Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth.

Longitudinal imaging of T cell-based immunotherapy with multi-spectral, multi-scale optoacoustic tomography.

Most imaging studies of immunotherapy have focused on tracking labeled T cell biodistribution in vivo for understanding trafficking and homing parameters and predicting therapeutic efficacy by the presence of transferred T cells at or in the tumour mass. Conversely, we investigate here a novel concept for longitudinally elucidating anatomical and pathophysiological changes of solid tumours after adoptive T cell transfer in a preclinical set up, using previously unexplored in-tandem macroscopic and mesoscopic optoacoustic (photoacoustic) imaging. We show non-invasive in vivo observations of vessel collapse during tumour rejection across entire tumours and observe for the first time longitudinal tumour rejection in a label-free manner based on optical absorption changes in the tumour mass due to cellular decline.

Synthetic data framework to estimate the minimum detectable concentration of contrast agents for multispectral optoacoustic imaging of small animals.

The concentrations of contrast agents for optoacoustic imaging of small animals must usually be optimized through extensive pilot experiments on a case-by-case basis. The present work describes a streamlined approach for determining the minimum detectable concentration (MDC) of a contrast agent given experimental conditions and imaging system parameters. The developed Synthetic Data Framework (SDF) allows estimation of MDCs of various contrast agents under different tissue conditions without extensive animal experiments.

A Bayesian Approach to Eigenspectra Optoacoustic Tomography.

The quantification of hemoglobin oxygen saturation (sO2) with multispectral optoacoustic (OA) (photoacoustic) tomography (MSOT) is a complex spectral unmixing problem, since the OA spectra of hemoglobin are modified with tissue depth due to depth (location) and wavelength dependencies of optical fluence in tissue. In a recent work, a method termed eigenspectra MSOT (eMSOT) was proposed for addressing the dependence of spectra on fluence and quantifying blood sO2 in deep tissue. While eMSOT offers enhanced sO2 quantification accuracy over conventional unmixing methods, its performance may be compromised by noise and image reconstruction artifacts.

Coded-Aperture Compressed Sensing X-Ray Luminescence Tomography.

Objective: The purpose of this work is to introduce and study a novel imaging geometry for X-ray luminescence computed tomography (XLCT), termed coded aperture compressive X-ray luminescence tomography (CAC-XLCT).

Methods: CAC-XLCT is studied through simulations of X-ray and diffuse light propagation and the implementation of a compressed sensing image reconstruction algorithm.

Results: CAC-XLCT is compared against cone beam XLCT considering simulated targets with varying complexity, and it is found to offer a remarkable enhancement in spatial resolution and image quality with only a small overhead in image acquisition time.