Reliability assessment for blood oxygen saturation levels measured with optoacoustic imaging

Ulrich, Leonie; Held, K. Gerrit; Jaeger, Michael; Frenz, Martin; Akarçay, H. Günhan

doi:10.1117/1.jbo.25.4.046005

Cited by 5 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the spectral unmixing step is conducted on a per-pixel basis and is thus vulnerable to motion artifacts, which may be further emphasized by noise due to vessel reactivity. For this reason, several motion correction algorithms have been recently introduced (31,32). Further, a recent study showed that MSOT provided consistent and reproducible functional soft tissue characterization, independent on the investigating personnel (33).…”

Section: Discussionmentioning

confidence: 99%

Multispectral optoacoustic tomography of peripheral arterial disease based on muscle hemoglobin gradients—a pilot clinical study

Karlas¹,

Masthoff²,

Kallmayer³

et al. 2021

Ann Transl Med

View full text Add to dashboard Cite

Background: Current imaging assessment of peripheral artery disease (PAD) relies on anatomical crosssectional visualizations of the affected arteries. Multispectral optoacoustic tomography (MSOT) is a novel molecular imaging technique that provides direct and label-free visualizations of soft tissue perfusion and oxygenation.Methods: MSOT was prospectively assessed in a pilot trial in healthy volunteers (group n 1 =4, mean age 31, 50% male and group n 3 =4, mean age 37.3, 75% male) and patients with intermittent claudication (group n 2 =4, mean age 72, 75% male, PAD stage IIb). We conducted cuff-induced ischemia (group n 1 ) and resting state measurements (groups n 2 and n 3 ) over the calf region. Spatially resolved mapping of oxygenated (HbO 2 ), deoxygenated (Hb) and total (THb) hemoglobin, as well as oxygen saturation (SO 2 ), were measured via hand-held hybrid MSOT-Ultrasound based purely on hemoglobin contrast.Results: Calf measurements in healthy volunteers revealed distinct dynamics for HbO 2 , Hb, THb and SO 2 under cuff-induced ischemia. HbO 2 , THb and SO 2 levels were significantly impaired in PAD patients compared to healthy volunteers (P<0.05 for all parameters). Revascularization led to significant improvements in HbO 2 of the affected limb.Conclusions: Clinical MSOT allows for non-invasive, label-free and real-time imaging of muscle oxygenation in health and disease with implications for diagnostics and therapy assessment in PAD.

show abstract

Section: Discussionmentioning

confidence: 99%

Multispectral optoacoustic tomography of peripheral arterial disease based on muscle hemoglobin gradients—a pilot clinical study

Karlas¹,

Masthoff²,

Kallmayer³

et al. 2021

Ann Transl Med

View full text Add to dashboard Cite

show abstract

“…The fluence ϕ is dependent on unknowns such as the absorption and scattering in the tissue surrounding x 0 . Quantitative OA imaging methods either depend on model-based inversion [2][3][4][5][6][7] or data-driven approaches. [8][9][10][11][12][13] These approaches perform well in silico but often struggle with the translation to real measurements in phantoms or in vivo.…”

Section: Introductionmentioning

confidence: 99%

Multiple illumination learned spectral decoloring for quantitative optoacoustic oximetry imaging

Kirchner

Frenz

2021

J. Biomed. Opt.

Self Cite

View full text Add to dashboard Cite

Significance: Quantitative measurement of blood oxygen saturation (sO 2 ) with optoacoustic (OA) imaging is one of the most sought after goals of quantitative OA imaging research due to its wide range of biomedical applications.Aim: A method for accurate and applicable real-time quantification of local sO 2 with OA imaging.Approach: We combine multiple illumination (MI) sensing with learned spectral decoloring (LSD). We train LSD feedforward neural networks and random forests on Monte Carlo simulations of spectrally colored absorbed energy spectra, to apply the trained models to real OA measurements. We validate our combined MI-LSD method on a highly reliable, reproducible, and easily scalable phantom model, based on copper and nickel sulfate solutions.Results: With this sulfate model, we see a consistently high estimation accuracy using MI-LSD, with median absolute estimation errors of 2.5 to 4.5 percentage points. We further find fewer outliers in MI-LSD estimates compared with LSD. Random forest regressors outperform previously reported neural network approaches.Conclusions: Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.

show abstract

“…There the fluence φ is dependent on unknowns like the absorption and scattering in the tissue surrounding x 0 . qPAI methods either depend on model-based inversion [2][3][4][5][6][7] or data-driven approaches. [8][9][10][11][12][13] These approaches often perform well in silico but struggle with the translation to real measurements in either phantoms or in vivo.…”

Section: Introductionmentioning

confidence: 99%

Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloring

Kirchner¹,

Frenz²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Significance: Quantitative measurement of blood oxygen saturation (sO 2 ) with photoacoustic (PA) imaging is one of the most sought after goals of quantitative PA imaging research due to its wide range of biomedical applications. Aim: A method for accurate and applicable real-time quantification of local sO 2 with PA imaging. Approach: We combine multiple illumination (MI) sensing with learned spectral decoloring (LSD); training on Monte Carlo simulations of spectrally colored absorbed energy spectra, in order to apply the trained models to real PA measurements. We validate our combined MI-LSD method on a highly reliable, reproducible and easily scalable phantom model, based on copper and nickel sulfate solutions.Results: With this sulfate model we see a consistently high estimation accuracy using MI-LSD, with median absolute estimation errors of 2.5 to 4.5 percentage points. We further find fewer outliers in MI-LSD estimates compared to LSD. Random forest regressors outperform previously reported neural network approaches. Conclusions: Random forest based MI-LSD is a promising method for accurate quantitative PA oximetry imaging.

show abstract

Reliability assessment for blood oxygen saturation levels measured with optoacoustic imaging

Cited by 5 publications

References 41 publications

Multispectral optoacoustic tomography of peripheral arterial disease based on muscle hemoglobin gradients—a pilot clinical study

Multispectral optoacoustic tomography of peripheral arterial disease based on muscle hemoglobin gradients—a pilot clinical study

Multiple illumination learned spectral decoloring for quantitative optoacoustic oximetry imaging

Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloring

Contact Info

Product

Resources

About