Approach The validation strategy for the hDOS device follows a comprehensive approach that goes beyond conventional optical performance assessments. Rather than solely verifying fundamental system parameters, the evaluation comprises of real-world usability, operator and patient safety, and clinical implementation. The device’s precision and usability were rigorously tested in vivo through test-retest measurements and comparisons with a commercially available device (INVOS 5100C). This was subsequently followed by a seven-month clinical evaluation at Parc Taulí Hospital Universitari. Results The device underwent extensive validation, accumulating over 200 hours of usage across approximately 150 measurement sessions. The hDOS device exhibited two-fold lower inter-subject and intra-subject variability in baseline tissue oxygen saturation compared to the INVOS 5100C. Furthermore, during a a vascular occlusion challenge, statistically significant differences were observed between the two systems across all extracted parameters. Finally, as a proof of concept, hDOS successfully detected differences in the microvasculature between a general mixed ICU patient cohort ( n = 100) and a healthy control group ( n = 37). Conclusions Overall, hDOS device has performed well in both bench-top and realistic clinical applications on patients in vivo . hDOS device provides a unique combination of parameters, available for the first time in a fully automated, self-contained platform. 1 Introduction Near-infrared spectroscopy (NIRS) is a non-invasive optical technique that utilizes light in the range of ∼ 650-950 nm to monitor tissue properties such as local microvascular blood oxygenation (StO 2 ) and blood volume. Over the years, NIRS has evolved as a promising tool for assessing tissue perfusion and metabolism, with various implementations designed to overcome technological limitations. Commercially available NIRS monitors predominantly employ continuous-wave NIRS (CWNIRS), which is cost-effective and widely used in clinical settings. However, CW-NIRS lacks depth discrimination and cannot differentiate between changes in absorption and scattering (i.e., µ a and µ ′ s ). As a result, it provides only relative measurements of hemoglobin concentration. Several adaptations of CW-NIRS, such as spatially resolved spectroscopy (SRS), broadband NIRS, and second derivative NIRS, have been proposed to improve the accuracy of tissue oxygenation indices. 1 , 2 Despite these advancements, CW-NIRS faces challenges related to precision and reproducibility, 3 – 5 making comparisons across studies difficult. Consequently, StO 2 monitors have not achieved routine clinical implementation. To address these limitations, time-domain NIRS (TD-NIRS) has emerged as an alternative. Unlike CW-NIRS, TD-NIRS enables depth-resolved measurements and allows direct quantification of both absorption and scattering coefficients. This capability enhances the accuracy of assessing tissue oxygenation and perfusion. Historically, TD-NIRS systems were complex, bulky, and expensive, restricting their widespread adoption. However, advancements in laser sources, detection methods, and miniaturization have led to the development of compact and cost-effective TD-NIRS devices. 6 – 11 The commercialization of TD-NIRS 7 , 12 has further accelerated its potential for integration into clinical and research applications. A significant advancement in optical monitoring has been the development of hybrid instruments that combine TD-NIRS with diffuse correlation spectroscopy (DCS). While TD-NIRS provides depth-resolved information on tissue oxygenation by quantifying absorption and scattering properties, DCS measures microvascular blood flow through the analysis of dynamic light scattering. This integration allows simultaneous measurements of tissue oxygenation and blood perfusion, offering a comprehensive assessment of microvascular function, addressing the limitation of traditional CW-NIRS. Hybrid NIRS-DCS systems have been applied in neuroscience and preclinical research since the early studies on brain monitoring and animal models 13 – 17 and it has been increasingly adopted in a wide range of clinical scenarios 18 – 27 One prominent use case of NIRS is hemodynamic monitoring in the intensive care unit (ICU). As a non-invasive, bedside tool, NIRS assesses microvascular reactivity and endothelial function in critically ill patients, when combined with a vascular occlusion test (VOT). 28 NIRS has been applied in ICU settings, including monitoring acute respiratory distress syndrome 29 and COVID-19, 30 , 31 weaning from mechanical ventilation, 32 – 37 and evaluating microvascular reactivity in sepsis and septic shock. 38 – 49 Beyond the ICU, NIRS has been utilized in trauma care, 50 – 54 surgery, 55 – 58 anesthesia, 43 , 59 – 62 and severe medical conditions. 63 – 72 NIRS has been utilized not only in the critically ills but also in advancing our understanding of microvascular function in healthy subjects 73 – 80 and athletic performance. 81 – 85 In this work, a detailed characterization and validation of the hDOS device 1 is presented. The hDOS device is a hybrid optical multi-purpose system designed for non-invasive, bedside assessment of microvascular oxygenation and blood perfusion in critically ill patients. The operation and use case of a former version of the hDOS device was previously described in Ref. 86 1.1 Objectives of the device validation A holistic approach has been opted for in the evaluation of the hDOS device, going beyond the commonly reported “evaluation of basic performances” approach utilized for similar hybrid devices. 87 – 90 The typical approach ensures that the basic optical performance metrics are met. In this case, a step further is taken by considering the replication of the systems and their independent operation in clinical settings. This is conceptually illustrated in Fig. 1 , where the corresponding tests and measures implemented to address the problems and needs of the end-user are outlined. Download figure Open in new tab Fig 1 An illustrative overview of the key aspects that has been followed in the deployment of the hDOS device, along with the corresponding measures implemented. Detailed descriptions of how these measures were integrated during the design phase are provided in section 2.1 and their use in a typical protocol is described in Ref. 86 Three key aspects have been addressed in the design and development of the hDOS device: (i) usability, (ii) user and patient safety, and (iii) device characteristics and performances. A typical research system developed by us and other laboratories 19 , 87 , 88 , 91 – 94 rely on extensive training and the experience of the operator for clinical study deployment. Here, the system was provided with user-friendly software for independent clinical use with minimal training. Extensive automated safety checks were implemented, detailed data and probe placement metrics were used to provide front panel and on-screen alerts, and software messages were designed to guide users in understanding the signal, identifying problems, and troubleshooting. The software guides users through the protocol and provides real-time access to both raw and processed data, thereby reducing the complexity of operating the device. Real-time safety measures for both operators and patients have been incorporated through hardware and software tools. Continuous monitoring and quick-response mechanisms are used to maintain safe operation. Quality indicators are also included to alert when the laser is active, environmental light interferes with the signal, or real-time data fitting is not optimal as described in Section 2 . The device has been deployed in the intensive care unit at Parc Taulí Hospital Universitari for seven months. This evaluation was conducted not only for usability and training purposes but also to assess the device performance in an the ICU environment. Tests have been conducted on both tissue-mimicking phantoms and in vivo , in order to test the device precision and to set the basic performances in clinical settings. In particular, the device performance during probe repositioning (test-retest) and its susceptibility to interference affecting the two optical modules (as detailed in the dedicated section) were evaluated, and comparisons were made with a commercially available NIRS device used in the clinics. 2 Methods 2.1 Platform description This platform was developed in a close-knit collaboration in a modular fashion based on the experience accumulated by several partners involved in this project and is illustrated in Fig. 2 . Here we highlight the primary links between the modules and their communication paths to ensure proper operation and include a picture of the device and its accessories in Fig. 3 . 86 Download figure Open in new tab Fig 2 Schematic of the device. 1) TD-NIRS module; 2) DCS module; 3) Pulse oximeter module connected to the SpO 2 sensor; 4) noninvasive blood pressure module (NIBP) connected to the properly sized cuff; 5) optical probe and sensor board communicating with two microcontrollers ( µ Ctrl 1 and 2); 6) IRF/phantom box; 7) safety board and primary connection with the other primary modules; 8) single board computer with 9) in-house made software running onboard. A Bluetooth module is available on board for remote control. The SBC is also connected with an internal and/or external screen; 10) power management system. TD-NIRS: time domain near-infrared spectroscopy; DCS: diffuse correlation spectroscopy; Pulse ox.: pulse oximeter; 4. NIBP: noninvasive blood pressure; µ Ctrl 1 and 2: microcontrollers 1 and 2; IRF: instrument response function; SBC: single board computer (SBC). Download figure Open in new tab Fig 3 Picture of the hDOS device with its accessories; a) whole platform with main components and a zoom on the LED indicators; b) front and back views of the optical probe together with its smart sensor board embedded within the probe; c) IRF/phantom box. The following paragraphs will detail each module as indicated in the figure, explaining the design choices and features. A thorough description of the use of a previous (yet very similar) version of this device has been published elsewhere. 86 Here, we focus on salient technical details and features that define the hDOS device. 1. Time-domain NIRS, TD-NIRS. The TD-NIRS module (1) is an original equipment manufacturer (OEM) customized version of the NIRSBOX (PIONIRS s.r.l., Italy). 95 TD-NIRS 6 , 96 , 97 employs two pulsed lasers working at 53 MHz, emitting short pulses at 685 and 828 nm (with the duration in the order of ≈100 ps) that are shone into the tissue by means of a bifurcated bundle composed of two 100/140 µ m glass graded index multimode fibers (NA=0.22). The lasers are also coupled with optical attenuators which are commanded via software in order to reach the correct signal-to-noise-ratio (SNR). Laser pulses from each wavelength are injected alternatively into the bundle. The maximum power injected into the tissue is 3.5 and 3.8 mW for 685 and 828 nm, respectively and it is limited by the maximum permissible emission (MPE) by the ANSI Z1136.1-2022 and IEC 6825-2-1:2014. 98 , 99 Furthermore, the TD-NIRS module is equipped with an interlock system that receive a transistor- to-transistor (TTL) signal that originates from the safety board (8) and it allows for controlling the TD-NIRS laser emission. Diffuse light is then recollected by 1 mm graded index core plastic multimode fiber (NA=0.39) placed at 2.5 cm from the source, which is in turn fed into a silicon photomultiplier detector. Wavelength selection is made by a custom-made dual pass band filter. Both source and detector fibres are hosted in the optical probe (5). The distribution of time of flight (DTOF) of photons is reconstructed at each wavelength every second, with an integration time of 500 ms, using a custom digital single photon counting unit. The TD-NIRS module communicates with a single board computer (SBC) (8) to exchange data and time-stamps, as well as other important information such as status from the lasers and other internal controls. Primary communication is via USB 2.0. All the information saved and exchanged with this module are summarized in the Table 5 , available in the Appendix. 2. Diffuse correlation spectroscopy, DCS. DCS (custom developed based on ICFO/HemoPhotonics S.L., Spain modules) 13 , 100 , 101 (2) utilizes long-coherence length ( > 10 m), CW light at 785 nm to quantify the statistics of the diffuse laser speckles as impressed by the movement of red blood cells. 100 , 102 Light at 785 nm is coupled into a fibre splitter (200 µ m core and NA = 0.22) and then fed into two step index 400 µ m core (NA=0.39) fibers. For this particular case, CW light at 785 nm is limited to 28 mW by the ANSI Z1136.1-2022 and IEC 6825-2-1:2014. 98 , 99 For this reason, as already proposed in other approaches, 103 the injection point is split into two injection points, which are located on a arc circumference of 3.5 mm with a radius of 2.5 cm, corresponding to the source detector distance for DCS. This approach allows for doubling the SNR of the measurement. This allows to probe still the same volume while accounting for thermal relaxation. CW light is then collected by two 4.4/125 µ m core/cladding diameter single mode (NA = 0.13) fibers. Since TD-NIRS and DCS measurements are performed simultaneously, each DCS detection fiber is connected to pigtailed bandpass filter (OZ optics Ltd., Canada). Finally, the detection fibers are fed each into a single photon avalanche photo diode whose TTL output is utilised by an hardware correlator to construct the intensity autocorrelation function g 2 ( τ )= / 2 , where t is the measurement time, τ is the lag time and denotes a time average. In this case, DCS quantifies the g 2 over an averaging time of 26 ms, allowing the resolution of the pulsatility of blood flow due to the cardiac cycle. DCS module also allows for a precise synchronization with the TD-NIRS (1) module by an external trigger, a TTL signal (from 1 to 10 Hz), originating from the hardware that calculates the autocorrelation functions. A replica of this signal is available for external connections to eventually synchronize with other clinical monitors and/or data aggregation platforms (Ext. sync.). Finally, the DCS module communicates to the SBC (8) via USB 2.0, to exchange data collected (g 2 , intensity and time stamps). All the information saved and exchanged with this module are summarized in the Table 6 , available in the Appendix. 3. Pulse-oximeter, SpO 2 module. The platform features a pulse oximeter (Medlab GmbH, Germany) (3) that continuously and synchronously calculates the photoplethysmograph signal at 50 Hz (PPG), arterial oxygenation (SpO 2 ), heart rate (HR), and perfusion index at 1 Hz. The module communicates with the SBC (8) via serial communication RS-232. All the information saved and exchanged with this module are summarized in the Table 7 and 8 available in the Appendix. 4. Automated tourniquet, NIBP module. The automated tourniquet (custom-developed by Medlab GmbH, Germany) (4) has been modified to support rapid inflation and deflation rates, recommended for VOT. Cuff pressure data is transmitted to the SBC (8) at 5 Hz, with a maximum recommended inflation pressure of 300 mmHg. The device includes a range of cuff sizes (Medlab GmbH, Germany) designed to accommodate different limb circumferences. These cuffs are fabricated from biocompatible flexible polyurethane and can be reused following disinfection. This module communicate with the computer via RS232 serial communication. All the information saved and exchanged with this module are summarized in the Table 7 available in the Appendix. 5. Probe. The primary objectives in designing the multimodal probe were to ensure comfort, safety, and data quality. In this paragraph, the rationale and methods behind achieving these goals will be explained. A picture of the probe is shown in Fig. 3 b). TD-NIRS and DCS fibers are encased in a durable, 3D-printed black rubber material with a shore hardness rating of 85A. The fibers in the probe head are organized such that the sources, including the bifurcated bundle of TD-NIRS and one DCS source, are grouped together and coupled to the tissue using two right-angle 2 mm prisms. For detection, all the TD-NIRS and DCS fibers are combined into a single bundle, which is then coupled with the tissue by using a right-angle 3 mm prism. Each module source and detector pairs are 2.5 cm far from each other. Due to the geometrical arrangement, filters have been positioned in front of the TD-NIRS and DCS to accurately select the wavelengths of interest, as explained in the previous sections. Each fiber is enclosed in a lightweight vinyl sleeve with a minimal diameter and all are ultimately protected by a meshed sleeve. The arrangement of the source and detector is designed to accommodate a touch sensor (7 x 7 mm x 0.02 inch copper plate) for detecting contact, as well as a force sensing resistor (FSR400) for monitoring pressure changes exerted by the probe on the tissue. Both capacitive and force sensor terminations are connected through coaxial cables to their respective sensing chip and feedback circuit embedded on a custom printed circuit board placed on the probe head. In addition to a contact and force sensor, a 3-axis accelerometer and a photodiode have been included. On the back of the probe head, a smart sensor board (12 x 21 mm) is enclosed, which transmits data to a microcontroller ( µ Ctrl 1) via I2C communication protocols. The µ Ctrl 1, based on a pre-set threshold on the touch sensor, sends a TTL signal to the safety board (7) to indicate the probe attachment to the tissue. The µ Ctrl 1 also transmits data to a second microcontroller ( µ Ctrl 2), which then communicates with the SBC (8). The data received from µ Ctrl 2 are utilized by the software to set user and device alerts. The use of two sequential microcontrollers is aimed at minimizing the possibility of accessing the sensor board and overriding the safety signals. Due to the presence of signal and power lines, an electrical isolator is positioned along the probe to decouple the patient from the device. Additionally, a reset switch has been implemented, which must be pressed if probe detachment is detected for more than 10 s, in compliance with the latest safety standard IEC 60601-2-22:2019. 104 If the switch is not pressed laser emission is not enabled. Collectively, these and other measures (see below) taken allow this device to be characterized as being in Laser Class 1C as opposed to Class 3B. Alerts and indicators of signal quality are available to the user and for post processing as shown in Table 7 , available in the Appendix. 6. IRF/phantom box. The platform is equipped with a smart box that facilitates daily instrument response function (IRF) assessment 105 which also includes a durable, solid tissue-simulating phantom (BioPixS Limited, Ireland) for TD-NIRS quality control. The smart IRF/phantom box ( Fig. 3 c) is equipped with an electrical circuit made of strategically placed mechanical switches. When the probe is inserted into the box, these switches are activated, generating a signal that is fed to the on-board safety control unit that in turn alerts the user by activating the “probe attached” LED indicator on the front panel and engages the internal interlocks for the TD-NIRS and DCS lasers. This feature is designed to simulate the attachment to tissue via a contact sensor, which does not function with the phantom. Once the “attachment” is detected, the operator is allowed to acquire an IRF and/or a phantom measurement. The IRF measurement involves acquiring a single DTOF for 1 s, aiming for a photon count rate of 10 6 photons per second for each wavelength. The key IRF parameters, such as the temporal position of its barycenter and full-width-half-maximum (FWHM), are calculated real time by the on board software (9). In particular, the FWHM represents the temporal resolution, while its barycenter reflects the stability of the laser, detection, and acquisition chain, which ultimately impacts the retrieval of the tissue’s optical properties. The phantom utilized in this case, is made of silicone background, with absorption contribution given by carbon black and scattering given by titanium dioxide, with desired optical properties at 685 nm µ a = 0.23 cm −1 ( µ ′ s =12.8 cm −1 ) and at 830 nm µ a = 0.19 cm −1 ( µ ′ s = 9.5 cm −1 ) (Biopixs, s.l., Ireland). The phantom measurement in particular is made of 20 repetitions with acquisition time of 1 s and target count rate of 10 6 counts per second per wavelength. Also, for the phantom measurements the on board software (9) calculates in real time the FWHM, the barycenter as well as the optical properties of the phantom. Throughout the entire validation process, all users, including lab technicians and clinicians, have been guided to acquire an IRF to accurately calibrate TD-NIRS measurements as well as a phantom measurement at each session. The purpose is to assess the day-to-day performance in terms of precision and reproducibility, as well as quantifying potential degradation of the module over time. 7. Safety board. In order to integrate and manage all the safety features from the previously described modules, a dedicated safety board featuring key components such as µ Ctrl 1 and 2 has been implemented. The safety board operates by generating the laser-on signal for both TD-NIRS and DCS based on several conditions: the key on the front panel must be in the “on” position; the user reset button on the probe must be “off”, indicating that the probe is attached and no detachment (monitored by µ Ctrl 1) longer than 10 s has occurred; µ Ctrl 2 must receive the software command to enable the lasers for both systems, subsequently communicating this to the safety board; while using the IRF/phantom box the probe must be inserted properly for the “attachement” to be sensed. If any of these conditions is not fulfilled, the safety board activates/deactivates LEDs placed on the front panel to indicate the necessary action that the user needs to take. This ensures prompt attention to maintain operational safety. 8. Single board computer, SBC. The platform is controlled by an industrial-grade single board computer (SBC-230D N4200, ASRock Industrial Computer Corp. Taiwan, R.O.C.), which includes a built-in touchscreen and keypad. The keypad is programmed for performing manual markings, initiating pre-programmed protocols, and managing initial calibration or self-test procedures. It connects to the µ Ctrl 2, which communicates any keypress to the SBC. The SBC handles critical functions such as running the software, managing communication with all modules for sending commands and receiving data, and ensuring reliable data storage. Additionally, remote operation is enabled through Bluetooth technology. This feature is particularly useful in settings like infectious disease triage. 9. Software. The onboard software is tailored to meet the needs of medical teams. It provides real-time plots of oxy-, deoxy- and total hemoglobin (HbO, HbR and tHb) concentrations, StO 2 , blood flow index (BFI) and quality parameters for TD-NIRS, DCS, and pulse oximeter readings. How these parameters are extracted in real time has been already described in the Ref. 86 Operators can also access a device monitor displaying intensity autocorrelation functions for DCS and DTOFs for the TD-NIRS module. The software suite comprises two applications: one developed in NetBeans IDE 8.2 using Java Development Kit 8 and another in Python 3.11 for Bluetooth communication. Both applications run on the Ubuntu 22.04 LTS operating system. The hDOS device software’s main panel displays time traces and measurements in real-time ( Fig. 4 a)). Users can also monitor safety and quality indicators, such as emission and probe attachment status, to ensure optimal device performance ( Fig. 4 c)). Different actions are available to the operator. Details about the software and the different protocol available are already explained elsewhere alongside with their functions. 86 Download figure Open in new tab Fig 4 Snapshot of the hDOS device user interface: a) The main panel displays real-time parameters such as hemoglobin concentrations (HbO, HbR, and tHb in µ M), StO 2 , SpO 2 (in %), and BFI (in cm 2 /s; b) The available modalities include a clinical monitor (main panel) for parameter display and a device monitor for raw data quality assessment; c) Real- time safety and quality indicators for SpO 2 (OXI), TD-NIRS, and DCS (labeled as TRS and DCS, respectively) are provided. A battery indicator is also included since the device operates on battery power; d) available protocols. HbO: oxygenated hemoglobin; HbR: deoxygenated hemoglobin; tHb: total hemoglobin; StO 2 : tissue oxygen saturation; SpO 2 : peripheral oxygen saturation and BFI: blood flow index. 10. Power management system. The power management is handled by a dedicated board designed to convert and distribute the necessary power from four battery packs, each connected to its respective power management system (RRC Power Solutions GmbH, Germany). These power management systems can seamlessly switch between battery and external power supply. Additionally, the batteries communicate their charge status and other critical information to µ Ctrl 2, ensuring efficient power monitoring and management. The addition of a power management module ensures the device’s usability in challenging settings, such as ICU, where numerous monitors are used simultaneously, and power plugs are not always readily available. Finally, the device has been tested by means of an electrical safety analyzer which is compliant with the IEC 6060-1 standard. 106 An emergency button has been incorporated and strategically placed to be easily accessible on the front panel, allowing for a quick action. This safety feature ensures that all the electrical lines are disconnected, thereby stopping the device operation and preventing potential harm to operator and patients. 2.2 Analysis method for TD-NIRS and DCS and VOT derived biomarkers The retrieval of the parameters of interest in real time are explained in Ref. 86 On the other hand, the parameters derived from the VOT challenge are obtained a posteriori with a semi-automated script developed in MATLAB2021b (The MathWorks Inc., Natick, Massachusetts, USA). VOT-derived parameter calculation The Fig. 5 reports a schematic of the typical response to an ischemic challenge for the StO 2 and BFI parameters. The VOT corresponds to an initial three minute baseline, a three minute arterial occlusion to a pressure exceeding the systolic pressure by 50 mmHg, and a recovery period of five minutes upon cuff release. Download figure Open in new tab Fig 5 Schematic response of a VOT for StO 2 (%) and BFI (cm 2 /s). Black vertical dashed lines represent the inflation and the deflation points. The purple lines represent i) the linear fitting of the first minute of occlusion with its slope being the DeO 2 (%/min) and ii) the linear fitting upon the cuff release, with its slope being the ReO 2 (%/s). The shaded areas represent under the curve in both StO 2 (%·min) and BFI (cm 2 ). The VOT-derived parameters are also highlighted: i) baseline phase for both StO 2 and BFI, ii) deoxygenation rate (DeO 2 ) corresponds to the slope of the linear fitting of the first minute of occlusion, iii) reoxygenation rate (ReO 2 ) is the slope of the linear fitting from the deflation point (corresponding to the minimum StO 2 reached during the occlusion) to the intersect of StO 2 with the second baseline reached in the recovery period and iv) the area under the curve (AUC) StO 2 and AUC BFI is the area calculated from the first to the second intersect of StO 2 with the baseline reached in the recovery period. Starting of the measurement, inflation and deflation points correspond to the black lines. Baseline metabolism assessment The index of local metabolic rate of oxygen extraction for the skeletal muscle (MMRO 2 ) is derived at the baseline by the simultaneous and independent assessment of BFI (oxygen transport to the tissue) along with arterial and microvascular tissue oxygenation (oxygen utilization), without the need for a vascular occlusion. In a steady-state condition, MMRO 2 can be calculated as MMRO 2 ≈[Hb]·(StO 2 -SpO 2 )/ γ SpO 2 · BFI, as derived from the Fick’s law, 13 , 16 , 107 where [Hb] is the hemoglobin concentration in g/dL and γ is the percentage of blood content in the venous compartment. 2.3 Precision of the hDOS device The hDOS device validation has been performed mainly with a focus on the optical modules. The accessory modules, such as the pulse oximeter and the automatized cuff are tested separately by the original equipment manufacturer according to their standard operating procedures. This section focuses on the tests conducted to evaluate the precision of the device, specifically in four areas: (i) variability of key parameters in vivo and on phantom at module level; (ii) the variability observed during probe placement and repositioning, and (iii) the device variability under different detected light levels and finally, (iv) optical interferences during measurements. The following paragraphs will provide an in-depth explanation of the protocols used to address these aspects. 2.3.1 Variability of key parameters at module level As explained in Section 2.1 , point 6 , all users were instructed to acquire a IRF and a phantom measurement prior each session. For each phantom the effective tHb ph and StO 2 ph were computed, using the hemoglobin absorption coefficients at 685 and 828 nm, similar to a previous study. 103 The coefficient of variation (CV) is a typical figure of merit for the assessment of day-by-day variability and it is defined as the ratio between standard deviation(x) and the mean(x) (in %) over the entire campaign, where x is the measurand of interest, such as FWHM, barycenter, optical properties and effective tHb ph and StO 2 ph . In order to simplify the calibration process and improves the overall efficiency of the system, as a proof-of-concept, the first IRF acquired in month 3 was utilized to process all the phantom measurements acquired in that same month. It has been then compared to the standard day-by-day calibration. This comparison was performed using the Wilcoxon signed-rank test with a significance level set at p < 0.05. Regarding DCS, it is not possible to monitor in a robust manner quality parameters by utilizing a solid phantom since being static, only contribute to fixed scattering and do not mimic the dynamic changes essential for evaluating flow or motion metrics. A decision was taken to not measure a liquid phantom at each session to ensure a smoother measurement process. On the other hand, the raw g 2 is carefully analyzed at each in vivo measurement session. The count rate and the β can be used as quality parameters. The β parameter is a constant number related to the number of modes detected. In a typical DCS device, β ≈0.5 and it has to remain stable throughout the whole measurement time. A drop in this parameter might be due to instabilities of the laser, non-coherent light leakage or probe detachment. To test the variability of the DCS module, the CV over the count rate and the β are reported during the baseline. For β the median and the standard deviation are calculated from 60 s of baseline measurements where the raw g 2 is integrated by 10 s. In the same 60 s of baseline, the median and standard deviation of the count rate is obtained in kHz. In order to determing if specific trends are visibile, Spearman’s correlation analyses were conducted for both β and count rate. Additionally, constant user feedback helped us in identifying fiber issues and power losses, which were promptly addressed. 2.3.2 Test-retest variability In order to assess the variability due to probe repositioning, a test-retest experiment has been performed on the brachioradialis muscle of one subject by repositioning the probe five times and acquiring data for about 200 s at 1 Hz for both DCS and TD-NIRS, similar to what is reported in other works by us and others. 95 , 103 Finally, the mean values of StO 2 as well as BFI in the five tests and as well as the intra-test and inter-test variability as CV, have been calculated. 2.3.3 Dependence of precision with respect to absolute values A custom protocol was implemented to assess the device precision and identify small but meaningful alterations in both StO 2 and BFI. This was done by occluding arterial blood flow at increments of 40%, 60%, 80%, and 100% of the limb occlusion pressure (LOP) during both inflation and deflation. The LOP was identified as the point at which the PPG signal vanishes during limb occlusion. For each occlusion interval, lasting 60 s, the CV was calculated for both BFI and StO 2 , over the last 15 s of the inflation or deflation interval. The protocol is shown in Figure 6 . The dependence of the CV on the mean absolute values calculated across the different inflation/deflation periods was then analyzed, along with how CV changes at the various signal levels, as indicated by the count rate (for DCS) or dynamic range (for TD-NIRS), by means of Spearman’s correlation. The dynamic range is calculated as the ratio as the maximum of the DTOF (in number of photons) over the standard deviation of the background. Download figure Open in new tab Fig 6 Schematic of the protocol for the assessment of the sensitivity of the platform in vivo . The cuff is inflated and deflated every 60 s at pressures equal to 40%, 60%, 80% and 100 % of the LOP. LOP: limb occlusion pressure; BSL: baseline. 2.3.4 Optical interference A specific ”quality phase” (QP) has been implemented to continuously assess the quality of TDNIRS, DCS, and pulse oximeter signals throughout the protocol. During this phase, the internal software alternates between switching DCS and TD-NIRS sources on and off to evaluate potential interference and dark light levels. This phase begins with signal equalization for TD-NIRS up to 10 6 counts/s and adjusts DCS signals to the maximum deliverable power. The QP lasts less than two minutes, and its results are stored in data files and displayed to the user via quality indicators that turn red or green based on the outcome ( Fig. 7 ). Download figure Open in new tab Fig 7 Schematic of data quality phase (QP). After an the inital equalization of the TD-NIRS, lasers are switched ON and OFF alternatively for a time interval of 10 s. The first minute correspond to the equalization phase of the TD-NIRS lasers. At the end of the QP, the system flags any quality issues, such as low signal levels, interference between TD-NIRS and DCS, or poor pulse oximeter performance. These results guide operators in ensuring reliable data collection. Additional details about the software and its protocols have been discussed elsewhere. 86 Approximately 10% of the subjects were randomly selected to investigate the influence of TDNIRS and DCS light levels on each other, both in the absence and presence of laser illumination. The background level was measured by integrating the total photon count when no diffuse light was incident on the detector, for both modules, both with and without the counterpart light active. The effect of the counterpart light on the signal was assessed by integrating the photon count with the counterpart light either turned off or on. The influence of a module on the other was then analyzed using the Mann-Whitney test, with a significance level set at p < 0.05. 2.4 Validation for clinical settings 2.4.1 Comparison against commercially available device during a vascular occlusion test The hDOS device was compared with a commercially available INVOS 5100C (Somanetic, Minnesota, USA) on two different muscles: the palmaris longus and brachioradialis . Subject demographics (age and biological sex) and the ATT were collected prior to the measurements. In particular, the ATT for both palmaris longus and brachioradialis muscles was measured by means of a portable ultrasound (ECUBEi7, ALPINION MEDICAL SYSTEMS Co., Ltd., Republic of Korea). The INVOS 5100C and hDOS device were alternately positioned on one muscle or the other of healthy subjects in a randomized order for two subsequent VOTs, with an interval of approximately 30 minutes between each test. For each measurement, the pertinent VOT-derived parameters, as explained previously, were calculated. In order to synchronize the two devices, manual markers were utilized that are then used as a reference in post processing. The hDOS TD-NIRS module operates at 1 Hz, while the INVOS 5100C provides StO 2 values every five to six s at its fastest rate. Therefore, TD-NIRS data were resampled to match the timing of the INVOS 5100C. The responses of the two devices to the VOT, as well as their performance across the two different muscles, were then compared. The CV was used as a metric to assess intra-muscle variability within the two muscles. The statistical difference between the palmaris longus and brachioradialis was evaluated using the Wilcoxon signed-rank test, with significance set at p < 0.05. Additionally, a Bland-Altman plot was employed to visualize and assess whether the StO 2 measurements from the INVOS 5100C are interchangeable with those of hDOS device. 2.4.2 Characterization of the microcirculation: comparison between healthy subjects and general mixed ICU patients The clinical validation aimed to ensure the device’s efficacy and safety in critical care scenarios and it was part of the VASCOVID clinical campaign. Measurements on healthy subjects were conducted at the Institute of Photonic Sciences (ICFO) and were approved by the associated ethical committee (Ref: ICFO _ HCP/2012/1). The clinical protocol carried out at Parc Taulí Hospital received approval from its local Ethics Committee and the Comité d’Investigació amb Medicaments (CEIm) (Ref:2021/3015). The study was conducted in accordance with the Helsinki Declaration of 1975, and revised in 2008. The purpose of this study is to characterize microvascular reactivity in the brachioradialis muscle of patients admitted to the ICU and compare it with healthy subjects. The study includes two different groups: (1) Adult healthy volunteers with no previous history of disease that can affect blood circulation, recruited on a voluntary basis; (2) adult general ICU patients, including septic and non-septic patients. Exclusion criteria for the ICU patients included the presence of venous thrombosis in the upper limbs, as well as hematomas or skin lesions on the forearm that could interfere with the placement of the hDOS device’s probe. Hemodynamically unstable patients, defined by uncorrected arterial hypotension or the need for active resuscitation to optimize blood pressure and/or cardiac output, were not included. Participation in the study was voluntary, with informed consent obtained either from the patient or from their legal representative. Due to the exceptional situation of the pandemic from SARS-CoV-19 infection, if the physical access to the patients’ representatives was not possible, informed consent was obtained verbally, by means of telephone conference, with an external witness, not involved in the study. Data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at ICFO. 108 , 109 The vascular reactivity of the subjects was characterized by means of a VOT, as previously described in Section 2.2 , on the brachioradialis muscle, with a pressure applied 50 mmHg above the systolic value. Systolic pressure was measured using a commercial blood pressure monitor on the contralateral arm for the healthy subjects, while in the ICU patients it was obtained by invasive blood pressure monitoring. Finally, the differences between the groups were evaluated by means of Mann-Whitney U test with a significance level of p < 0.05. 3 Results 3.1 Precision of the hDOS device 3.1.1 Variability of key parameters at module level In a period of time of seven months, a total of 59 IRF measurements and corresponding phantom measurements were collected. These measurements correspond to 59 days of measurements corresponding to more than 100 sessions of measurements recorded at the hospital. The CV for the FWHM in the IRF was 2.9% (1.1%) at 685 nm (828 nm), and for the barycenter, the precision was 3.9± 0.02 ns (CV = 0.4 %), and at 828 nm it was 3.6 ± 0.02 ns (CV = 0.6 %). The CV of the FWHM was 2.9 % for 685 nm and 1.1 % for 828 nm. For µ a , the CV was 0.9% (1.0%) at 685 nm (828 nm). For µ ′ s , the CV was 2.2% (2.3%) at 685 nm (828 nm). When calculating the effective tHb ph , the CV was 1.0%, and for StO 2 ph , it was 1.2% over the entire measurement period. The intra-phantom variability was 1.4% for tHb ph and 1.3% for StO 2 . Results for the effective tHb ph and StO 2 ph are shown in the Fig. 8 where they have been grouped by month for clarity and a summary of the results is reported in the Table 1 . Each dot represents the mean and the errorbars the standard deviation over 20 consecutive repetitions. The shaded areas represent the standard deviation over the whole period of time considered. View this table: View inline View popup Download powerpoint Table 1 Coefficient of variation (CV %) calculated across all measurement sessions recorded in seven months for both the full-width-half-maximum (FWHM) of the IRF and the optical properties phantom ( µ a in cm − 1 and µ′ s in cm − 1 ). For the barycenter the standard deviation across all measurement sessions is reported, in ns. A total of 59 IRF and phantom measurements were collected during this period. The µ a in cm − 1 was employed to calculate the effective tHb ph and StO 2 ph . Download figure Open in new tab Fig 8 Effective StO 2 ph in %(top panel) and tHb ph in µ M (bottom panel) obtained by the solid phantom for 59 days of measurements, grouped by month for clarity. The dots represent the average values while the error bars represent the standard deviation over 20 repetitions. The shaded areas represent the standard deviation over all the measurements. Download figure Open in new tab Fig 9 Top panel: β parameter obtained for seven months worth of measurement. The dots represent the average values over 60 s of baseline, with averaging time of 10 s, while the error bars represent the standard deviation. Botton panel: count rate for DCS in kHz for each subject admitted in the clinical campaign. Dots represent the mean values, while errorbars the standard deviation over 20 s of baseline measurement. The green shaded area correspons to the standard deviation over all the measurements. When fitting each phantom with the first phantom of the month (e.g., all phantom measurements acquired in month 3 are fitted with the first IRF acquired in month 3), we obtained a higher, but not statistically significantly different, variability in the retrieval of µ a (CV = 2.8% for both 685 and 828 nm; p = 0.52) and µ ′ s (CV = 2.5% for both 685 and 828 nm; p = 0.75). This higher variability translated to a CV = 2.8% in the effective tHb ph and a CV = 1.0% in the StO 2 ph . More details about the variability on the optical properties of the phantom are shown in Appendix 6.1. The photon count rate and β parameter were analyzed for N = 137 subjects. The photon count rate was subject-dependent and showed a weak positive trend (Spearman’s correlation, ρ = 0.3, p = 0.03). Since in vivo conditions vary across subjects, the recruitement of subjects with better signal might have contributed to this weak positive trend. The β parameter had an average value of β = 0.49 ± 0.01 with a CV = 1.9%, recorded in seven months worth of measurements. No significant trends were observed for β over time (Spearman’s correlation, ρ = 0.2, p = 0.06). This result suggests that there are no degradation in the system over the time of measurements at the hospital. Further details about DCS quality parameters are available in Appendix 6.1. 3.1.2 Test-retest variability As a test-retest evaluation, the hDOS device’s probe was replaced five times onto the brachioradialis muscle of a healthy female subject (32 years old). The results are summarized in Fig. 10 . The mean values of StO 2 and BFI across the five tests are reported. An inter-repetition CV of 1.2% and CV of 12.6% were obtained for the entire experiment. The within-test variability was found to be CV < 0.8% for StO 2 and CV < 15.2% for BFI and it was similar to what was found in Ref., 103 although StO 2 was better with hDOS (1.2% reported here compared to 8.5% in Ref. 103 ). Download figure Open in new tab Fig 10 a) StO 2 values obtained over 5 repositioning. The dots represent the mean values, while the errorbars are the standard deviations calculated over 200 acquisitions. The shaded area represent the standard deviation, centered around the mean over the 5 tests. 3.1.3 Dependence of precision with respect to absolute values Seven (N=7) healthy subjects were enrolled to evaluate the variability at different light levels of both StO 2 and BFI. In Fig. 11 an example is reported for a single subject. As displayed in Fig. 12 , it can be observed that for both BFI and StO 2 , the CV was dependent on the mean value retrieved during the various periods of inflation/deflation, with higher CV observed during deflation for both StO 2 and BFI (panels a and c, respectively). The CV BFI exhibited similar behavior, showing smaller variability around higher count-rates (panel b). As expected, CV StO 2 decreased with higher dynamic ranges of the acquired TD-NIRS curves (panel d). A statistically significant correlation was obtained between the CV BFI and count rate ( ρ = -0.41, p = 0.001). No statistically significant correlation was found between count rate and the increase in BFI ( ρ = -0.09, p = 0.46), nor between CV BFI and BFI ( ρ = -0.19, p = 0.15). For StO 2 , a statistically significant correlation was found between CV StO 2 and the dynamic range (DR) at 685 nm ( ρ = -0.5, p < 0.001), as well as with StO 2 ( ρ = -0.33, p = 0.007). Finally, no statistically significant correlation was found when comparing StO 2 and the dynamic range at 685 nm ( ρ = 0.08, p = 0.5). Download figure Open in new tab Fig 11 Example of VOT-LOP protocol for tissue oxygen saturation (StO 2 in % and bloof flow index (BFI in cm 2 /s). The cuff was inflated and deflated in step of 40,60,80 and 100 % of the LOP, with occluding pressure maintained for 1 minute each time. Mean value, standard deviation and CV are calculated in the last 15 s of occlusion for each interval. Download figure Open in new tab Fig 12 Coefficient of variation (CV in %) of blood flow (BFI) with respect to a) the mean values of BFI (in cm 2 /s) and b) to the count rate (in kHz), retrieved during the inflation/deflation periods; CV of tissue oxygen saturation (StO 2 ) with respect to c) the mean value of StO 2 (in %) and d) the dynamic range (DR) of the wavelength 685 nm. 3.1.4 Optical interference Results for the assessment of the presence of optical interference, is reported in Fig. 13 . Download figure Open in new tab Fig 13 Boxplots illustrating the influence of the DCS signal on the TD-NIRS total background count rate (TD-NIRS OFF) when the DCS was ON or OFF for wavelength 685 (panel a) and 828 (panel c) nm; b) influence of the DCS signal on the total count rate of the TD-NIRS module (TD-NIRS ON) when the DCS was ON or OFF for the wavelengths 685 (panel b) and 828 (panel d). Influence of the TD-NIRS signal when the TD-NIRS lasers are either ON or OFF, when the DCS lasers was either OFF (panel e) or ON (panel f). A statistically significant difference is depicted by “*” when p < 0.05. The background count rate for the TD-NIRS was significantly higher when the DCS laser was emitting at full power (p < 0.001 for both 685 and 828 nm). On the other hand, the background count rate of the DCS is not affected by the TD-NIRS lasers shining (p=0.48). Finally, a difference in the count rate when the TD-NIRS lasers are emitting and the DCS was either ON or OFF has not been found (p=0.15 for 685 nm and p=0.60 for 828 nm). Similar results are obtained when the DCS laser is emitting, and the TD-NIRS lasers are switched ON and OFF (p=0.31). The results suggest that the filters are functioning as expected, effectively preventing significant interference. The increase in background count rate when the DCS was ON, though statistically significant, was minimal and spread across the entire temporal window. Despite this increase, the system’s performance remains uncompromised, as sufficient dynamic range was maintained to reliably fit the DTOF and extract robust optical properties from the signal. A representative result is shown in Figure 14 where the StO 2 and the BFI of a single patient is reported during the automatic QP. In this particular case, the CV for the BFI was 7.5% when the DCS laser was ON while the TD-NIRS lasers were OFF. Conversely, when the TD-NIRS module was ON and the DCS was OFF, the CV for the µ a and µ ′ s was less than 1% at both wavelengths, with a resulting CV for StO 2 at 0.6% and for tHb at 0.5%. When both the TD-NIRS and DCS modules were ON, the CV for BFI remained at 7.5%, while the CV for StO 2 and tHb increased slightly to 0.8%, and the CV for µ a and µ ′ s was less than 1.2% at both wavelengths. Download figure Open in new tab Fig 14 Example of StO 2 (in black solid line) and BFI (in orange solid line) time traces during the quality phase. Dashed vertical llines denote the changes in ON/OFF cycles. A grey shaded area represents the equalization phase for TD-NIRS to reach the desired count rate 10 6 counts/s. 3.2 Validation for clinical settings 3.2.1 Comparison against a commercially available device A total of ten subjects (six females) with a mean age of 28±5 years old and a BMI of 24.9±2.3 kg/m 2 were included in this study. In addition, the ATT over the two measured muscles was 4.0±0.1 mm for the brachioradialis and 4.4±0.1 mm for the palmaris longus which were not statistically significantly different from each other (p=0.67). An example of hDOS device and INVOS 5100C time traces is reported in the Fig 15 . The response to the ischemic challenge was lower in the hDOS device. Moreover, the StO 2 in the INVOS 5100C was limited to a maximum value of 95% and a minimum value of 15%, which makes the extraction of the area under the curve unreliable (see the zoomed area in Fig 15 ). A summary of the VOT-derived parameters, their medians and interquartile ranges are shown in Table 2 for both devices and muscles. Download figure Open in new tab Fig 15 Example of the time trace of a simultaneous measurement of hDOS device (orange line resampled at the INVOS 5100C sampling time) and INVOS 5100C (blue line). The dashed lines represent the start of the measurement, after the data quality phase, inflation and deflation time. The StO 2 hyperemic peak is highlighted in the zoomed window. View this table: View inline View popup Download powerpoint Table 2 Comparison of hDOS TD-NIRS and INVOS 5100C values obtained for the brachioradialis and palmaris longus during the VOT. Values are median and first and third interquartile range values in square brackets. The symbol “*” represents statistically signficant difference between brachioradialis and palmaris longus ; The symbol “†” represents statistically significant difference between the hDOS TD-NIRS and INVOS 5100C. In both cases the significance was set for p < 0.05. The hDOS TD-NIRS StO 2 presents a smaller interquartile range, particularly in DeO 2 and ReO 2 . Concerning intersubject variability, a CV of 3.3 (5.0)% for hDOS device in the brachioradialis ( palmaris longus ) was obtained, compared to 6.1 (9.6)% for INVOS 5100C. Additionally, when intrasubject variability was considered, an average of 0.4 (0.6)% was found for hDOS device versus 0.8 (1.0)% for the brachioradialis ( palmaris longus ) in INVOS 5100C, respectively. Statistical differences in the positioning of the probe were not observed for any of the variables retrieved by INVOS 5100C. However, differences were found in the DeO 2 (p = 0.004) and ReO 2 (p = 0.009) variables between the brachioradialis and palmaris longus when examining TD-NIRS-hDOS. In the brachioradialis , differences were noted in the StO 2 baseline (p = 0.02), as well as in DeO 2 (p < 0.001) and ReO 2 (p < 0.001) when comparing TD-NIRS and INVOS 5100C. No significant differences were found in the AUC StO 2 (p = 0.61). In the palmaris longus , differences were observed only in DeO 2 and ReO 2 (p < 0.001). In Fig. 16 a Bland-Altman plot is reported where for each subject (in different colors), the differences between INVOS 5100C and hDOS StO 2 are plotted against their average values at each time point of the VOT. The black solid line correspond to the bias, while the black dashed lines correspond to ±1.96 times the standard deviation. A bias of +2.14% is reported which is not representative in this case since there is a non-zero slope (R = 0.72, p < 0.001) confirming that the two devices differ from each other in a non-trivial manner. Furthermore, this difference is not subject dependent. In fact, all subjects display a significant non-zero slope. Download figure Open in new tab Fig 16 Bland-Altman plot for StO 2 as measured by INVOS 5100C (indicated as INVOS) and hDOS TD-NIRS (indicated as TD-NIRS). Colors represent all the subjects ID included in the study (N=10). Both positions of measurement ( brachioradialis and palmaris longus ) are taken into consideration in the plot. 3.2.2 Characterization of the microcirculation: comparison between healthy subjects and general mixed ICU patients Thirty-seven (N=37) healthy subjects and one hundred (N=100) general ICU patients were recruited. The demographic, clinical, and morphological data are summarized in Table 3 , while the optical data, as well as baseline and median responses to the ischemic challenge, are presented in Table 4 . View this table: View inline View popup Download powerpoint Table 3 Demographic data. Median and interquartile range (25% and 75%) is reported in square brackets. BMI: body mass index; ATT: adipose tissue thickness; MAP: mean arterial pressure; SpO 2 : peripheral tissue oxygenation; HR: heart rate; N/A: information not available or not applicable. (*) indicates difference between the general mixed ICU and healthy population with a significance level p < 0.05 according to Wilcoxon rank-sum test. View this table: View inline View popup Download powerpoint Table 4 Optical data obtained for the three subjects’ cohorts. The mean values and standard deviations (mean ± std. dev) of tHb, StO 2 , BFI, OEF and MMRO 2 are calculated over 30 s prior to the occlusion interval. Median and interquartile range (25% and 75%) of DeO 2 , ReO 2 , AUC StO 2 and AUC BFI are reported in square brackets. (*) indicates difference between the general mixed ICU and healthy population with a significance level p < 0.05 according to Wilcoxon rank-sum test. It has been found statistically significant difference between the general ICU and healthy groups in age, BMI, arm circumference, as well as SpO 2 and HR at baseline (p < 0.001). MAP also showed a signficant difference between the two groups (p=0.02). Significant differences were found in µ ′ s at both wavelengths (p < 0.001). Healthy group showed a significantly higher StO 2 with respect to the general mixed ICU group (p < 0.001). A significant difference in BFI was detected (p=0.03) with higher values in the healthy group. MMRO 2 was found to be significantly reduced in the ICU population (p < 0.001). During the VOT, significantly lower values were identified for the ReO 2 , AUC StO 2 and AUC BFI in the ICU population compared to healthy subjects (all p < 0.001). 4 Discussion In this paper, the hDOS device is introduced as a versatile, multimodal system that integrates advanced TD-NIRS and DCS technologies, along with essential accessories such as a pulse oximeter and an automated tourniquet for vascular occlusion tests and baseline metabolism assessment. The validation of this platform went beyond basic performance metrics, emphasizing its robustness and suitability for independent clinical use. While optimized for intensive care, its adaptability extends to various settings, including laboratory environments, clinics, and more demanding conditions such as the operating room and emergency care unit. The device underwent rigorous testing, with over 200 hours of use across approximately 150 measurement sessions. A user-friendly software improves usability with an intuitive interface, quality feedback, and real-time safety checks to ensure compliance with safety standards. The multimodal probe, equipped with a force sensor, touch sensor, and accelerometer, aids in standardizing probe pressure, improving measurement quality, and enabling data rejection when necessary. The accelerometer detects motion artifacts, while the capacitive touch sensor ensures continuous tissue contact for accurate measurements. 89 , 110 , 111 Deployed in the intensive care unit for seven months 2 , the device underwent evaluations including tests on tissue-mimicking phantoms and in vivo assessments to gauge its stability, reproducibility, and performance in a clinical setting. To note that for some of these assessments a replica device has been utilized. These replicas differ only in having slightly improved and more robust electronics, but are optically identical. Specifically, it has been the focus of the work of Ref. 12 where a comprehensive comparison involving ten TD-NIRS devices based on the same hDOS TD-NIRS technology was presented. They in fact revealed a remarkably high level of reproducibility and accuracy in the retrieval of optical parameters from well-characterized tissue-mimicking phantoms among different replicas, which is promising for the consistency and reliability of TD-NIRS devices manufactured at scale and utilized in this very same platform. In particular, for a similar system, in Ref. 112 authors report a variability better than 1.2% on different types of phantoms and different, bulkier implementations of TD-NIRS technology, with comparable optical properties to the one employed with hDOS device. For hDOS TD-NIRS a variability in tHb ph and StO 2 ph was found to be below 1.2%. The focus was on assessing a day-to-day variability, not on validating absolute values. These results are consistent with findings from Ref., 103 where variability was 3.0% over nearly 10 months. Monthly calibration with the IRF showed less than 3% variability in effective tHb ph and StO 2 ph , despite minor, statistically insignificant variations in optical properties ( µ a and µ ′ s ). DCS stability, well-documented in Refs., 18 , 20 , 21 , 88 , 91 , 113 – 115 was confirmed by monitoring count rate and β parameters in in vivo measurements. No significant trends were observed, indicating stable performance during data acquisition. The precision of the hDOS device was evaluated through test-retest and precision analysis concerning absolute values. In a test-retest study on a single subject, TD-NIRS results were consistent with literature for both TD-NIRS 95 , 116 and DCS. 87 , 117 The CV for StO 2 with hDOS was notably lower at 1.2% compared to 8.5 % reported previously 103 and also superior to MOXY (Fortiori Design LLC, Minnesota, US) and Portamon (PortaMon, Artinis, Medical System, The Netherlands) devices, which reported CVs of less than 2.5%. 118 The hDOS TD-NIRS shows a better precision with respect to a novel commercially available wearable device (Train.Red FYER) where a CV of 5% was reported. Regarding the evaluation of the precision with respect to absolute values, CV was found to be dependent on light levels for both StO 2 and BFI. During dynamic phases, such as deoxygenation and reoxygenation, a lower StO 2 resulted in a reduced SNR and increased CV. Future work will aim to improve precision across all measurement ranges using real-time optimization algorithms and fast tunable filters. Despite this, the precision was better than 4% across the StO 2 range. BFI variability increased during full arterial occlusion, but this does not impact measurements as BFI during occlusion only confirms ischemia. Evaluating in vivo variability, especially in skeletal muscle, remains complex and underexplored compared to brain studies. Precision studies in tissue mimicking phantoms and in vivo provide a valuable benchmark when translating the usefulness of these technologies to clinical applications. On the other hand, it is also critical to assess how it compares to a less expensive commercially available device, such as the the INVOS 5100C. The INVOS 5100C, like the hDOS TD-NIRS system, uses near-infrared light for tissue oxygenation assessment but relies on the so-called SRS algorithm/probe. 1 , 2 While valued for its cost-effectiveness and ease of use, it only measures tissue oxygenation without providing direct indicators of perfusion and metabolism. Variability in findings from NIRS-VOT studies 119 underscores the challenge of uniform standards, an issue addressed in the in vivo validation of the VASCOVID project. The key difference between these technologies lies in their assumptions: the INVOS 5100C assumes light attenuation is primarily due to absorption and that scattering depends linearly on wavelength, 120 , 121 deriving a tissue saturation index. In contrast, the TDNIRS system in the hDOS device calculates absolute absorption values without such assumptions, overcoming limitations of light penetration depth by adjusting injected power within safety limits. Literature consensus supports that TD-NIRS offers superior accuracy, precision, and repeatability compared to CW-NIRS methods. 97 , 122 This suggests that clinical systems like the INVOS 5100C may provide less accurate data, especially during vascular occlusion tests. While definitive in vivo comparisons are challenging due to varying algorithms and lack of gold standards, hDOS TD- NIRS demonstrated greater consistency with lower intersubject and intrasubject variability when comparing baseline StO 2 to INVOS 5100C. Statistical differences were observed in VOT-derived parameters when comparing the brachioradialis and palmaris longus positions for INVOS 5100C but not for hDOS TD-NIRS. hDOS TD-NIRS showed distinctions in DeO 2 and ReO 2 between probe positions. Specifically, in the brachioradialis , differences in StO 2 baseline, DeO 2 , and ReO 2 were noted, while in the palmaris longus , differences were seen in DeO 2 and ReO 2 . A Bland-Altman plot analysis revealed a 2.1% which was not representative due to a non-zero slope. This highlights that the two devices cannot be used interchangeably and bias cannot be corrected by using a simple correction factor. Finally, a total of 37 healthy young subjects and 100 general mixed ICU patients were recruited for the clinical validation where a three minutes VOT was performed. Literature suggests that fixed time thresholds could introduce variability in ReO 2 changes. 123 On the other hand, due to the slower deoxygenation rate observed with hDOS, achieving a consistent 40% StO 2 , as suggested, 123 threshold upon cuff release was challenging. This device offers a precise automatized VOT protocol thereby reducing any additional variability due to operator. Optical and hemodynamic properties reported are similar to those in Ref. 116 for the brachioradialis muscle. The hDOS device successfully differentiated healthy microcirculation from impaired states in the general mixed ICU group, particularly in the baseline StO 2 , BFI and MMRO 2 . Also VOT-derived parameters showed differences in parameters related to microvascular reactivity such as ReO 2 , AUC StO 2 and AUC BFI. These findings align with literature, 29 – 31 , 31 – 45 , 124 – 126 although studies often use the thenar muscle due to its accessibility. The lack of standardized VOT protocols for NIRS technology is another significant concern. Variations in probe positioning, cuff size, inflation pressures, and VOT durations across studies complicate comparisons. 28 , 30 , 43 , 49 , 53 , 57 , 62 , 71 , 103 , 123 , 127 – 137 On the other hand, when comparing our results specifically for the healthy population with what is present in the literature, a high variability is shown in the reported baseline values for StO 2 in healthy subjects. In fact, baseline varies across devices generally ranging between 65% to 87%. Also DeO 2 , ReO 2 and AUC StO 2 differ widely in the reported healthy populations, which reflects both methodological differences (e.g. how the fitting point are chosen) and device sensitivities (e.g. source and detector distance in the probe, technology used, etc.) The hDOS TD-NIRS presented in this work for the healthy group, it shows slower DeO 2 and ReO 2 of what is normally reported. For example, ReO 2 reported in the literature, ranges from ≈1.2 to ≈ 9.5 %/s. A comparison with AUC StO 2 is more complex due to inconsistent reporting. In particular, the variability on ReO 2 and AUC StO 2 are due also to variations in protocols where VOT of 3 minutes or 5 minutes duration are often used. 30 , 44 , 73 , 77 , 130 , 138 – 141 These considerations stress the need of a standardized way of measuring the microcirculation in the healthy and ICU population. The hDOS device demonstrates promising clinical applications. In critical care, it aids in assessing tissue perfusion and oxygenation, monitoring microcirculatory changes, and evaluating endothelial dysfunction, among the others. 5 Conclusion The hDOS device is a hybrid diffuse optical platform that has been developed for application in the critical care, but given its performances it can find potential applications in many other fields, such as operating rooms and emergency care. The platform has been proven to be more accurate than a commercially available device. Data Availability Data will be made available by the corresponding author upon reasonable request taking into account the appropriate norms for personal data privacy. Disclosures The role of all the companies (BiopixS ltd, PIONIRS s.r.l., ASPHALION s.l., SPLENDO, Hemophotonics s.l.) and their employees involved has been defined by the project objectives, tasks, and work packages and has been reviewed by the European Commission (European Union’s Horizon 2020 research and innovation programme, VASCOVID project, grant agreement No. 101016087). At the time of the execution of the project, ICFO had equity ownership in the spin-off company HemoPhotonics S.L. and UMW was the CEO. The company has since ceased to exist. Mauro Buttafava, Michele Lacerenza, Davide Contini, Alessandro Torricelli and Alberto Tosi are cofounders of PIONIRS s.r.l., a spin-off company from Politecnico di Milano (Italy), and Mauro Buttafava is the CEO of the company and Michele Lacerenza, the CTO of the company. All potential financial conflicts of interest and objectivity of research were monitored by ICFO’s Knowledge & Technology Transfer Department. Acknowledgments This work has received funding from: the EuropeanUnion’s Horizon 2020 research and innovation programme under grant agreements No. 101016087 (VASCOVID), No. 101017113 (TINY-BRAINS), No. 871124 (LASERLABEUROPE V) and under the under the Marie Skłodowska- Curie grant agreement No. 101062306, Fundació CELLEX Barcelona, Fundació Mir-Puig, Agencia Estatal de Investigación (PHOTOMETABO, SCOSWEAR, SCOSDET), the “Severo Ochoa” Programme for Centres of Excellence in R&D (CEX2019-000910-S), LUX4MED, Generalitat de Catalunya (CERCA, AGAUR-2022-SGR-01457, RIS3CAT-001-P-001682 CECH), and Secretaria d’Universitats i Recerca del Departament d’Empresa. Appendix 5.1 Data quality 5.1.1 TD-NIRS quality parameters In Fig 17 a), the IRF/phantom box described in Section 2 is shown. If the probe is inserted with the optics facing upwards, the IRF is acquired; otherwise, a phantom measurement is obtained. To monitor the data quality of the TD-NIRS module, the IRF resolution, its shape, and temporal stability must be evaluated through its full-width-half maximum (FWHM) and barycenter. A summary of the quality parameters is presented in Fig 17 b). For clarity, only a portion of the temporal window where the distribution of time-of-flight is reconstructed is highlighted. The TD-NIRS laser is driven at 53 MHz corresponding to an available temporal window of approximately 19 ns wide and a timing resolution of 9.76 ps. For each measurement, one IRF is collected with an integration time of 1 s targeting 10 6 counts per second per wavelength. In Fig 17 c), the results for the FWHM (in ps) and barycenter position in the temporal window (in ns) are reported, with shaded areas indicating the standard deviation calculated over seven months of measurements, for both 685 and 828 nm. For the barycenter here the mean ± standard deviation is reported, alongside the coefficient of variation (CV = standard deviation/mean). For the FWHM only the CV is reported. As mentioned in the main text, the barycenter of the DTOF at 685 nm was 3.9± 0.02 ns (CV = 0.4 %), and at 828 nm it was 3.6 ± 0.02 ns (CV = 0.6 %). The CV of the FWHM was 2.9 % for 685 nm and 1.1 % for 828 nm. In Fig. 18 , the results obtained from fitting for µ a and µ ′ s at both 685 and 828 nm, based on the 31 phantom measurements collected in month 3, are reported. Each point in the distribution correspond to the single phantom repetitions (31 measurements x 20 repetitions). In Fig. 19 , the effective total hemoglobin (tHb ph ) and tissue oxygen saturation (StO 2 ph ) extracted are shown. The distributions of the results, obtained by fitting the convolution of the DTOFs acquired with the corresponding IRF of the day (labeled “day” in the figure), were compared to those obtained by convoluting the DTOFs with the first IRF acquired in that specific month (labeled ”month” in the figure), using the Wilcoxon sign-rank test, with significance set at p < 0.05. A larger CV was found in both µ a (CV = 2.8%) and µ ′ s (CV = 2.5%) for both wavelengths. This translated into a CV of 2.8% and 1.0% in tHb ph and StO 2 ph . However, no statistically significant difference was found when comparing the “day” results to the“month” results for µ a (p = 0.39 for 685 nm and p = 0.59 for 828 nm) and µ ′ s (p = 0.15 for 685 nm and p = 0.19 for 828 nm). Download figure Open in new tab Fig 17 a) smart IRF and phantom box, with the probe optics facing upwards to obtain an IRF. b) An example of an IRF acquired at 685 nm is displayed, with the FWHM and barycenter figure of merit highlighted. c) The graph presents the FWHM and barycenter data for the 59 days of measurements, grouped by month for clarity. The shaded areas represent the standard deviation over all measurements. Download figure Open in new tab Fig 18 µ a and µ′ s fitted at 685 and 828 nm for both the day-by-day (day) and month evaluation (month). Each dot in the distribution correspond to a single repetiton in each phantom measurement. Download figure Open in new tab Fig 19 StO 2 ph and tHb ph for both the day-by-day (day) and month evaluation (month). Each dot in the distribution correspond to a single repetiton in each phantom measurement. 5.1.2 DCS quality parameters To assess the performance of the DCS, the count rate (in kHz) and the β parameters are evaluated. An example is depicted in Fig. 20 , where the intensity of the detected DCS signal at the baseline of a healthy subject and the β parameter are shown. The β parameter, which depends on the number of modes detected, is calculated as the weighted average of the 2nd to the 4th bin of the autocorrelation function g 2 . In this example, a β value of 0.49±0.01 at the baseline is reported. Download figure Open in new tab Fig 20 Left panel: intensity recorded during the baseline of a VOT protocol in an healthy subject. The blue shaded area represents the 10 s of integration time. Right panel: intensity autocorrelation function (g 2 ) as a function of the lag time ( τ ), averaged over 10s of measurement. The β parameter is calculated as the weighted average of the 2nd to the 4th bin of the g 2 ( τ ). 5.2 Data and information saved In this section, we report the list of all the variables that are recorded. Each module communicates with the single board computer (SBC) as described in Section 2 and the list of variables are listed and briefly described in Table 5 for the TD-NIRS module, in Table 6 for the DCS module, in Table 7 for the sensor and safety boards and finally in Table 8 for the PPG module. View this table: View inline View popup Table 5 Data and information stored by the software from the TD-NIRS module. SBC: single board computer; DCS: diffuse correlation spectroscopy. View this table: View inline View popup Download powerpoint Table 6 Data and information stored by the software from the DCS module. SBC: single board computer; DCS: diffuse correlation spectroscopy. View this table: View inline View popup Table 7 Data and information stored by the software from the various modules. SBC: single board computer; DCS: diffuse correlation spectroscopy; TD-NIRS: time-domain near-infrared spectroscopy. View this table: View inline View popup Download powerpoint Table 8 Data and information stored by the software from the SpO 2 module. SBC: single board computer; DCS: diffuse correlation spectroscopy; TD-NIRS: time-domain near infrared spectroscopy. Footnotes ↵ 1 Developed during the VASCOVID project – European Union Horizon 2020 research and innovation, No. 101016087 ↵ 2 We note that the current usage has reached 24 months with over 500 sessions on 410 patients under 12 protocols. We continue to monitor the quality and stability of the device and this will be reported along each particular clinical study results. References 1. ↵ Martin Wolf , Marco Ferrari , and Valentina Quaresima . Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications . Journal of Biomedical Optics , 12 ( 6 ): 062104 , November 2007 . Publisher: SPIE. OpenUrl CrossRef PubMed 2. ↵ Felix Scholkmann , Stefan Kleiser , Andreas Jaakko Metz , Raphael Zimmermann , Juan Mata Pavia , Ursula Wolf , and Martin Wolf . A review on continuous wave functional nearinfrared spectroscopy and imaging instrumentation and methodology . NeuroImage , 85 : 6 – 27 , January 2014 . OpenUrl CrossRef PubMed Web of Science 3. ↵ J. C. Hirsch , J. R. Charpie , R. G. Ohye , and J. G. Gurney . Near infrared spectroscopy (nirs) should not be standard of care for postoperative management . Seminars in Thoracic and Cardiovascular Surgery: Pediatric Cardiac Surgery Annual , 13 ( 1 ): 51 – 4 , 2010 . OpenUrl CrossRef PubMed 4. G. Greisen . Is near-infrared spectroscopy living up to its promises? Seminars in Fetal and Neonatal Medicine , 11 ( 6 ): 498 – 502 , 2006 . OpenUrl 5. ↵ L. Dix , F. van Bel , W. Baerts , and P. Lemmers . Comparing near-infrared spectroscopy devices and their sensors for monitoring regional cerebral oxygen saturation in the neonate . Pediatric research , 74 ( 5 ): 557 – 563 , 2013 . OpenUrl CrossRef PubMed Web of Science 6. ↵ Antonio Pifferi , Davide Contini , Alberto Dalla Mora , Andrea Farina , Lorenzo Spinelli , and Alessandro Torricelli . New frontiers in time-domain diffuse optics, a review . Journal of Biomedical Optics , 21 ( 9 ): 091310 , June 2016 . Publisher: SPIE. 7. ↵ Han Y. Ban , Geoffrey M. Barrett , Alex Borisevich , Ashutosh Chaturvedi , Jacob L. Dahle , Hamid Dehghani , Julien Dubois , Ryan M. Field , Viswanath Gopalakrishnan , Andrew Gundran , Michael Henninger , Wilson C. Ho , Howard D. Hughes , Rong Jin , Julian Kates- Harbeck , Thanh Landy , Michael Leggiero , Gabriel Lerner , Zahra M. Aghajan , Michael Moon , Isai Olvera , Sangyong Park , Milin J. Patel , Katherine L. Perdue , Benjamin Siepser , Sebastian Sorgenfrei , Nathan Sun , Victor Szczepanski , Mary Zhang , and Zhenye Zhu . Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system . Journal of Biomedical Optics , 27 ( 7 ): 074710 , January 2022 . 8. Laura Di Sieno , Tuomo Talala , Elisabetta Avanzi , Ilkka Nissinen , Jan Nissinen , and Alberto Dalla Mora . 0.5 Billion Counts per Second Enable High Speed and Penetration in Time-Domain Diffuse Optics . IEEE Journal of Selected Topics in Quantum Electronics , 30 (1: Single-Photon Technologies and Applications):1–11, January 2024 . Conference Name: IEEE Journal of Selected Topics in Quantum Electronics. 9. Chao Zhang , Scott Lindner , Ivan Michel Antolovic , Martin Wolf , and Edoardo Charbon . A CMOS SPAD Imager with Collision Detection and 128 Dynamically Reallocating TDCs for Single-Photon Counting and 3D Time-of-Flight Imaging . Sensors , 18 ( 11 ): 4016 , November 2018. 10. Jingjing Jiang , Aldo Di Costanzo Mata , Scott Lindner , Edoardo Charbon , Martin Wolf , and Alexander Kalyanov . 2.5 Hz sample rate time-domain near-infrared optical tomography based on SPAD-camera image tissue hemodynamics . Biomedical Optics Express , 13 ( 1 ): 133 – 146 , January 2022 . Publisher: Optica Publishing Group. OpenUrl PubMed 11. ↵ Michele Lacerenza , Mauro Buttafava , Marco Renna , Alberto Dalla Mora , Lorenzo Spinelli , Franco Zappa , Antonio Pifferi , Alessandro Torricelli , Alberto Tosi , and Davide Contini . Wearable and wireless time-domain near-infrared spectroscopy system for brain and muscle hemodynamic monitoring . Biomedical Optics Express , 11 ( 10 ): 5934 – 5949 , October 2020 . Publisher: Optica Publishing Group. OpenUrl PubMed 12. ↵ M. Lacerenza , A. Torricelli , A. Tosi , D. Contini , A. Dalla Mora , A. Pifferi , and Buttafava M . Performance and reproducibility assessment across multiple time-domain near-infrared spectroscopy device replicas . In SPIE proceedings, editor, In Design and Quality for Biomedical Technologies XV , volume 11951 , pages 43 – 48 , 2022 . OpenUrl 13. ↵ T. Durduran , R. Choe , W.B. Baker , and A.G. Yodh . Diffuse optics for tissue monitoring and tomography . Reports on Progress in Physics , 73 ( 7 ): 076701 , 2010 . 14. T. Durduran , C. Zhou , E. M. Buckley , M.N. Kim , G. Yu , R. Choe , and D.J. Licht . Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects . Journal of biomedical optics , 15 ( 3 ): 037004 – 037004 , 2010 . OpenUrl CrossRef PubMed 15. C. Cheung , J.P. Culver , A.G. Yodh , K. Takahashi , and J.H. Greenberg . In-vivo cerebral measurement combining diffuse near-infrared absorption and correlation spectroscopies . Phys Med Biol , 46 : 2053 – 2065 , 2001 . OpenUrl CrossRef PubMed Web of Science 16. ↵ J. P. Culver , T. Durduran , D. Furuya , C. Cheung , J. H. Greenberg , and A. G. Yodh . Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia . Journal of cerebral blood flow and metabolism , 23 ( 8 ): 911 – 924 , 2003 . OpenUrl CrossRef PubMed Web of Science 17. ↵ David Alan Boas . Diffuse photon probes of structural and dynamical properties of turbid media: theory and biomedical applications . PhD thesis, University of Pennsylvania , 1996 . 18. ↵ Susanna Tagliabue . Comprehensive monitoring of the injured brain by hybrid diffuse optics: towards brain-oriented theranostics . Ph.D. Thesis, Universitat Politècnica de Catalunya , April 2022 . 19. ↵ Susanna Tagliabue , Michał Kacprzak , Isabel Serra , Federica Maruccia , Jonas B Fischer , Marilyn Riveiro-Vilaboa , Anna Rey-Perez , Lourdes Expósito , Claus Lindner , Marcelino Báguena , and others. Transcranial, Non-Invasive Evaluation of Potential Misery Perfusion During Hyperventilation Therapy of Traumatic Brain Injury Patients . Journal of neurotrauma , 40 ( 19-20 ): 2073 – 2086 , 2023 . Publisher: Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New.... OpenUrl PubMed 20. ↵ Clara Gregori Pla . Correlates of cerebral vasoreactivity measured by non-invasive diffuse optical measurements as biomarkers of brain injury risk . Ph.D. Thesis, Universitat Politècnica de Catalunya , March 2019 . Accepted: 2021-03-03T09:50:13Z Publication Title: TDX (Tesis Doctorals en Xarxa). 21. ↵ Marco Nabacino , Caterina Amendola , Davide Contini , Rebecca Re , Lorenzo Spinelli , and Alessandro Torricelli . Fast Multi-Distance Time-Domain NIRS and DCS System for Clinical Applications . Sensors , 24 ( 22 ): 7375 , January 2024 . OpenUrl PubMed 22. Laura Mawdsley , Rasa Eskandari , Farah Kamar , Ajay Rajaram , Lawrence C. M. Yip , Naomi Abayomi , Stephanie Milkovich , Jeffrey J. L. Carson , Keith St. Lawrence , Christopher G. Ellis , and Mamadou Diop . In vivo optical assessment of cerebral and skeletal muscle microvascular response to phenylephrine . FASEB BioAdvances , 6 ( 9 ): 390 – 399 , 2024 . eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1096/fba.2024-00063 . OpenUrl PubMed 23. Rasa Eskandari , Stephanie Milkovich , Farah Kamar , Daniel Goldman , Donald G. Welsh , Christopher G. Ellis , and Mamadou Diop . Non-invasive point-of-care optical technique for continuous in vivo assessment of microcirculatory function: Application to a pre-clinical model of early sepsis . The FASEB Journal , 38 ( 23 ): e70204 , 2024 . eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1096/fj.202401889R . OpenUrl PubMed 24. Pablo Fernández Esteberena , Lorenzo Cortese , Marta Zanoletti , Giuseppe Lo Presti , Gloria Aranda Velazquez , Sabina Ruiz Janer , Mauro Buttafava , Marco Renna , Laura Di Sieno , Alberto Tosi , Alberto Dalla Mora , Stanislaw Wojtkiewicz , Hamid Dehghani , Sixte de Fraguier , An Nguyen-Dinh , Bogdan Rosinski , Udo M. Weigel , Dibya J. Sarangi , Mattia Squarcia , Felicia A. Hanzu , Davide Contini , Mireia Mora Porta , and Turgut Durduran . Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies . Biomedical Optics Express , 15 ( 12 ): 7035 – 7055 , December 2024 . OpenUrl PubMed 25. Valeria Calcaterra , Michele Lacerenza , Caterina Amendola , Mauro Buttafava , Davide Contini , Virginia Rossi , Lorenzo Spinelli , Sara Zanelli , Gianvincenzo Zuccotti , and Alessandro Torricelli . Cerebral baseline optical and hemodynamic properties in pediatric population: a large cohort time-domain near-infrared spectroscopy study . Neurophotonics , 11 ( 4 ): 045009 – 045009 , 2024 . Publisher: Society of Photo-Optical Instrumentation Engineers. OpenUrl PubMed 26. Caterina Amendola , Agnese De Carli , Tiziana Boggini , Davide Contini , Sofia Passera , Nicola Pesenti , Lorenzo Spinelli , Martina Giovannella , Turgut Durduran , Udo M. Weigel , Alessandro Torricelli , Gorm Greisen , and Monica Fumagalli . Effects of red blood cell transfusion on cerebral hemodynamics of preterm neonates . Neurophotonics , 11 ( 4 ): 045014 , December 2024 . Publisher: SPIE. 27. ↵ Susanna Tagliabue , Anna Rey-Perez , Lourdes Exposito , Andrés F. Jimenez , Sara Valles Angulo , Federica Maruccia , Jonas B. Fischer , Michal Kacprzak , Maria A. Poca , and Turgut Durduran . Hybrid diffuse optical appraisal of peripheral and cerebral changes in critically ill patients receiving red blood cell transfusion . Biophotonics Discovery , 2 ( 1 ): 015001 , January 2025 . Publisher: SPIE. 28. ↵ Hernando Gómez , Andrés Torres , Patricio Polanco , Hyung Kook Kim , Sven Zenker , Juan Carlos Puyana , and Michael R. Pinsky . Use of non-invasive NIRS during a vascular occlusion test to assess dynamic tissue O(2) saturation response . Intensive Care Medicine , 34 ( 9 ): 1600 – 1607 , September 2008 . OpenUrl CrossRef PubMed Web of Science 29. ↵ D. Orbegozo Cortés , L. Rahmania , M. Irazabal , C. Santacruz , V. Fontana , D. De Backer , J. Creteur , and J. L. Vincent . Microvascular reactivity is altered early in patients with acute respiratory distress syndrome . Respiratory research , 17 : 59 , 2016 . 30. ↵ J. Mesquida , A. Caballer , L. Cortese , C. Vila , U. Karadeniz , M. Pagliazzi , M. Zanoletti , A. P. Pacheco , P. Castro , M. García-de Acilu , R. C. Mesquita , D. R. Busch , T. Durduran , and HEMOCOVID-19 Consortium . Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in covid-19 patients admitted to intermediate respiratory and intensive care units . Critical care (London, England) , 25 : 381 , 2021 . OpenUrl PubMed 31. ↵ Hendrik Schäfer , Marc Teschler , Frank C. Mooren , and Boris Schmitz . Altered tissue oxygenation in patients with post COVID-19 syndrome . Microvascular Research , 148 : 104551 , July 2023 . 32. ↵ J. Mesquida , G. Gruartmoner , C. Espinal , J. Masip , C. Sabatier , A. Villagrá , H. Gómez , M. Pinsky , F. Baigorri ,, and A. Artigas . Thenar oxygen saturation (sto 2 ) alterations during a spontaneous breathing trial predict extubation failure . Annals of intensive care , 10 ( 1 ): 381 , 2021 . 33. Z. Louvaris , M. Van Hollebeke , A. Dhaenens , M. Vanhemelen , P. Meersseman , J. Wauters , R. Gosselink , A. Wilmer , D. Langer , and G. Hermans . Cerebral cortex and respiratory muscles perfusion during spontaneous breathing attempts in ventilated patients and its relation to weaning outcomes: a protocol for a prospective observational study . BMJ open , 9 : e031072 , 2019 . OpenUrl Abstract / FREE Full Text 34. K. Ericksen , G. Alpan , and E. F. La Gamma . Effect of ventilator modes on neonatal cerebral and peripheral oxygenation using near-infrared spectroscopy . Acta paediatrica , 110 : 1151 – 1156 , 2021 . OpenUrl PubMed 35. D. Margetis , E. Maury , P. Y. Boelle , M. Alves , A. Galbois , J. I. Baudel , G. Offenstadt , B. Guidet , and Ait-Oufella. Peripheral microcirculatory exploration during mechanical ventilation weaning . Minerva Anestesiol , 80 ( 1 ): 1188 – 1197 , 2014 . OpenUrl PubMed 36. G. Gruartmoner , J. Mesquida , J. Masip , M. L. Martínez , A. Villagra , F. Baigorri , M. R. Pinsky , and A. Artigas . Thenar oxygen saturation during weaning from mechanical ventilation: an observational study . The European respiratory journal , 43 ( 1 ): 213 – 220 , 2014 . OpenUrl PubMed 37. ↵ M. Poriazi , M. Kontogiorgi , E. Angelopoulos , I. Vasileiadis , E. S. Tripodaki , V. Nanou , A. Fassoulaki , S. Nanas , and C. Routsi . Changes in thenar muscle tissue oxygen saturation assessed by near-infrared spectroscopy during weaning from mechanical ventilation . Minerva Anestesiologica , 80 ( 6 ): 666 – 675 , June 2014 . OpenUrl PubMed 38. ↵ S. P. J. Macdonald , F. B. Kinnear , G. Arendts , K. M. Ho , and D. M. Fatovich . Near-infrared spectroscopy to predict organ failure and outcome in sepsis: the assessing risk in sepsis using a tissue oxygen saturation (aristos) study. european journal of emergency medicine . official journal of the European Society for Emergency Medicine , 26 : 174 – 179 , 2019 . OpenUrl 39. A. Rodriguez , T. Lisboa , I. Martín-Loeches , E. Díaz , S. Trefler , M. I. Restrepo , and J. Rello . Mortality and regional oxygen saturation index in septic shock patients: a pilot study . The Journal of trauma , 70 : 1145 – 1152 , 2011 . OpenUrl 40. M. Girardis , L. Rinaldi , S. Busani , I. Flore , S. Mauro , and A. Pasetto . Muscle perfusion and oxygen consumption by near-infrared spectroscopy in septic-shock and non-septic-shock patients . Intensive care medicine , 29 : 1173 – 1176 , 2003 . OpenUrl CrossRef PubMed Web of Science 41. D. Orbegozo Cortés , F. Su , K. Xie , L. Rahmania , F. S. Taccone , D. De Backer , J. L. Vincent , and J.L. Creteur . Peripheral muscle near-infrared spectroscopy variables are altered early in septic shock . Shock , 50 : 87 – 95 , 2018 . OpenUrl PubMed 42. Didier Payen , Cecilia Luengo , Laurent Heyer , Matthieu Resche-Rigon , Sébastien Kerever , Charles Damoisel , and Marie Reine Losser . Is thenar tissue hemoglobin oxygen saturation in septic shock related to macrohemodynamic variables and outcome? Critical Care (London, England) , 13 Suppl 5(Suppl 5):S6, 2009 . 43. ↵ Jordi Masip , Jaume Mesquida , Cecilia Luengo , Gisela Gili , Gemma Gomà , Ricard Ferrer , Jean Louis Teboul , Didier Payen , and Antonio Artigas . Near-infrared spectroscopy StO2 monitoring to assess the therapeutic effect of drotrecogin alfa (activated) on microcirculation in patients with severe sepsis or septic shock . Annals of Intensive Care , 3 ( 1 ): 30 , September 2013 . 44. ↵ J. Marín-Corral , L. Claverias , M. Bodí , S. Pascual , A. Dubin , J. Gea , and A. Rodriguez . Prognostic value of brachioradialis muscle oxygen saturation index and vascular occlusion test in septic shock patients . Medicina Intensiva , 40 ( 4 ): 208 – 215 , May 2016 . OpenUrl PubMed 45. ↵ Daniel De Backer . Novelties in the evaluation of microcirculation in septic shock . Journal of Intensive Medicine , 3 ( 2 ): 124 – 130 , April 2023 . OpenUrl PubMed 46. Jacques Creteur , Tiziana Carollo , Giulia Soldati , Gustavo Buchele , Daniel De Backer , and Jean-Louis Vincent . The prognostic value of muscle StO2 in septic patients . Intensive Care Medicine , 33 ( 9 ): 1549 – 1556 , September 2007 . OpenUrl CrossRef PubMed Web of Science 47. Aurélie Thooft , Raphaël Favory , Diamantino Ribeiro Salgado , Fabio S. Taccone , Katia Donadello , Daniel De Backer , Jacques Creteur , and Jean-Louis Vincent . Effects of changes in arterial pressure on organ perfusion during septic shock . Critical Care (London, England) , 15 ( 5 ): R222 , 2011 . OpenUrl CrossRef PubMed 48. Alexandre Lima , Jasper van Bommel , Karolina Sikorska , Michel van Genderen , Eva Klijn , Emmanuel Lesaffre , Can Ince , and Jan Bakker . The relation of near-infrared spectroscopy with changes in peripheral circulation in critically ill patients . Critical Care Medicine , 39 ( 7 ): 1649 – 1654 , July 2011 . OpenUrl CrossRef PubMed Web of Science 49. ↵ Jean-François Georger , Olfa Hamzaoui , Anis Chaari , Julien Maizel , Christian Richard , and Jean-Louis Teboul . Restoring arterial pressure with norepinephrine improves muscle tissue oxygenation assessed by near-infrared spectroscopy in severely hypotensive septic patients . Intensive Care Medicine , 36 ( 11 ): 1882 – 1889 , November 2010 . OpenUrl CrossRef PubMed Web of Science 50. ↵ A. Campos-Serra , J. Mesquida , S. Montmany-Vioque , P. Rebasa-Cladera , M. Barquero-Lopez , A. Cidoncha-Secilla , N. Llorach-Perucho , M. Morales-Codina , J.C. Puyana , and S. Navarro-Soto . Alterations in tissue oxygen saturation measured by near-infrared spectroscopy in trauma patients after initial resuscitation are associated with occult shock . Eur J Trauma Emerg Surg , 49 ( 1 ): 307 – 315 , 2023 . OpenUrl PubMed 51. F.X. Guyette , H. Gomez , B. Suffoletto , J. Quintero , J. Mesquida , H.K. Kim , D. Hostler , J.C. Puyana , and M.R. Pinsky . Prehospital dynamic tissue oxygen saturation response predicts in-hospital lifesaving interventions in trauma patients . The journal of trauma and acute care surgery , 72 ( 4 ): 930 – 935 , 2012 . OpenUrl 52. Jerome Duret , Julien Pottecher , Pierre Bouzat , Julien Brun , Anatole Harrois , Jean-Francois Payen , and Jacques Duranteau . Skeletal muscle oxygenation in severe trauma patients during haemorrhagic shock resuscitation . Critical Care , 19 ( 1 ): 141 , December 2015 . 53. ↵ Roberta Domizi , Elisa Damiani , Claudia Scorcella , Andrea Carsetti , Roberta Castagnani , Sara Vannicola , Sandra Bolognini , Vincenzo Gabbanelli , Simona Pantanetti , and Abele Donati . Association between sublingual microcirculation, tissue perfusion and organ failure in major trauma: A subgroup analysis of a prospective observational study . PloS One , 14 ( 3 ): e0213085 , 2019 . OpenUrl PubMed 54. ↵ A. Feldheiser , O. Hunsicker , L. Kaufner , J. Köhler , H. Sieglitz , R. Casans Francés , K.-D. Wernecke , J. Sehouli , and C. Spies . Dynamic muscle O2 saturation response is impaired during major non-cardiac surgery despite goal-directed haemodynamic therapy . Revista Espanola De Anestesiologia Y Reanimacion , 63 ( 3 ): 149 – 158 , March 2016 . OpenUrl PubMed 55. ↵ C. K. Niezen , D. Massari , J. J. Vos , and T. W. L. Scheeren . The use of a vascular occlusion test combined with near-infrared spectroscopy in perioperative care: a systematic review . Journal of Clinical Monitoring and Computing , 36 ( 4 ): 933 – 946 , August 2022 . OpenUrl PubMed 56. Sabino Scolletta , Federico Franchi , Elisa Damiani , Armando Cennamo , Roberta Domizi , Antonio Meola , Claudia Scorcella , Davide Vanoli , Christopher Münch , Erica Adrario , Luca Marchetti , Fabio Silvio Taccone , and Abele Donati . Tissue oxygen saturation changes and postoperative complications in cardiac surgery: a prospective observational study . BMC anesthesiology , 19 ( 1 ): 229 , December 2019 . 57. ↵ Tae Kyong Kim , Youn Joung Cho , Jeong Jin Min , John M. Murkin , Jae-Hyon Bahk , Deok Man Hong , and Yunseok Jeon . Microvascular reactivity and clinical outcomes in cardiac surgery . Critical Care (London, England) , 19 ( 1 ): 316 , September 2015 . 58. ↵ Ah-Reum Cho , Hyeon-Jeong Lee , Jeong-Min Hong , Christine Kang , Hyae-Jin Kim , Eun-Jung Kim , Min Su Kim , Soeun Jeon , and Hyewon Hwang . Microvascular reactivity as a predictor of major adverse events in patients with on-pump cardiac surgery . Korean Journal of Anesthesiology , 75 ( 4 ): 338 – 349 , August 2022 . OpenUrl PubMed 59. ↵ Ah-Reum Cho , Hyeon-Jeong Lee , Hyae-Jin Kim , Wangseok Do , Soeun Jeon , Seung-Hoon Baek , Eun-Soo Kim , Jae-Young Kwon , and Hae-Kyu Kim . Microvascular Reactivity Measured by Dynamic Near-infrared Spectroscopy Following Induction of General Anesthesia in Healthy Patients: Observation of Age-related Change . International Journal of Medical Sciences , 18 ( 5 ): 1096 – 1103 , 2021 . OpenUrl PubMed 60. Ah-Reum Cho , Hyae-Jin Kim , Hyeon-Jeong Lee , Haekyu Kim , Wangseok Do , Christine Kang , and Yesul Kim . Changes in the microvascular reactivity during spinal anesthesia . Microvascular Research , 137 : 104176 , September 2021 . 61. Aleksandra Biedrzycka , Maciej Kowalik , Rafał Pawlaczyk , Dariusz Jagielak , Dariusz Swietlik , Wiktor Szymanowicz , and Romuald Lango . Aortic cross-clamping phase of cardiopulmonary bypass is related to decreased microvascular reactivity after short-term ischaemia of the thenar muscle both under intravenous and volatile anaesthesia: a randomized trial . Interactive Cardiovascular and Thoracic Surgery , 23 ( 5 ): 770 – 778 , November 2016 . OpenUrl CrossRef PubMed 62. ↵ Celine Bernet , Olivier Desebbe , Sebastien Bordon , Charlotte Lacroix , Pascal Rosamel , Fadi Farhat , Jean-Jacques Lehot , and Maxime Cannesson . The impact of induction of general anesthesia and a vascular occlusion test on tissue oxygen saturation derived parameters in high-risk surgical patients . Journal of Clinical Monitoring and Computing , 25 ( 4 ): 237 – 244 , August 2011 . OpenUrl PubMed 63. ↵ Taketomo Soga , Kaoru Sakatani , Tsukasa Yagi , Tsuyoshi Kawamorita , and Atsuo Yoshino . The relationship between hyperlactatemia and microcirculation in the thenar eminence as measured using near-infrared spectroscopy in patients with sepsis . Emergency medicine journal: EMJ , 31 ( 8 ): 654 – 658 , August 2014 . OpenUrl PubMed 64. Tsuyoshi Kawamorita , Keiichiro Kuronuma , Tsukasa Yagi , Eizo Tachibana , Shonosuke Sugai , Satoshi Hayashida , Kazuki Iso , Korehito Iida , Wataru Atsumi , Satoshi Kunimoto , Yasuyuki Suzuki , Shigemasa Tani , Naoya Matsumoto , Yasuo Okumura , and Kaoru Sakatani . Application of Peripheral Near Infrared Spectroscopy to Assess Risk Factors in Patient with Coronary Artery Disease: Part 1 . Advances in Experimental Medicine and Biology , 1232 : 331 – 337 , 2020. 65. Keiichiro Kuronuma , Tsukasa Yagi , Shonosuke Sugai , Satoshi Hayashida , Kazuki Iso , Korehito Iida , Wataru Atsumi , Eizo Tachibana , Satoshi Kunimoto , Yasuyuki Suzuki , Shigemasa Tani , Naoya Matsumoto , Yasuo Okumura , and Kaoru Sakatani . Effect of Atorvastatin on Microcirculation Evaluated by Vascular Occlusion Test with Peripheral Near-Infrared Spectroscopy . Advances in Experimental Medicine and Biology , 1395 : 351 – 356 , 2022. 66. Miklos Lipcsey , Nicholas Cz Woinarski , and Rinaldo Bellomo . Near infrared spectroscopy (NIRS) of the thenar eminence in anesthesia and intensive care . Annals of Intensive Care , 2 ( 1 ): 11 , May 2012 . 67. Sam J. Thomson , Matthew L. Cowan , Daniel M. Forton , Sarah J. Clark , Saif Musa , Michael Grounds , and Tony M. Rahman . A study of muscle tissue oxygenation and peripheral micro- circulatory dysfunction in cirrhosis using near infrared spectroscopy . Liver International: Official Journal of the International Association for the Study of the Liver , 30 ( 3 ): 463 – 471 , March 2010 . OpenUrl PubMed 68. Ravi Shankar Samraj , Dalia Lopez-Colon , Maria Kerrigan , Frederick J. Fricker , Biagio A. Pietra , Mark Bleiweis , and Dipankar Gupta . Thenar Muscle Oxygen Saturation Using Vascular Occlusion Test: A Novel Technique to Study Microcirculatory Abnormalities in Pediatric Heart Failure Patients . Pediatric Cardiology , 40 ( 6 ): 1151 – 1158 , August 2019 . OpenUrl PubMed 69. Franz Haertel , Diana Reisberg , Martin Peters , Sebastian Nuding , P. Christian Schulze , Karl Werdan , and Henning Ebelt . Predicting the Need for Renal Replacement Therapy Using a Vascular Occlusion Test and Tissue Oxygen Saturation in Patients in the Early Phase of Multiorgan Dysfunction Syndrome . Journal of Clinical Medicine , 11 ( 5 ): 1420 , March 2022. 70. R. A. De Blasi . Is muscle StO2 an appropriate variable for investigating early compensatory tissue mechanisms under physiological and pathological conditions? Intensive Care Medicine , 34 ( 9 ): 1557 – 1559 , September 2008 . OpenUrl PubMed 71. ↵ Geoff A. Bellingham , Ryan S. Smith , Patricia Morley-Forster , and John M. Murkin . Use of near infrared spectroscopy to detect impaired tissue oxygen saturation in patients with complex regional pain syndrome type 1 . Canadian Journal of Anaesthesia = Journal Canadien D’anesthesie , 61 ( 6 ): 563 – 570 , June 2014 . OpenUrl 72. ↵ Rogerio N. Soares , Juan M. Murias , Flavia Saccone , Leopoldo Puga , Gustavo Moreno , Miguel Resnik , and Gabriela F. De Roia . Effects of a rehabilitation program on microvascular function of CHD patients assessed by near-infrared spectroscopy . Physiological Reports , 7 ( 11 ): e14145 , June 2019 . OpenUrl PubMed 73. ↵ Masahiro Horiuchi and Koichi Okita . Microvascular responses during reactive hyperemia assessed by near-infrared spectroscopy and arterial stiffness in young, middle-aged, and older women . Microvascular Research , 129 : 103972 , May 2020 . 74. Emily M. Rogers , Nile F. Banks , and Nathaniel D. M. Jenkins . Metabolic and microvascular function assessed using near-infrared spectroscopy with vascular occlusion in women: age differences and reliability . Experimental Physiology , 108 ( 1 ): 123 – 134 , January 2023 . OpenUrl PubMed 75. Diego Orbegozo Cortés , Florin Puflea , Daniel De Backer , Jacques Creteur , and Jean-Louis Vincent . Near infrared spectroscopy (NIRS) to assess the effects of local ischemic preconditioning in the muscle of healthy volunteers and critically ill patients . Microvascular Research , 102 : 25 – 32 , November 2015 . OpenUrl PubMed 76. R. Boushel , H. Langberg , J. Olesen , J. Gonzales-Alonzo , J. Bülow , and M. Kjaer . Monitoring tissue oxygen availability with near infrared spectroscopy (NIRS) in health and disease . Scandinavian Journal of Medicine & Science in Sports , 11 ( 4 ): 213 – 222 , August 2001 . OpenUrl PubMed 77. ↵ Rogerio N. Soares , Yasina B. Somani , David N. Proctor , and Juan M. Murias . The association between near-infrared spectroscopy-derived and flow-mediated dilation assessment of vascular responsiveness in the arm . Microvascular Research , 122 : 41 – 44 , March 2019 . OpenUrl PubMed 78. Rogério Nogueira Soares , Raylene A. Reimer , and Juan Manuel Murias . Changes in vascular responsiveness during a hyperglycemia challenge measured by near-infrared spectroscopy vascular occlusion test . Microvascular Research , 111 : 67 – 71 , May 2017 . OpenUrl PubMed 79. Louis Kolb , Diego Orbegozo , Jacques Creteur , Jean-Charles Preiser , Jean-Louis Vincent , and Daniel De Backer . Oral Nitrate Increases Microvascular Reactivity and the Number of Visible Perfused Microvessels in Healthy Volunteers . Journal of Vascular Research , 54 ( 4 ): 209 – 216 , 2017 . OpenUrl PubMed 80. ↵ Jackson Davis , Rachel I. Feldman , Miranda K. Traylor , Sylvie M. Gray , Shawn M. Drake , and Joshua L. Keller . Myofascial release induces declines in heart rate and changes to microvascular reactivity in young healthy adults . Journal of Bodywork and Movement Therapies , 38 : 254 – 262 , April 2024 . OpenUrl 81. ↵ Jacob T. Caldwell , Garrett C. Wardlow , Patrece A. Branch , Macarena Ramos , Christopher D. Black, and Carl J. Ade. Effect of exercise-induced muscle damage on vascular function and skeletal muscle microvascular deoxygenation . Physiological Reports , 4 ( 22 ): e13032 , November 2016 . OpenUrl CrossRef PubMed 82. Letizia Rasica , Erin Calaine Inglis , Danilo Iannetta , Rogerio N. Soares , and Juan M. Murias . Fitness Level- and Sex-Related Differences in Macrovascular and Microvascular Responses during Reactive Hyperemia . Medicine and Science in Sports and Exercise , 54 ( 3 ): 497 – 506 , March 2022 . OpenUrl 83. Blai Ferrer-Uris , Albert Busquets , Faruk Beslija , and Turgut Durduran . Assessment of Microvascular Hemodynamic Adaptations in Finger Flexors of Climbers. Bioengineering (Basel , Switzerland ), 11 ( 4 ): 401 , April 2024 . 84. Rogério Nogueira Soares , Mitchell A. George , David N. Proctor , and Juan M. Murias . Differences in vascular function between trained and untrained limbs assessed by near-infrared spectroscopy . European Journal of Applied Physiology , 118 ( 10 ): 2241 – 2248 , October 2018 . OpenUrl PubMed 85. ↵ Chris J. McManus , Jay Collison , and Chris E. Cooper . Performance comparison of the MOXY and PortaMon near-infrared spectroscopy muscle oximeters at rest and during exercise . Journal of Biomedical Optics , 23 ( 1 ): 1 – 14 , January 2018 . OpenUrl CrossRef 86. ↵ M. Atif Yaqub , Marta Zanoletti , Lorenzo Cortese , Daniel Senciales Sánchez , Caterina Amendola , Lorenzo Frabasile , Umut Karadeniz , Jacqueline Martinez Garcia , Marta Martin , Jordi Cortes-Picas , Alba Caballer , Edgar Cortes , Sara Nogales , Alberto Tosi , Talyta Carteano , Diego Sanoja Garcia , Jakub Tomanik , Tessa Wagenaar , Hsiao Mui , Claudia Nunzia Guadagno , Shahrzad Parsa , Sanathana Konugolu Venkata Sekar , Luc Demarteau , Tijl Houtbeckers , Udo M. Weigel , Michele Lacerenza , Mauro Buttafava , Alessandro Torricelli , Davide Contini , Jaume Mesquida , and Turgut Durduran . Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care . JoVE (Journal of Visualized Experiments ) , ( 207 ): e66062 , May 2024 . 87. ↵ L. Cortese , G. L. Presti , M. Zanoletti , G. Aranda , M. Buttafava , D. Contini , A. Dalla Mora , H. Dehghani , L. Di Sieno , S. de Fraguier , F.A. Hanzu , M. Mora Porta , A. Nguyen-Dinh , M. Renna , B. Rosinski , M. Squarcia , A. Tosi , U.M. Weigel , S. Wojtkiewicz , and T. Durduran . The luca device: a multi-modal platform combining diffuse optics and ultrasound imaging for thyroid cancer screening . Biomedical Optics Express , 12 ( 6 ): 3392 – 3409 , 2021 . OpenUrl PubMed 88. ↵ C. Amendola , M. Lacerenza , M. Buttafava , A. Tosi , L. Spinelli , D. Contini , and A. Torricelli . A compact multi-distance dcs and time-domain nirs hybrid system for hemodynamic and metabolic measurements . Sensors , 21 ( 3 ): 870 , 2021 . 89. ↵ M. Giovannella , D. Contini , M. Pagliazzi , A. Pifferi , L. Spinelli , R. Erdmann , and U. M. Weigel . Babylux device: a diffuse optical system integrating diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy for the neuromonitoring of the premature newborn brain . Neurophotonics , 6 ( 2 ): 025007 – 0250075 , 2019 . OpenUrl PubMed 90. ↵ Carlos A. Gómez , Laurent Brochard , Ewan C. Goligher , Dmitry Rozenberg , W. Darlene Reid , and Darren Roblyer . Combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy system for comprehensive metabolic monitoring of inspiratory muscles during loading . Journal of Biomedical Optics , 29 ( 3 ): 035002 , March 2024 . Publisher: SPIE. 91. ↵ Martina Giovannella . Hybrid diffuse optical neuromonitoring of cerebral haemodynamics: from the smallest premature born infants to adults . Ph.D. Thesis, Universitat Politècnica de Catalunya , June 2019 . Accepted: 2021-06-01T07:45:15Z Publication Title: TDX (Tesis Doctorals en Xarxa). 92. Daniel Milej , Marwan Shahid , Androu Abdalmalak , Ajay Rajaram , Mamadou Diop , and Keith St. Lawrence . Characterizing dynamic cerebral vascular reactivity using a hybrid system combining time-resolved near-infrared and diffuse correlation spectroscopy . Biomed. Opt. Express , 11 ( 8 ): 4571 – 4585 , Aug 2020 . OpenUrl PubMed 93. Claus Lindner , Mireia Mora , Parisa Farzam , Mattia Squarcia , Johannes Johansson , Udo M Weigel , Irene Halperin , Felicia A Hanzu , and Turgut Durduran . Diffuse optical characterization of the healthy human thyroid tissue and two pathological case studies . PloS one , 11 ( 1 ): e0147851 , 2016 . Publisher: Public Library of Science San Francisco, CA USA. OpenUrl PubMed 94. ↵ Lian He , Wesley B. Baker , Daniel Milej , Venkaiah C. Kavuri , Rickson C. Mesquita , David R. Busch , Kenneth Abramson , Jane Y. Jiang , Mamadou Diop , Keith St Lawrence , Olivia Amendolia , Francis Quattrone , Ramani Balu , W. Andrew Kofke , and Arjun G. Yodh . Noninvasive continuous optical monitoring of absolute cerebral blood flow in critically ill adults . Neurophotonics , 5 ( 4 ): 045006 , November 2018 . Publisher: SPIE. 95. ↵ M. Lacerenùza , M. Buttafava , M. Renna , A.D. Mora , L. Spinelli , F. Zappa , A. Pifferi , A. Torricelli , A. Tosi , and D. Contini . Wearable and wireless time-domain near-infrared spectroscopy system for brain and muscle hemodynamic monitoring . Biomedical optics express , 11 ( 10 ): 5934 – 5949 , 2020 . OpenUrl PubMed 96. ↵ Alberto Dalla Mora , Davide Contini , Simon Arridge , Fabrizio Martelli , Alberto Tosi , Gianluca Boso , Andrea Farina , Turgut Durduran , Edoardo Martinenghi , Alessandro Torricelli , and Antonio Pifferi . Towards next-generation time-domain diffuse optics for extreme depth penetration and sensitivity . Biomedical Optics Express , 6 ( 5 ): 1749 – 1760 , May 2015 . Pub-lisher: Optica Publishing Group. OpenUrl PubMed 97. ↵ Yukio Yamada , Hiroaki Suzuki , and Yutaka Yamashita . Time-domain near-infrared spectroscopy and imaging: A review . Applied Sciences , 9 ( 6 ): 1127 , January 2019 . Number: 6 Publisher: Multidisciplinary Digital Publishing Institute. OpenUrl 98. ↵ American national standard for safe use of lasers - ansi z136 .1- 2022 . 99. ↵ International electrical equipment - iec 60825-1 : 2014 . Safety of laser products - Part 1: Equipment classification and requirements . 100. ↵ David A Boas , LE Campbell , and Arjun G Yodh . Scattering and imaging with diffusing temporal field correlations . Physical review letters , 75 ( 9 ): 1855 , 1995 . Publisher: APS. OpenUrl CrossRef PubMed Web of Science 101. ↵ David A Boas and Arjun G Yodh . Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation . JOSA A , 14 ( 1 ): 192 – 215 , 1997 . Publisher: Optica Publishing Group. OpenUrl 102. ↵ B. J. Ackerson , R. L. Dougherty , N. M. Reguigui , and U. Nobbmann . Correlation transfer - Application of radiative transfer solution methods to photon correlation problems . Journal of Thermophysics and Heat Transfer , 6 ( 4 ): 577 – 588 , October 1992 . Publisher: American Institute of Aeronautics and Astronautics. OpenUrl 103. ↵ L. Cortese , M. Zanoletti , U. Karadeniz , M. Pagliazzi , M.A. Yaqub , Busch D.R. , Mesquida J. , and Durduran T . Performance assessment of a commercial continuous-wave near-infrared spectroscopy tissue oximeter for suitability for use in an international, multi-center clinical trial . Sensors , 21 ( 21 ): 6957 , 2021 . OpenUrl PubMed 104. ↵ International electrical equipment - iec 60601-2-22 : 2019 . Medical electrical equipment - Part 2-22: Particular requirements for basic safety and essential performance of surgical, cosmetic, therapeutic and diagnostic laser equipment . 105. ↵ Ileana Pirovano , Rebecca Re , Alessia Candeo , Davide Contini , Alessandro Torricelli , and Lorenzo Spinelli . Instrument response function acquisition in reflectance geometry for time-resolved diffuse optical measurements . Biomedical Optics Express , 11 ( 1 ): 240 – 250 , December 2019 . OpenUrl PubMed 106. ↵ Medical electrical equipment - iec 60601-1 . Medical electrical equipment - ALL PARTS . 107. ↵ R. C. Mesquita , T. Durduran , Q. Yu , E. M. Buckley , M. N. Kim , C. Zhou , R. Choe , U. Sunar , and Yodh A. G . Direct measurement of tissue blood flow and metabolism with diffuse optics . Phil. Trans. R. Soc. A ., 369 ( 1955 ): 4390 – 4406 , 2011 . OpenUrl CrossRef PubMed 108. ↵ P.A. Harris , R. Taylor , R. Thielke , J. Payne , K.G. Gonzales , and J.G. Conde . Research electronic data capture (redcap) – a metadata-driven methodology and workflow process for providing translational research informatics support . J. Biomed. Inform ., 42 : 377 – 381 , 2009 . OpenUrl CrossRef PubMed Web of Science 109. ↵ P.A. Harris , R. Taylor , B.L. Minor , V. Elliott , M. Fernandez , L. O’Neal , L. McLeod , G. Delacqua , F. Delacqua , J. Kirby , S.N. Duda , and REDCap Consortium. The redcap consortium: Building an international community of software platform partners . J. Biomed. Inform ., 95 : 103208 , 2219 . 110. ↵ R. Huang , K. Hong , D. Yang , and G. Huang . Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review . Front Neurosci ., 16 : 878750 , 2022 . 111. ↵ M. Renna , J. Peruch , A. Sunwoo , Z. Starkweather , A. Martin , and M. A. Franceschini . A contact-sensitive probe for biomedical optics . Sensors , 22 : 2361 , 2022 . OpenUrl PubMed 112. ↵ Fangzhou Zhao , Pietro Levoni , Lorenzo Frabasile , Hong Qi , Michele Lacerenza , Pranav Lanka , Alessandro Torricelli , Antonio Pifferi , Rinaldo Cubeddu , and Lorenzo Spinelli . Reproducibility of identical solid phantoms . Journal of Biomedical Optics , 27 ( 7 ): 074713 , February 2022 . 113. ↵ Claus Lindner . Translation of non-invasive optical measurements of hemodynamics and oxygen metabolism to the clinic . Ph.D. Thesis, Universitat Politècnica de Catalunya , March 2017 . 114. Jonas B. Fischer . Transcranial diffuse optical measurements of pulsatility derived parameters for neuromonitoring applications . Ph.D. Thesis, Universitat Politècnica de Catalunya , September 2021 . 115. ↵ Lisa C. Kobayashi Frisk . Application and development of diffuse optical methods for the non-invasive bedside assessment of cerebral hemodynamics in the stroke unit . Ph.D. Thesis, Universitat Politècnica de Catalunya , July 2023 . 116. ↵ C. Amendola , M. Buttafava , T. Carteano , L. Contini , L. Cortese , T. Durduran , L. Frabasile , C.N. Guadagno , U. Karadeniz , M. Lacerenza , J. Mesquida , S. Parsa , R. Re , D. Sanoja Garcia , S. KV Sekar , L. Spinelli , A. Torricelli , A. Tosi , U.M Weigel , M.A. Yaqub , M. Zanoletti , and D. Contini . Assessment of power spectral density of microvascular hemodynamics in skeletal muscles at very low and low-frequency via near-infrared diffuse optical spectro-scopies . Biomedical Optics Express , 14 : 5994 – 6015 , 2023 . OpenUrl PubMed 117. ↵ L. Cortese , G. Lo Presti , M. Pagliazzi , D. Contini , A. Dalla Mora , H. Dehghani , F. Ferri , J. B. Fischer , M. Giovannella , F. Martelli , U. M. Weigel , S. Wojtkiewicz , M. Zanoletti , and T. Durduran . Recipes for diffuse correlation spectroscopy instrument design using commonly utilized hardware based on targets for signal-to-noise ratio and precision . Biomedical optics express , 12 ( 6 ): 3265 – 3281 , 2021 . OpenUrl PubMed 118. ↵ C.J. McManus , J. Collison , and C.E. Cooper . Performance comparison of the moxy and portamon near-infrared spectroscopy muscle oximeters at rest and during exercise . Journal of biomedical optics , 23 : 015007 , 2018 . 119. ↵ C.K. Niezen , D. Massari , Vos J.J. , and T.W.L. Scheeren . The use of a vascular occlusion test combined with near-infrared spectroscopy in perioperative care: a systematic review . J. Clin. Monit. Comput ., 36 ( 4 ): 933 – 946 , 2022 . OpenUrl PubMed 120. ↵ S. Suzuki , T. Takasaki , S. Ozaki , and Y. Kobayashi . Tissue oxygenation monitor using nir spatially resolved spectroscopy . Proc. SPIE 3597, Optical Tomography and Spectroscopy of Tissue III ., 1999 . 121. ↵ C. Amendola , D. Contini , R. Re , L. Spinelli , L. Frabasile , P. Levoni , and Torricelli A . Robustness of tissue oxygenation estimates by continuous wave space-resolved near infrared spectroscopy . J. Biomed. Opt ., 28 ( 7 ), 2023 . 122. ↵ A. Torricelli , D. Contini , A. Pifferi , M. Caffini , R. Re , L. Zucchelli , and L. Spinelli . Time domain functional nirs imaging for human brain mapping . NeuroImage , 85 : 28 – 50 , 2014 . OpenUrl CrossRef PubMed 123. ↵ H. Gomez , J. Mesquida , P. Simon , H. K. Kim , J. C. Puyana , C. Ince , and M. R. Pinsky . Characterization of tissue oxygen saturation and the vascular occlusion test: influence of measurement sites, probe sizes and deflation thresholds . Critical Care , 13 ( S3 ), 2009 . 124. ↵ J. Mesquida , G. Gruartmoner , and C. Espinal . Skeletal muscle oxygen saturation (sto 2 ) measured by near-infrared spectroscopy in the critically ill patients . BioMed research international , 2013 : 502194 , 2013 . OpenUrl PubMed 125. K.E. Mullier , D. E. Skarda , K.H. Taylor , D.E. Myers , M.K. McGraw , B.L. Gallea , and G.J. Beilman . Near-infrared spectroscopy in patients with severe sepsis: correlation with invasive hemodynamic measurements . Surg Infect (Larchmt ), 9 : 515 – 9 , 2008 . OpenUrl CrossRef PubMed 126. ↵ D. E. Skarda , K.E. Mullier , D.E. Myers , J. H. Taylor , M.K. McGraw , and G.J. Beilman . Dynamic near-infrared spectroscopy measurements in patients with severe sepsis . Schock , 27 : 348 – 53 , 2007 . OpenUrl 127. ↵ R. Bezemer , A. Lima , D. Myers , E. Klijn , M. Heger , P.T. Goedhart , J. Bakker , and C. Ince . Assessment of tissue oxygen saturation during a vascular occlusion test using near-infrared spectroscopy: the role of probe spacing and measurement site studied in healthy volunteers . Critical Care , 13 ( S4 ), 2009 . 128. C. Mayeur , S. Campard , C. Richard , and J. Teboul . Comparison of four different vascular occlusion tests for assessing reactive hyperemia using near-infrared spectroscopy . Critical Care , 39 ( 4 ): 695 – 701 , 2011 . OpenUrl 129. C. Luengo , M. Resche-Rigon , C. Damoisel , S. Kerever , J. Creteur , and D. Payen . Comparison of two different generations of “nirs” devices and transducers in healthy volunteers and icu patients . J Clin Monit Comput , 27 : 71 – 79 , 2013 . OpenUrl CrossRef PubMed 130. ↵ Hernando Gómez , Jaume Mesquida , Peter Simon , Hyung Kook Kim , Juan C. Puyana , Can Ince , and Michael R. Pinsky . Characterization of tissue oxygen saturation and the vascular occlusion test: influence of measurement sites, probe sizes and deflation thresholds . Critical Care (London, England) , 13 Suppl 5(Suppl 5):S3, 2009 . 131. Cornelia K. Niezen , Jaap J. Vos , Arend F. Bos , and Thomas W. L. Scheeren . Microvascular effects of oxygen and carbon dioxide measured by vascular occlusion test in healthy volunteers . Microvascular Research , 145 : 104437 , January 2023 . 132. Abele Donati , Elisa Damiani , Roberta Domizi , Claudia Scorcella , Andrea Carsetti , Stefania Tondi , Valentina Monaldi , Erica Adrario , Rocco Romano , Paolo Pelaia , and Mervyn Singer . Near-infrared spectroscopy for assessing tissue oxygenation and microvascular reactivity in critically ill patients: a prospective observational study . Critical Care (London, England) , 20 ( 1 ): 311 , October 2016 . 133. Ji-Hyun Lee , Yong-Hee Park , Hee-Soo Kim , and Jin-Tae Kim . Comparison of two devices using near-infrared spectroscopy for the measurement of tissue oxygenation during a vascular occlusion test in healthy volunteers (INVOS® vs . InSpectra™). Journal of Clinical Monitoring and Computing , 29 ( 2 ): 271 – 278 , April 2015 . OpenUrl 134. Miklós Lipcsey , Glenn M. Eastwood , Nicholas C. Z. Woinarski , and Rinaldo Bellomo . Near-infrared spectroscopy of the thenar eminence: comparison of dynamic testing protocols . Critical Care and Resuscitation: Journal of the Australasian Academy of Critical Care Medicine , 14 ( 2 ): 142 – 147 , June 2012 . OpenUrl 135. Claire Mayeur , Sébastien Campard , Christian Richard , and Jean-Louis Teboul . Comparison of four different vascular occlusion tests for assessing reactive hyperemia using near-infrared spectroscopy . Critical Care Medicine , 39 ( 4 ): 695 – 701 , April 2011 . OpenUrl PubMed 136. Hugo Mozina and Matej Podbregar . Near-infrared spectroscopy during stagnant ischemia estimates central venous oxygen saturation and mixed venous oxygen saturation discrepancy in patients with severe left heart failure and additional sepsis/septic shock . Critical Care (London, England) , 14 ( 2 ): R42 , 2010 . 137. ↵ Roman Pareznik , Rajko Knezevic , Gorazd Voga , and Matej Podbregar . Changes in muscle tissue oxygenation during stagnant ischemia in septic patients . Intensive Care Medicine , 32 ( 1 ): 87 – 92 , January 2006 . OpenUrl CrossRef PubMed Web of Science 138. ↵ Andrea Campos-Serra , Jaume Mesquida , Sandra Montmany-Vioque , Pere Rebasa-Cladera , Marta Barquero-Lopez , Ariadna Cidoncha-Secilla , Núria Llorach-Perucho , Marc Morales-Codina , Juan Carlos Puyana , and Salvador Navarro-Soto . Alterations in tissue oxygen saturation measured by near-infrared spectroscopy in trauma patients after initial resuscitation are associated with occult shock . European Journal of Trauma and Emergency Surgery , 49 ( 1 ): 307 – 315 , February 2023 . OpenUrl 139. Rick Bezemer , Alexandre Lima , Dean Myers , Eva Klijn , Michal Heger , Peter T. Goedhart , Jan Bakker , and Can Ince . Assessment of tissue oxygen saturation during a vascular occlusion test using near-infrared spectroscopy: the role of probe spacing and measurement site studied in healthy volunteers . Critical Care (London, England) , 13 Suppl 5(Suppl 5):S4, 2009 . 140. Sebastiaan A. Bartels , Rick Bezemer , Dan M. J. Milstein , Matthijs Radder , Alexandre Lima , Thomas G. V. Cherpanath , Michal Heger , John M. Karemaker , and Can Ince . The micro- circulatory response to compensated hypovolemia in a lower body negative pressure model . Microvascular Research , 82 ( 3 ): 374 – 380 , November 2011 . OpenUrl PubMed 141. ↵ Miranda K. Traylor , Genevieve B. Batman , Kylie N. Sears , Kyndall V. Ransom , Shane M. Hammer , and Joshua L. Keller . Sex-specific microvascular and hemodynamic responses to passive limb heating in young adults . Microcirculation (New York, N.Y.: 1994) , 31 ( 4 ): e12848 , May 2024 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted June 03, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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