  Radio frequency (RF) data contain richer information compared to other data
types, such as envelope or B-mode, and employing RF data for training deep
neural networks has attracted growing interest in ultrasound image processing.
However, RF data is highly fluctuating and additionally has a high dynamic
range. Most previous studies in the literature have relied on conventional data
normalization, which has been adopted within the computer vision community. We
demonstrate the inadequacy of those techniques for normalizing RF data and
propose that individual standardization of each image substantially enhances
the performance of deep neural networks by utilizing the data more efficiently.
We compare conventional and proposed normalizations in a phase aberration
correction task and illustrate how the former enhances the generality of
trained models.
