Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of
  Deep Learning?
Zhangxing Bian Ahmed Alshareef Shuwen Wei Junyu Chen Yuli Wang Jonghye Woo Dzung L. Pham Jiachen Zhuo Aaron Carass Jerry L. Prince
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for
quantifying tissue motion and strain during deformation. However, a phenomenon
known as tag fading, a gradual decrease in tag visibility over time, often
complicates post-processing. The first contribution of this study is to model
tag fading by considering the interplay between $T_1$ relaxation and the
repeated application of radio frequency (RF) pulses during serial imaging
sequences. This is a factor that has been overlooked in prior research on tMRI
post-processing. Further, we have observed an emerging trend of utilizing raw
tagged MRI within a deep learning-based (DL) registration framework for motion
estimation. In this work, we evaluate and analyze the impact of commonly used
image similarity objectives in training DL registrations on raw tMRI. This is
then compared with the Harmonic Phase-based approach, a traditional approach
which is claimed to be robust to tag fading. Our findings, derived from both
simulated images and an actual phantom scan, reveal the limitations of various
similarity losses in raw tMRI and emphasize caution in registration tasks where
image intensity changes over time.