A novel 2D particle tracking method, that uses 1) iteration, 2) fast quadratic sub-pixel estimation (with only 28 multiplications per movement), and 3) a previous kernel, has been evaluated and compared with a full-search block-matching method. The comparison with high-frequency ultrasound data (40 MHz) was conducted in silico and on phantoms, which comprised lateral, diagonal, and ellipsoidal movement patterns with speeds of 0–15 mm/s. The mean tracking error was reduced by 68% in silico and 71% for the phantom measurements. When only sub-pixel estimation was used, the decrease in the tracking error was 61% in silico and 57% for the phantom measurements. As well as decreasing the tracking error, the new method only used 70% of the computational time needed by the full-search block-matching method. With a fast method having good tracking ability for high-frequency ultrasound data, we now have a tool to better investigate tissue movements and its dynamic functionality.