Background

Ultrasound localization (UL) or super-resolution ultrasound imaging surpasses the acoustic diffraction limit of traditional ultrasounds, enhancing spatial resolution by up to ten times while retaining the ability to probe deep tissues [1,2]. This technique works by localizing microbubbles flowing through the bloodstream and compiling these positions to create a super-resolved map of the microvasculature. Furthermore, tracking microbubble positions provides detailed hemodynamic information. In contrast, photoacoustic (PA) imaging utilizes the photoacoustic effect to produce high-contrast blood vessel images and can also deliver insights into blood oxygenation, tissue components like lipids and collagen, and disease-related biomarkers [3–7].

By integrating both modalities, PA and UL (PAUL) imaging offer a powerful tool that provides anatomical, structural, functional, and even molecular information, making it an ideal candidate for comprehensive assessments in disease and cancer research [8–11], such as kidney disease [12,13], ischemic stroke [10], and breast cancer [8–11,14,15]. For instance, PAUL imaging has been used for simultaneous renal oxygenation and hemodynamics sensing [8], both of which are known to be associated with acute kidney injury (AKI) and could potentially be applied to AKI studies. Additionally, both modalities operate within the same ultrasound system, allowing interleaved dual-contrast scans for enhanced spatial and temporal alignment of complementary datasets.

While this combination holds significant promise, a major limitation is the difference in data acquisition speeds: UL imaging requires over a minute to capture enough microbubble events for reconstruction, whereas PA imaging operates at a much higher rate of 10–100 Hz. To address this challenge, we developed a computationally accelerated UL approach and integrated it with PA imaging, resulting in a faster PAUL imaging system [8].

This protocol specifically supports the development and optimization of fast PAUL imaging, expanding its use in cancer, neuroscience, nephrology, and immunology research. By improving imaging speed and dual-modality integration, our protocol aims to promote the broader adoption and impact of PAUL imaging across diverse clinical research fields.