2,321 research outputs found
Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
Light propagating in tissue attains a spectrum that varies with location due
to wavelength-dependent fluence attenuation by tissue optical properties, an
effect that causes spectral corruption. Predictions of the spectral variations
of light fluence in tissue are challenging since the spatial distribution of
optical properties in tissue cannot be resolved in high resolution or with high
accuracy by current methods. Spectral corruption has fundamentally limited the
quantification accuracy of optical and optoacoustic methods and impeded the
long sought-after goal of imaging blood oxygen saturation (sO2) deep in
tissues; a critical but still unattainable target for the assessment of
oxygenation in physiological processes and disease. We discover a new principle
underlying light fluence in tissues, which describes the wavelength dependence
of light fluence as an affine function of a few reference base spectra,
independently of the specific distribution of tissue optical properties. This
finding enables the introduction of a previously undocumented concept termed
eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively
account for wavelength dependent light attenuation without explicit knowledge
of the tissue optical properties. We validate eMSOT in more than 2000
simulations and with phantom and animal measurements. We find that eMSOT can
quantitatively image tissue sO2 reaching in many occasions a better than
10-fold improved accuracy over conventional spectral optoacoustic methods.
Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor;
revealing so far unattainable tissue physiology patterns. Last, we related
eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2
images and tissue perfusion and hypoxia maps obtained by correlative
histological analysis
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Content-Based Image Retrieval (CBIR) systems are powerful search tools in
image databases that have been little applied to hyperspectral images.
Relevance feedback (RF) is an iterative process that uses machine learning
techniques and user's feedback to improve the CBIR systems performance. We
pursued to expand previous research in hyperspectral CBIR systems built on
dissimilarity functions defined either on spectral and spatial features
extracted by spectral unmixing techniques, or on dictionaries extracted by
dictionary-based compressors. These dissimilarity functions were not suitable
for direct application in common machine learning techniques. We propose to use
a RF general approach based on dissimilarity spaces which is more appropriate
for the application of machine learning algorithms to the hyperspectral
RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over
a real hyperspectral dataset.Comment: In Pattern Recognition Letters (2013
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