Comparison of tissue oximeters on a liquid phantom with adjustable optical properties: an extension

Kleiser, Stefan; Ostojić, Draženka; Andresen, Björn F.; Nasseri, Nassim; Isler, Helene; Scholkmann, Felix; Karen, Tanja; Greisen, Gorm; Wolf, Martin

doi:10.1364/boe.9.000086

Cited by 84 publications

(82 citation statements)

References 51 publications

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“…The blood, however, although being supplied again from a blood bank, was not from the same donor as in refs. [19,20,24]. We later present data from 3 different phantoms.…”

Section: Liquid Phantomsmentioning

confidence: 95%

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Tissue oximetry by diffusive reflective visible light spectroscopy: Comparison of algorithms and their robustness

Nasseri

Kleiser

Wolf

et al. 2018

Journal of Biophotonics

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It is essential to measure tissue oxygen saturation (StO ) locally and in thin layers of tissue, for example, the bronchial mucosa, skin flaps and small bones. Visible light spectroscopy (VLS) with a shallow penetration depth is suitable method. Although several VLS algorithms have been developed and described, they have not yet been compared to each other. This hinders attempts to compare the clinical results obtained by different algorithms. To address this issue, we compared the algorithms of Harrison, Knoefel, Pittman-Duling, Sato and our OxyVLS oximeter, which applies the algorithm from Wodick and Lübbers, in a liquid phantom with optical properties of human tissue. We generally observed considerable differences between the algorithms, which were StO dependent. Exceptions were OxyVLS and Sato, showing a high level of agreement with negligible StO dependency. In spite of the considerable deviation between the other algorithms, the difference of StO between them in clinically normal StO was <10%. We did not observe any dependency of the algorithms on hemoglobin content of the phantom or temperature.

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“…The blood, however, although being supplied again from a blood bank, was not from the same donor as in refs. [19,20,24]. We later present data from 3 different phantoms.…”

Section: Liquid Phantomsmentioning

confidence: 95%

“…For details we refer to refs. [19,20,24]. The phantoms here consist of the same ingredients as described in refs.…”

Section: Liquid Phantomsmentioning

confidence: 99%

Tissue oximetry by diffusive reflective visible light spectroscopy: Comparison of algorithms and their robustness

Nasseri

Kleiser

Wolf

et al. 2018

Journal of Biophotonics

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“…Worse still, there are also no generally accepted intervention targets for low c‐rSO 2 values. Finally, c‐rSO 2 values are device specific . However, we do not regard these points as a shortcoming, as long as it is possible to obtain an individual c‐rSO 2 baseline value, measured under awake conditions.…”

Section: Nirs‐directed Hemodynamic Management: the Baseline‐bottomlinmentioning

confidence: 99%

“…Finally, c-rSO 2 values are device specific. [46][47][48] However, we do not regard these points as a shortcoming, as long as it is possible to obtain an individual c-rSO 2 baseline value, measured under awake conditions. Awake is the magic word here:…”

Section: Nir S-direc Ted Hemodynamic Manag Ement: the Ba S Eline-bomentioning

confidence: 99%

“…Dix et al found significant differences in c‐rSO 2 values in preterm neonates measured with different NIRS sensors . Kleiser et al investigated the impact of the source‐detector separation (SDS) of several commercially available NIRS sensors on their performance and reported different tissue oxygenation values measured by different device‐sensor combinations in the same neonatal head phantom. According to Wallin and Lönnqvist, currently available neonatal cerebral NIRS sensors should not be used in neonates <2.5 kg, because due to the small size of the head of these tiny infants it would be likely that the resulting c‐rSO 2 values would represent white matter or lateral ventricle oxygenation rather than a cortical trace …”

Section: A Practical Introduction To Near‐infrared Spectroscopy (Nirs)mentioning

confidence: 99%

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A practical approach to cerebral near‐infrared spectroscopy (NIRS) directed hemodynamic management in noncardiac pediatric anesthesia

Weber

Scoones

2019

Pediatric Anesthesia

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Safeguarding cerebral function is of major importance during pediatric anesthesia. Premature, ex‐premature, and full‐term neonates can be vulnerable to physiological changes that occur during anesthesia and surgery. Data from studies performed during pediatric cardiac surgery and in neonatal/pediatric intensive care units have shown the benefits of near‐infrared spectroscopy (NIRS) monitoring of regional cerebral oxygenation (c‐rSO2). However, NIRS monitoring is seldom used during noncardiac pediatric anesthesia. Despite compelling evidence that blood pressure does not reflect end‐organ perfusion, it is still regarded as the most important determinant of cerebral perfusion and the most relevant hemodynamic management target parameter by most (pediatric) anesthetists. The principle of NIRS monitoring is not self‐explanatory and sometimes seems even counterintuitive, which may explain why many anesthesiologists are reserved regarding its use. The first part of this paper is dedicated to a clinical introduction to NIRS monitoring. Despite scientific efforts, it has not yet been possible to define individual lower limit c‐rSO2 values and it is unlikely this will succeed in the near future. Nonetheless, published treatment algorithms usually specify c‐rSO2 values which may be associated with cerebral hypoxia. Our treatment guideline for maintaining sufficient cerebral oxygenation differs fundamentally from all previously published approaches. We define a baseline c‐rSO2 value, registered in the awake child prior to anesthesia induction, as the lowest acceptable limit during anesthesia and surgery. The cerebral rSO2 is the single target parameter, while blood pressure, heart rate, PaCO2, and SaO2 are major parameters that determine the c‐rSO2. Cerebral NIRS monitoring, interpreted together with its continuously available contributing parameters, may help avoid potentially harmful episodes of cerebral desaturation in anesthetized pediatric patients.

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Imaging Dynamics Beneath Turbid Media via Parallelized Single‐Photon Detection

Xu¹,

Yang²,

Liu³

et al. 2022

Advanced Science

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Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1-0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels.

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Comparison of tissue oximeters on a liquid phantom with adjustable optical properties: an extension

Cited by 84 publications

References 51 publications

Tissue oximetry by diffusive reflective visible light spectroscopy: Comparison of algorithms and their robustness

Tissue oximetry by diffusive reflective visible light spectroscopy: Comparison of algorithms and their robustness

A practical approach to cerebral near‐infrared spectroscopy (NIRS) directed hemodynamic management in noncardiac pediatric anesthesia

Imaging Dynamics Beneath Turbid Media via Parallelized Single‐Photon Detection

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