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.

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.

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.

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.