Registration Metrics

Metric selection follows the intensity relationship, not the optimizer.

The differentiable path exposes native MSE, NCC, LNCC, and NGF evaluators. The classical path complements them with mutual information, packed MIND-SSC, and sealed translation metrics. Both paths consume the RITK image geometry contract; the classical path performs an explicit conversion to Leto arrays at its numeric boundary.

Packed MIND-SSC

MIND describes anatomy by comparing nearby patches within each image instead of comparing CT and MR intensities directly. RITK’s SSC pattern uses the 12 cross-axis pairs among six axial neighbours. A 3×3×3 patch produces 12 summed squared patch distances; local mean distance normalizes contrast, and the minimum-distance subtraction makes the largest response one. Five unary bits encode six levels (0–5) and pack all 12 components into the low 60 bits of one u64. XOR/popcount is therefore the exact L1 distance between quantized descriptors.

MindSscFixedPrep stores packed descriptors only for a deterministic fixed set of complete-support centers. The default cap is 8,192. It follows the Hoeffding sample requirement of 6,623 for a bounded [0,1] mean loss with ±0.02 error at 99% confidence under uniform random sampling, rounded up to the next power of two. RITK fixes the hash seed and stratifies in physical space for reproducible coverage. That statistical design explains the cap; one fixed realization does not constitute population or clinical validation.

During each pose evaluation, RITK maps every required fixed patch point into moving physical space and trilinearly samples it. Support inside ITK’s half-voxel field uses replicate-edge interpolation; support outside is zero background and remains in the fixed denominator. This preserves a pose-invariant objective while keeping persistent memory proportional to selected centers, not image voxels.

Heinrich et al. define MIND in section 3 of the 2012 paper (https://doi.org/10.1016/j.media.2012.05.008) and the compact 12-component SSC pattern in section 3.1 of the 2013 paper (https://doi.org/10.1007/978-3-642-40811-3_24).

Evaluate the identity transform before optimization. A post-registration metric is meaningful only when the identity value, transform convention, sampled frame, and optimizer state are recorded beside it.

Example Summary