Multispectral photoacoustic imaging has been widely explored as an emerging tool to visualize and quantify tissue chromophores noninvasively. This modality can capture the spectral absorption signature of prominent tissue chromophores, such as oxygenated, deoxygenated hemoglobin, and other biomarkers in the tissue by using spectral unmixing methods. Currently, most of the reported image processing algorithms use standard unmixing procedures, which include user interaction in the form of providing the expected spectral signatures. For translational research with patients, these types of supervised spectral unmixing can be challenging, as the spectral signature of the tissues can differ with respect to the disease condition. Imaging exogenous contrast agents and accessing their biodistribution can also be problematic, as some of the contrast agents are susceptible to change in spectral properties after the tissue interaction. In this work, we investigated the feasibility of an unsupervised spectral unmixing algorithm to detect and extract the tissue chromophores without any a-priori knowledge and user interaction. The algorithm has been optimized for multispectral photoacoustic imaging in the spectral range of 680-900 nm. The performance of the algorithm has been tested on simulated data, tissue-mimicking phantom, and also on the detection of exogenous contrast agents after the intravenous injection in mice. Our finding shows that the proposed automatic, unsupervised spectral unmixing method has great potential to extract and quantify the tissue chromophores, and this can be used in any wavelength range of the multispectral photoacoustic images.

Medical implants made of biodegradable materials are advantageous for short-term applications as fracture fixation and mechanical support during bone healing. After completing the healing process, the implant biodegrades without any long-term side effects nor any need for surgical removal. In particular, Magnesium (Mg) implants, while degrading, can cause physiological changes in the tissues surrounding the implant. The evaluation of structural remodeling is relevant, however, the functional assessment is crucial to provide information about physiological changes in tissues, which can be applied as an early marker during the healing process. Hence, non-invasive monitoring of structural and functional changes in the surrounding tissue during the healing process is essential, and the need for new assessing methods is emerging. This paper provides an assessment of Mg based implants, and an extensive review of the literature is presented with the focus on the imaging techniques for investigation of the Mg implants' biodegradation. The potential of a hybrid analysis, including Near-Infrared Spectroscopy (NIRS) and photoacoustic imaging (PAI) technology, is further discussed. A hybrid solution may play a significant role in monitoring implants and have several advantages for monitoring tissue oxygenation in addition to tissue's acidity, which is directly connected to the Mg implants degradation process. Such a hybrid assessment system can be a simple, ambulant, and less costly technology with the potential for clinically monitoring of Mg implants at site.

The assessment of tissue chromophores at a volumetric scale is vital for an improved diagnosis and treatment of a large number of diseases. Spectral photoacoustic imaging (sPAI) co-registered with high-resolution ultrasound (US) is an innovative technology that has a great potential for clinical translation as it can assess the volumetric distribution of the tissue components. Conventionally, to detect and separate the chromophores from sPAI, an input of the expected tissue absorption spectra is required. However, in pathological conditions, the prediction of the absorption spectra is difficult as it can change with respect to the physiological state. Besides, this conventional approach can also be hampered due to spectral coloring, which is a prominent distortion effect that induces spectral changes at depth. Here, we are proposing a novel data-driven framework that can overcome all these limitations and provide an improved assessment of the tissue chromophores. We have developed a superpixel spectral unmixing (SPAX) approach that can detect the most and less prominent absorber spectra and their volumetric distribution without any user interactions. Within the SPAX framework, we have also implemented an advanced spectral coloring compensation approach by utilizing US image segmentation and Monte Carlo simulations, based on a predefined library of optical properties. The framework has been tested on tissue-mimicking phantoms and also on healthy animals. The obtained results show enhanced specificity and sensitivity for the detection of tissue chromophores. To our knowledge, this is a unique framework that accounts for the spectral coloring and provides automated detection of tissue spectral signatures at a volumetric scale, which can open many possibilities for translational research.

Magnesium (Mg) alloys possess unique properties that make them ideal for use as biodegradable implants in clinical applications. However, reports on the in vivo assessment of these alloys are insufficient. Thus, monitoring the degradation of Mg and its alloys in vivo is challenging due to the dynamic process of implant degradation and tissue regeneration. Most current works focus on structural remodeling, but functional assessment is crucial in providing information about physiological changes in tissues, which can be used as an early indicator of healing. Here, we report continuous wave near-infrared spectroscopy (CW NIRS), a non-invasive technique that is potentially helpful in assessing the implant-tissue dynamic interface in a rodent model. The purpose of this study was to investigate the effects on hemoglobin changes and tissue oxygen saturation (StO) after the implantation of Mg-alloy (WE43) and titanium (Ti) implants in rats' femurs using a multiwavelength optical probe. Additionally, the effect of changes in the skin on these parameters was evaluated. Lastly, combining NIRS with photoacoustic (PA) imaging provides a more reliable assessment of tissue parameters, which is further correlated with principal component analysis.

Characterizations based on anatomically realistic phantoms are highly effective to perform accurate technical validation of imaging systems. Specifically for photoacoustic imaging (PAI), although a variety of phantom models with simplified geometries are reported, an unmet need still exists to establish morphologically realistic heterogeneous pre-clinical phantoms. So the development of a mouse-mimicking phantom can reduce the use of animals for the validation and standardization studies of pre-clinical PAI systems and thus eventually translate the PAI technology to clinical research.