NVIDIA and researchers from Siemens Healthineers teamed up to find answers to these questions. The result of this work is a reconstruction model we are releasing called NV-Raw2Insights-US.

At its core, ultrasound is not an image—it’s sound. What clinicians ultimately see on the screen is a reconstructed picture built from millions of tiny echoes returning from the body. But in that reconstruction process, much of the original signal—the richness of how sound actually moved through tissue—is simplified or lost.

Our approach starts earlier. Instead of working from finished images, NV-Raw2Insights-US learns directly from the raw signals captured by the ultrasound probe—the closest representation of how sound truly interacts with the body. This allows the model to “listen” more carefully and understand how each patient uniquely shapes those sound waves. Our vision is to enable end-to-end AI for ultrasound imaging and this is the first step towards that vision. We call this class of models Raw2Insights.

In this first Raw2Insights application we estimate speed of sound for adaptive image focusing. The result is a system that can generate a personalized map of sound speed for each patient—and use it to correct the image in real time. What once required complex, time-consuming computation is now performed in a single AI pass. This is the shift from raw ultrasound channel data to actionable insight: an AI system that doesn’t just process ultrasound images, but actively understands and adapts to the physics of each individual patient.