Multispectral opto-acoustic tomography (MSOT) of the brain and glioblastoma characterization.

Brain research depends strongly on imaging for assessing function and disease in vivo. We examine herein multispectral opto-acoustic tomography (MSOT), a novel technology for high-resolution molecular imaging deep inside tissues. MSOT illuminates tissue with light pulses at multiple wavelengths and detects the acoustic waves generated by the thermoelastic expansion of the environment surrounding absorbing molecules. Using spectral unmixing analysis of the data collected, MSOT can then differentiate the spectral signatures of oxygenated and deoxygenated hemoglobin and of photo-absorbing agents and quantify their concentration. By being able to detect absorbing molecules up to centimeters deep in the tissue it represents an ideal modality for small animal brain imaging, simultaneously providing anatomical, hemodynamic, functional, and molecular information. In this work we examine the capacity of MSOT in cross-sectional brain imaging of mice. We find unprecedented optical imaging performance in cross-sectional visualization of anatomical and physiological parameters of the mouse brain. For example, the potential of MSOT to characterize ischemic brain areas was demonstrated through the use of a carbon dioxide challenge. In addition, indocyanine green (ICG) was injected intravenously, and the kinetics of uptake and clearance in the vasculature of the brain was visualized in real-time. We further found that multiparameter, multispectral imaging of the growth of U87 tumor cells injected into the brain could be visualized through the intact mouse head, for example through visualization of deoxygenated hemoglobin in the growing tumor. We also demonstrate how MSOT offers several compelling features for brain research and allows time-dependent detection and quantification of brain parameters that are not available using other imaging methods without invasive procedures.

Multispectral optoacoustic tomography by means of normalized spectral ratio.

Quantification of biomarkers using multispectral optoacoustic tomography can be challenging due to photon fluence variations with depth and spatially heterogeneous tissue optical properties. Herein we introduce a spectral ratio approach that accounts for photon fluence variations. The performance and imaging improvement achieved with the proposed method is showcased both numerically and experimentally in phantoms and mice.

Model-based optoacoustic inversions with incomplete projection data.

PURPOSE: Optoacoustic imaging is an emerging noninvasive imaging modality that can resolve optical contrast through several millimeters to centimeters of tissue with diffraction-limited resolution of ultrasound. Yet, quantified reconstruction of tissue absorption maps requires optoacoustic signals to be collected from as many locations around the object as possible. In many tomographic imaging scenarios, however, only limited-view or partial projection data are available, which has been shown to generate image artifacts and overall loss of quantification accuracy. METHODS: In this article, the recently introduced interpolated-matrix-model optoacoustic inversion method is tested in different limited-view scenarios and compared to the standard backprojection algorithm. Both direct (TGSVD) and iterative (PLSQR) regularization methods are proposed to improve the accuracy of image reconstructions with their performance tested on simulated and experimental data. RESULTS: While for full-view tomographic data the model-based inversion has been generally shown to attain higher reconstruction accuracy compared to backprojection algorithms, the incomplete tomographic datasets lead to ill-conditioned forward matrices and, consequently, to error-prone inversions, with strong artifacts following a distinct ripple-type pattern. The proposed regularization techniques are shown to stabilize the inversion and eliminate the artifacts. CONCLUSIONS: Overall, it has been determined that the regularized interpolated-matrix-model-based optoacoustic inversions show higher accuracy than reconstructions with the standard backprojection algorithm. Finally, the combination of model-based inversion with PLSQR or TGSVD regularization methods can lead to an accurate reconstruction of limited-projection angle optoacoustic data and practical systems for optoacoustic imaging in many realistic cases where the full-view dataset is unavailable.

Optoacoustic tomography with varying illumination and non-uniform detection patterns.

Quantification of tissue morphology and biomarker distribution by means of optoacoustic tomography is an important and longstanding challenge, mainly caused by the complex heterogeneous structure of biological tissues as well as the lack of accurate and robust reconstruction algorithms. The recently introduced model-based inversion approaches were shown to mitigate some of reconstruction artifacts associated with the commonly used back-projection schemes, while providing an excellent platform for obtaining quantified maps of optical energy deposition in experimental configurations of various complexity. In this work, we introduce a weighted model-based approach, capable of overcoming reconstruction challenges caused by per-projection variations of object's illumination and other partial illumination effects. The universal weighting procedure is equally shown to reduce reconstruction artifacts associated with other experimental imperfections, such as non-uniform transducer sensitivity fields. Significant improvements in image fidelity and quantification are showcased both numerically and experimentally on tissue phantoms and mice.

Efficient framework for model-based tomographic image reconstruction using wavelet packets.

The use of model-based algorithms in tomographic imaging offers many advantages over analytical inversion methods. However, the relatively high computational complexity of model-based approaches often restricts their efficient implementation. In practice, many modern imaging modalities, such as computed-tomography, positron-emission tomography, or optoacoustic tomography, normally use a very large number of pixels/voxels for image reconstruction. Consequently, the size of the forward-model matrix hinders the use of many inversion algorithms. In this paper, we present a new framework for model-based tomographic reconstructions, which is based on a wavelet-packet representation of the imaged object and the acquired projection data. The frequency localization property of the wavelet-packet base leads to an approximately separable model matrix, for which reconstruction at each spatial frequency band is independent and requires only a fraction of the projection data. Thus, the large model matrix is effectively separated into a set of smaller matrices, facilitating the use of inversion schemes whose complexity is highly nonlinear with respect to matrix size. The performance of the new methodology is demonstrated for the case of 2-D optoacoustic tomography for both numerically generated and experimental data.