Super-resolution ultrasound imaging (SRUI) surpasses the diffraction limit of conventional ultrasound, enabling visualization of microvascular architecture and hemodynamics with potential applications in neurology, oncology, and cardiology. However, clinical adoption remains limited by complex parameter optimization, subjective interpretation, and time-consuming workflows. We present a multimodal artificial intelligence framework that integrates a custom SRUI platform with large language models of DeepSeek-R1 for natural language processing and of MiniCPM-V for image recognition. Clinicians issue voice commands to initiate imaging tasks, which are translated into acquisition parameters, including temporal windows and adaptive microbubble filtration. The system performs super-resolution reconstruction, extracts quantitative vascular metrics, and generates structured diagnostic reports incorporating relevant clinical context. Filtration thresholds were dynamically determined using the Microbubble Similarity Score. Structured reports were generated within approximately four minutes. Evaluation by fourteen clinicians demonstrated good structural integrity and standardized terminology. This framework streamlines SRUI workflows and supports AI-assisted, clinically contextualized super-resolution ultrasound imaging. Trial registration: Chinese Clinical Trial Registry ChiCTR2100048361 registered July 6, 2021.
Guo et al. (Sun,) studied this question.