Photoacoustic polydopamine-indocyanine green (PDA-ICG) nanoprobe for detection of senescent cells.

Cellular senescence is considered an important tumour suppression mechanism in response to damage and oncogenic stress in early lesions. However, when senescent cells are not immune-cleared and persist in the tumour microenvironment, they can drive a variety of tumour-promoting activities, including cancer initiation, progression, and metastasis. Additionally, there is compelling evidence demonstrating a direct connection between chemo(radio)therapy-induced senescence and the development of drug resistance and cancer recurrence.

In Vivo Assessment of Deep Vascular Patterns in Murine Colitis Using Optoacoustic Mesoscopic Imaging.

The analysis of vascular morphology and functionality enables the assessment of disease activity and therapeutic effects in various pathologies. Raster-scanning optoacoustic mesoscopy (RSOM) is an imaging modality that enables the visualization of superficial vascular networks in vivo. In murine models of colitis, deep vascular networks in the colon wall can be visualized by transrectal absorber guide raster-scanning optoacoustic mesoscopy (TAG-RSOM).

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature.

The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion.

Tutorial on phantoms for photoacoustic imaging applications.

Significance: Photoacoustic imaging (PAI) is an emerging technology that holds high promise in a wide range of clinical applications, but standardized methods for system testing are lacking, impeding objective device performance evaluation, calibration, and inter-device comparisons. To address this shortfall, this tutorial offers readers structured guidance in developing tissue-mimicking phantoms for photoacoustic applications with potential extensions to certain acoustic and optical imaging applications.

Aim: The tutorial review aims to summarize recommendations on phantom development for PAI applications to harmonize efforts in standardization and system calibration in the field.

Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.

Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels.