  A Transfer Function approach was recently demonstrated to mitigate data
mismatches at the acquisition level for a single ultrasound scanner in deep
learning (DL) based quantitative ultrasound (QUS). As a natural progression, we
further investigate the transfer function approach and introduce a
Machine-to-Machine (M2M) Transfer Function, which possesses the ability to
mitigate data mismatches at a machine level, i.e., mismatches between two
scanners over the same frequency band. This ability opens the door to
unprecedented opportunities for reducing DL model development costs, enabling
the combination of data from multiple sources or scanners, or facilitating the
transfer of DL models between machines with ease. We tested the proposed method
utilizing a SonixOne machine and a Verasonics machine. In the experiments, we
used a L9-4 array and conducted two types of acquisitions to obtain calibration
data: stable and free-hand, using two different calibration phantoms. Without
the proposed calibration method, the mean classification accuracy when applying
a model on data acquired from one system to data acquired from another system
was approximately 50%, and the mean AUC was about 0.40. With the proposed
method, mean accuracy increased to approximately 90%, and the AUC rose to the
0.99. Additional observations include that shifts in statistics for the z-score
normalization had a significant impact on performance. Furthermore, the choice
of the calibration phantom played an important role in the proposed method.
Additionally, robust implementation inspired by Wiener filtering provided an
effective method for transferring the domain from one machine to another
machine, and it can succeed using just a single calibration view without the
need for multiple independent calibration frames.
