Simulation of acquisition shifts in T2 Flair MR images to stress test AI
  segmentation networks
Christiane Posselt Mehmet Yigit Avci Mehmet Yigitsoy Patrick Schünke Christoph Kolbitsch Tobias Schäffter Stefanie Remmele
Purpose: To provide a simulation framework for routine neuroimaging test
data, which allows for "stress testing" of deep segmentation networks against
acquisition shifts that commonly occur in clinical practice for T2 weighted
(T2w) fluid attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging
(MRI) protocols.
  Approach: The approach simulates "acquisition shift derivatives" of MR images
based on MR signal equations. Experiments comprise the validation of the
simulated images by real MR scans and example stress tests on state-of-the-art
MS lesion segmentation networks to explore a generic model function to describe
the F1 score in dependence of the contrast-affecting sequence parameters echo
time (TE) and inversion time (TI).
  Results: The differences between real and simulated images range up to 19 %
in gray and white matter for extreme parameter settings. For the segmentation
networks under test the F1 score dependency on TE and TI can be well described
by quadratic model functions (R^2 > 0.9). The coefficients of the model
functions indicate that changes of TE have more influence on the model
performance than TI.
  Conclusions: We show that these deviations are in the range of values as may
be caused by erroneous or individual differences of relaxation times as
described by literature. The coefficients of the F1 model function allow for
quantitative comparison of the influences of TE and TI. Limitations arise
mainly from tissues with the low baseline signal (like CSF) and when the
protocol contains contrast-affecting measures that cannot be modelled due to
missing information in the DICOM header.