Artificial Intelligence Assisted Multi-modal Photoacoustic-Ultrasound Imaging for Studying Renal Tissue Function and Hemodynamics.

Combined functional-anatomic imaging modalities, which integrate the benefits of visualizing gross anatomy along with the functional or metabolic information of tissue has revolutionized the world of medical imaging. However, such existing imaging modalities are very costly. An alternative option could be a hybrid modality combining contrast-enhanced ultrasound, doppler and photoacoustic imaging. In the current study, we propose an artificial intelligence assisted multi-modal imaging platform where we have used U-net model for segmenting the anatomical features from the ultrasound images obtained from an animal model study. The neural network has performed accurately for three different cases, each with a high dice score. The model was co-validated with doppler images. Further, blood perfusion and tissue oxygenation information from the predicted anatomical structures were also studied. The present findings confirm the feasibility of using this multimodal imaging modality facilitated by artificial intelligence for better understanding of the hemodynamics of the kidney.Clinical Relevance-A multi-modal imaging technique has been proposed which would provide anatomical and functional information to the clinicians for early detection and tracking of the disease prognosis. Unlike existing imaging modalities like PET-CT (Positron Emission Tomography- Computed Tomography), the proposed modality is much more costeffective and radiation free (non-ionizing nature).