UNFOLDING MODEL-BASED BEAMFORMING FOR HIGH QUALITY ULTRASOUND IMAGING

Aperture Domain Model Image REconstruction (ADMIRE) is an advanced ultrasound beamforming method that uses a model-based approach to suppress sources of acoustic clutter and improve ultrasound image quality. However, it requires solving an ill-posed inverse problem for which regularization is utilized. As a result, the iterative nature of solving this problem is computationally expensive, and the choice of regularization bounds the fidelity of the obtained solution. Therefore, in this work, we pose ADMIRE as a sparse coding problem and unfold the iterations of the iterative shrinkage and thresholding algorithm (ISTA), training it end to end to yield learned ISTA (LISTA). This enables effective tailoring of the solver to the specific data distribution and task at hand, while enjoying higher data efficiency and robustness than generic deep learning methods. Evaluation of our proposed method on both simulated cyst data and in vivo liver data demonstrates its potential to outperform conventional ADMIRE.