Wang, HanHanWangPerez Mejia, Eduardo JoseEduardo JosePerez MejiaRömer, FlorianFlorianRömer2024-01-082024-01-082023https://publica.fraunhofer.de/handle/publica/45851810.1109/IUS51837.2023.10308257By subsampling optimally in the spatial and tempo ral domains, ultrasound imaging can achieve high performance, while also accelerating data acquisition and reducing storage requirements. We study the design of experiment problem that attempts to find an optimal choice of the subsampling patterns, leading to a non-convex combinatorial optimization problem. Recently, deep learning was shown to provide a feasible approach for solving such problems efficiently by virtue of the softmax function as a differentiable approximation of the one-hot encoded subsampling vectors. We incorporate softmax neural networks into information theory-based and task-based algorithms, respectively, to design optimal subsampling matrices in Full Matrix Capture (FMC) measurements predicated on compressed sensing theory.enCompressed SensingDeep LearningUltrasonic Signal ProcessingCramer-Rao-BoundDDC::600 Technik, Medizin, angewandte Wissenschaftenconference paper