Switchable and Tunable Deep Beamformer Using Adaptive Instance Normalization for Medical Ultrasound

Shujaat Khan, Jaeyoung Huh, Jong Chul Ye

Abstract: Recent proposals of deep learning-based beamformers for ultrasound imaging (US) have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be readily combined. Unfortunately, with the existing technology, a large number of beamformers need to be trained and stored for different probes, organs, depth ranges, operating frequency, and desired target &#x2018;styles&#x2019;, demanding significant resources such as training data, etc. To address this problem, here we propose a <i>switchable and tunable</i> deep beamformer that can switch between various types of outputs such as DAS, MVBF, DMAS, GCF, etc., and also adjust noise removal levels at the inference phase, by using a simple switch or tunable nozzle. This novel mechanism is implemented through Adaptive Instance Normalization (AdaIN) layers, so that distinct outputs can be generated using a single generator by merely changing the AdaIN codes. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed method for various applications.

External IDs:dblp:journals/tmi/KhanHY22