Real-time ultrasound image reconstruction as an inverse problem on a GPU

Ultrasonic image reconstruction methods based on inverse problems have been shown to produce sharp, high-quality images using more information about the acquisition process in its processing. This improved reconstruction has high computational cost, usually requiring to solve large systems and making real-time imaging very difficult. Parallelizing the reconstruction using graphics processing units (GPU) can significantly accelerate this processing, but the amount of memory needed by current system models is high for current GPU capacity. This paper presents a new system model to halve this memory requirement; it exploits the symmetry of the point spread functions (PSF) of the system matrix that occurs when symmetric transducers are used for acquisition. In this case, only one of the two symmetric PSFs needs to be stored; the other function is produced by reordering the stored one. Thus, we can reconstruct ultrasound images that are twice as large, making real-time reconstruction on a GPU possible for this application.