Constrained Inversion and Spectral Unmixing in Multispectral Optoacoustic Tomography

Abstract

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.

Sonophore-enhanced nanoemulsions for optoacoustic imaging of cancer

“…Namely, the following linear regression methods were used: (1) direct classical least squares (DCLS) using (a) UV/Vis absorbance or (b) optoacoustic (OA) reference spectra; (2) non-negative least squares (NN-LS); as well as blind unmixing methods: (3) principal component analysis (PCA), and (4) independent component analysis (ICA). 36 – 38 The results obtained from these different techniques are shown in Fig. 3 .…”

“…When it comes to the specific detection of sonophores, spectral analysis techniques become important. 35 – 38 Spectral unmixing is particularly necessary in an in vivo setting, with complex anatomy, biological variation, and intrinsic sonophores ( e.g. hemoglobin) that potentially produce high background signal.…”

Noninvasive Anatomical and Functional Imaging of Orthotopic Glioblastoma Development and Therapy using Multispectral Optoacoustic Tomography

“…In order to facilitate quantitative data analysis and avoid negative value artifacts commonly present in optoacoustic images reconstructed with back projection algorithms [26] , we employed a combined model-based reconstruction and unmixing framework incorporating non-negative constrained inversion [25] . Basically, the method is based on the discretization of the optoacoustic forward model to build a linear set of equations, associated to a model-matrix, that represent the recorded pressure values at different locations and different wavelengths.…”

“…For this, an orthotopic U87MG glioma model was employed, which has been extensively used in preclinical studies to identify therapeutic agents [22] , [23] and whose molecular profile is known to simulate a subclass of human glioblastoma [24] . Additionally, in order to quantitatively map sO 2 in whole mouse brains, we employed a newly-developed non-negative constrained approach for combined MSOT image reconstruction and unmixing [25] , the advantage of which compared to state-of-the-art back projection methods being reduced critical image artifacts, thus maximizing the available information. Specifically, the objectives of this study have been to investigate the MSOT performance in identifying orthotopic gliomas based on single wavelength information, evaluate their growth over time, visualize sO 2 in the tumor versus the rest of the brain and investigate hemodynamic changes upon administration of a vascular disruptive agent.…”

Optoacoustic imaging of GLP-1 Receptor with a near-infrared exendin-4 analog

“…), and the algorithm gives out a score. Hence the name CLS score [38][39][40] . In our case, three references were included and used for spectral unmixing of hemoglobin, deoxyhemoglobin and E4x12-Cy7.…”