A GPU-Based Implementation of ADMIRE

Multipath and off-axis scattering are two of the primary mechanisms for ultrasound image degradation. To address their impact, we have proposed Aperture Domain Model Image REconstruction (ADMIRE). This algorithm utilizes a model-based approach in order to identify and suppress sources of acoustic clutter. The ability of ADMIRE to suppress clutter and improve image quality has been demonstrated in previous works, but its use for real-time imaging has been infeasible due to its significant computational requirements. However, in recent years, the use of GPUs for general-purpose computing has enabled significant acceleration of compute-intensive algorithms. This is due to the fact that many modern GPUs have thousands of computational cores that can be utilized to perform massively parallel processing. Therefore, in this work, we have developed a GPU-based implementation of ADMIRE. The computations were distributed across two GPUs, and speedups of almost three orders of magnitude were achieved when compared to a serial CPU implementation. The frame rate depends upon various imaging parameters, and we demonstrate this using a small cyst simulation dataset and a large in-vivo kidney dataset. However, even for the large dataset, the implementation still provides the ability to process multiple frames of data per second. Due to this, it has the capability to serve as a real-time imaging framework.