Cross-Modality Image Registration using a Training-Time Privileged Third Modality
This work addresses a challenging medical imaging problem for prostate cancer diagnosis by enabling more accurate tumor localization through improved registration, though it is incremental as it builds on existing learning-based methods with a novel training strategy.
The paper tackles the problem of pairwise cross-modality image registration, specifically aligning T2-weighted and high b-value diffusion-weighted MRI scans for prostate cancer localization, by leveraging a training-only third modality (zero b-value diffusion scans) to improve accuracy. It reports a statistically significant reduction in median target registration error from 7.96 mm to 4.34 mm, outperforming classical and other learning-based methods.
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 diffusion-weighted 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 DWI$_{b=0}$, 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.