'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
— In this work, we consider the task of pairwise cross-modality image registration, which may benefit
from exploiting additional images available only at training
time from an additional modality that is different to those
being registered. As an example, we focus on aligning
intra-subject multiparametric Magnetic Resonance (mpMR)
images, between T2-weighted (T2w) scans and diffusionweighted scans with high b-value (DWI_{high−b}). For the application of localising tumours in mpMR images, diffusion
scans with zero b-value (DWI_{b=0}) are considered easier to
register to T2w due to the availability of corresponding
features. We propose a learning from privileged modality
algorithm, using a training-only imaging modality DWIb=0,
to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of
3D multiparametric MRI images from 356 prostate cancer
patients and report, with statistical significance, a lowered
median target registration error of 4.34 mm, when registering the holdout DWI_{high−b} and T2w image pairs, compared
with that of 7.96 mm before registration. Results also show
that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and
other tested learning-based methods with/without the additional modality. These compared algorithms also failed
to produce any significantly improved alignment between
DWI_{high−b} and T2w in this challenging application