Fast unmixing of multispectral optoacoustic data with vertex component analysis

Deán-Ben, X. Luís; Deliolanis, Nikolaos C.; Ntziachristos, Vasilis; Razansky, Daniel

doi:10.1016/j.optlaseng.2014.01.027

Cited by 23 publications

(14 citation statements)

References 29 publications

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“…2b shows the unmixed distributions of the wild-type and mutant proteins obtained with an unmixing algorithm termed vertex component analysis (VCA). 25,26 The temporal profiles for 488 nm excitation are also displayed. This unmixing algorithm allows isolating the contribution of both proteins even for a signal level which is orders of magnitude lower than that of blood.…”

Section: Resultsmentioning

confidence: 99%

Imaging the distribution of photoswitchable probes with temporally-unmixed multispectral optoacoustic tomography

et al. 2016

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Synthetic and genetically encoded chromo-and fluorophores have become indispensable tools for biomedical research enabling a myriad of applications in imaging modalities based on biomedical optics. The versatility offered by the optoacoustic (photoacoustic) contrast mechanism enables to detect signals from any substance absorbing light, and hence these probes can be used as optoacoustic contrast agents. While contrast versatility generally represents an advantage of optoacoustics, the strong background signal generated by light absorption in endogeneous chromophores hampers the optoacoustic capacity to detect a photo-absorbing agent of interest. Increasing the optoacoustic sensitivity is then determined by the capability to differentiate specific features of such agent. For example, multispectral optoacoustic tomography (MSOT) exploits illuminating the tissue at multiple optical wavelengths to spectrally resolve (unmix) the contribution of different chromophores. Herein, we present an alternative approach to enhance the sensitivity and specificity in the detection of optoacoustic contrast agents. This is achieved with photoswitchable probes that change optical absorption upon illumination with specific optical wavelengths. Thereby, temporally unmixed MSOT (tuMSOT) is based on photoswitching the compounds according to defined schedules to elicit specific time-varying optoacoustic signals, and then use temporal unmixing algorithms to locate the contrast agent based on their particular temporal profile. The photoswitching kinetics is further affected by light intensity, so that tuMSOT can be employed to estimate the light fluence distribution in a biological sample. The performance of the method is demonstrated herein with the reversibly switchable fluorescent protein Dronpa and its fast-switching fatigue resistant variant Dronpa-M159T.

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Section: Resultsmentioning

confidence: 99%

Imaging the distribution of photoswitchable probes with temporally-unmixed multispectral optoacoustic tomography

et al. 2016

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“…For unmixing, the known absorption spectra of oxygenated and deoxygenated hemoglobin were used whereas the spectra of AF750 and GNR were adopted from the results obtained with a blind unmixing procedure [45] in order to compensate for the spectral coloring effects at deep tissue locations [29]. In this scenario, accuracy of the retrieved spectra is ensured due to the local confinement of the imaging agents.…”

Section: Methodsmentioning

confidence: 99%

Constrained Inversion and Spectral Unmixing in Multispectral Optoacoustic Tomography

Ding

Deán‐Ben

Burton

et al. 2017

IEEE Trans. Med. Imaging

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Accurate extraction of physical and biochemical parameters from optoacoustic images is often impeded due to the use of unrigorous inversion schemes, incomplete tomographic detection coverage or other experimental factors that cannot be readily accounted for during the image acquisition and reconstruction process. For instance, inaccurate assumptions in the physical forward model may lead to negative optical absorption values in the reconstructed images. Any artifacts present in the single wavelength optoacoustic images can be significantly aggravated when performing a two-step reconstruction consisting in acoustic inversion and spectral unmixing aimed at rendering the distributions of spectrally-distinct absorbers. We investigate a number of algorithmic strategies with non-negativity constraints imposed at the different phases of the reconstruction process. Performance is evaluated in cross-sectional multispectral optoacoustic tomography (MSOT) recordings from tissue-mimicking phantoms and in vivo mice embedded with varying concentrations of contrast agents. Additional in vivo validation is subsequently performed with molecular imaging data involving subcutaneous tumors labeled with genetically-expressed iRFP proteins and organ perfusion by optical contrast agents. It is shown that constrained reconstruction is essential for reducing the critical image artifacts associated with inaccurate modeling assumptions. Furthermore, imposing the non-negativity constraint directly on the unmixed distribution of the probe of interest was found to maintain the most robust and accurate reconstruction performance in all experiments.

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“…Endmembers are in the extreme position of the feature space and represent different fundamental processes. The selection of endmembers is crucial for the temporal unmixing model; here, endmembers are selected by the geometric vertex method [38,39]. The curves of empirical orthogonal function provide vegetation increasing/decreasing trend as prior information.…”

Section: Temporal Unmixing Analysismentioning

confidence: 99%

Detection of Decreasing Vegetation Cover Based on Empirical Orthogonal Function and Temporal Unmixing Analysis

Chen

Xing³

et al. 2017

Mathematical Problems in Engineering

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Vegetation plays an important role in the energy exchange of the land surface, biogeochemical cycles, and hydrological cycles. MODIS (MODerate-resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) is considered as a quantitative indicator for examining dynamic vegetation changes. This paper applied a new method of integrated empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) to detect the vegetation decreasing cover in Jiangsu Province of China. The empirical orthogonal function (EOF) statistical results provide vegetation decreasing/increasing trend as prior information for temporal unmixing analysis. Temporal unmixing analysis (TUA) results could reveal the dominant spatial distribution of decreasing vegetation. The results showed that decreasing vegetation areas in Jiangsu are distributed in the suburbs and newly constructed areas. For validation, the vegetation’s decreasing cover is revealed by linear spectral mixture from Landsat data in three selected cities. Vegetation decreasing areas pixels are also calculated from land use maps in 2000 and 2010. The accuracy of integrated empirical orthogonal function and temporal unmixing analysis method is about 83.14%. This method can be applied to detect vegetation change in large rapidly urbanizing areas.

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Fast unmixing of multispectral optoacoustic data with vertex component analysis

Cited by 23 publications

References 29 publications

Imaging the distribution of photoswitchable probes with temporally-unmixed multispectral optoacoustic tomography

Imaging the distribution of photoswitchable probes with temporally-unmixed multispectral optoacoustic tomography

Constrained Inversion and Spectral Unmixing in Multispectral Optoacoustic Tomography

Detection of Decreasing Vegetation Cover Based on Empirical Orthogonal Function and Temporal Unmixing Analysis

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