KEYWORDS: Collagen, Liver, Photoacoustic imaging, Visualization, Tissues, Singular value decomposition, Signal intensity, Photoacoustic spectroscopy, Matrices, In vivo imaging
Liver fibrosis is a global health burden characterized by excessive collagen deposition, impairing liver function. Noninvasive techniques that automatically visualize and quantify collagen content are needed for early detection and monitoring of fibrosis progression. We explored the potential of spectral photoacoustic imaging (sPAI) in monitoring collagen development during liver fibrosis. Here a novel data-driven superpixel PA unmixing (SPAX) framework, has been implemented to differentiate collagen presence and assess its correlation with fibrosis progression non-invasively without any a priori information. Overall, the in-vivo findings highlight the potential of sPAI and SPAX in non-invasively monitoring collagen dynamics and assessing fibrosis severity.
The limited frequency bandwidth of the ultrasound transducer used as receivers in photoacoustic imaging (PAI) has a significant impact on the resolution and depth of the reconstructed image. Therefore, for clinical applications, PAI may require the use of a low or high-frequency transducer depending on the application. Here we propose an adaptive spectral PAI algorithm based on prior information of multiple linear array ultrasound transducers ranging from 1-50 MHz, to retrieve tissue molecular components from micro to macro scale. The method includes multiple modules to coherently unmix molecular components at multi-scale. Results on numerical simulations and human imaging demonstrate that the adaptive method can effectively merge multiple-scale molecular imaging and substantially improve PAI quality.
High-quality photoacoustic compatible phantoms can facilitate the imaging standardization and clinical translation of this emerging technique. We optimized the receipt of a copolymer-in-oil material, which has been recently proposed as a candidate photoacoustic-compatible material. Moreover, we proposed the methodology to fabricate a realistic, durable, and photoacoustic-compatible phantom by combining image-based modeling and 3D-printing techniques for clinical application. Beyond the fabrication, a detailed optical and acoustic characterization is also provided. The proposed tissue-mimicking phantom offers a tradeoff between manufacturing abilities, durability, reproducibility, and compatibility of the material. Furthermore, the phantom is durable and stable over time under storage and repeated use.
The non-invasive assessment of biomarkers is crucial to predict chronic molecular changes and subsequent complications. Here, an AI-assisted volumetric multi-spectral photoacoustic imaging framework has been designed for enhanced visualization of tissue biomarkers and their monitoring. Besides, the multi-frequency spectral photoacoustic imaging approach has enabled multi-scale and multi-contrast imaging. The performances have been tested via tissue mimicking phantoms, at multiple-frequency bandwidths of 5−10 MHz, 10−22 MHz, and 15−29 MHz. Besides the impact of the frequency response of the ultrasound transducer on the PAI depth and resolution has been evaluated in detail, laying the fundamentals for the translation of PAI in clinics.
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