Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.

Detection of intrinsic or extrinsically administered chromophores and photo-absorbing nanoparticles has been achieved by multi-spectral optoacoustic tomography (MSOT). The detection sensitivity of MSOT depends not only on the signal to noise ratio considerations, as in conventional optoacoustic (photoacoustic) tomography implementations, but also on the ability to resolve the molecular targets of interest from the absorbing tissue background by means of spectral unmixing or sub-pixel detection methods. However, it is not known which unmixing methods are optimally suited for the characteristics of multispectral optoacoustic images. In this work we investigated the performance of different sub-pixel detection methods, typically used in remote sensing hyperspectral imaging, within the context of MSOT. A quantitative comparison of the different algorithmic approaches was carried out in an effort to identify methods that operate optimally under the particulars of molecular imaging applications. We find that statistical sub-pixel detection methods can demonstrate a unique detection performance with up to five times enhanced sensitivity as compared to linear unmixing approximations, under the condition that the optical agent of interest is sparsely present within the tissue volume, as common when using targeted agents and reporter genes.

A key feature of optoacoustic imaging is the ability to illuminate tissue at multiple wavelengths and therefore record images with a spectral dimension. While optoacoustic images at single wavelengths reveal morphological features, in analogy to ultrasound imaging or X-ray imaging, spectral imaging concedes sensing of intrinsic chromophores and externally administered agents that can reveal physiological, cellular and subcellular functions. Nevertheless, identification of spectral moieties within images obtained at multiple wavelengths requires spectral unmixing techniques, which present a unique mathematical problem given the three-dimensional nature of the optoacoustic images. Herein we discuss progress with spectral unmixing techniques developed for multispectral optoacoustic tomography. We explain how different techniques are required for accurate sensing of intrinsic tissue chromophores such as oxygenated and deoxygenated haemoglobin versus extrinsically administered photo-absorbing agents and nanoparticles. Finally, we review recent developments that allow accurate quantification of blood oxygen saturation (sO) by transforming and solving the sO estimation problem from the spatial to the spectral domain.This article is part of the themed issue 'Challenges for chemistry in molecular imaging'.

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