Fig. 3.

Plots of registration accuracy vs. $\lambda$ (col. 1), deformation qualities vs. $\lambda$ (cols. $\&3$), and accuracy vs. training steps (col. 4). Benchmarks are performed against commonly used multimodality losses (row 1), extensions of the proposed techniques (row 2), and recent modality-pair agnostic methods (row 3). Across baseline losses, CR and mCR achieve the best tradeoff between accuracy and deformation characteristics (row 1, cols. 1–3). Further, using external losses and/or negatives reduces performance and supervised pretraining does not yield notable improvements (row 2, cols. 1–3). Compared to SynthMorph-brains [15], CR and mCR obtain higher accuracy (row 3, col. 1) in the $\lambda =0.0-0.15$ and $0.0-0.3$ ranges, respectively, at the cost of more irregular warps (row 3, cols. 2–3). See Table 1 for an analysis of trading off accuracy for smoothness.