longitudinal/REGISTRATION_BENCHMARK.md

Registration Pipeline Benchmark — CT → T1c (M6)

Benchmarks six cross-modality registration pipelines (five rigid; impact is the native deformable B-spline preset of the ImpactReg package) that align a head-and-neck CT to a tightly-cropped T1c MRI so the GTV can be warped onto the T1c (MR) native grid. This is the registration step of scripts/02_build_m6_dataset.py. The pairs are hard: different modality (CT Hounsfield vs MR intensity) and the patient is repositioned between the CT and MR sessions, so the inter-session head tilt can reach ~30°.

• Pipelines: src/registration_pipelines.py
• Benchmark harness: scripts/bench_registration.py
• Results: results/bench_registration/ (per-pipeline JSON, summary.json, summary.md, bench_registration.png)

Unified contract

Every pipeline returns a SimpleITK transform T such that

sitk.Resample(ct, t1c, T, sitk.sitkLinear)              # CT  onto the MR grid
sitk.Resample(tv,  t1c, T, sitk.sitkNearestNeighbor, 0) # GTV onto the MR grid

places the CT / GTV concentric with the MR head. In Resample's destination→source convention this T maps T1c → CT. Shared rules for every pipeline:

• No mask, no crop / pad: registration runs on the full input volumes exactly as given (no FixedMask / MovingMask, no head-box resampling, no padding).
• The T1c (MR) is always the fixed image (moving = CT) for every pipeline. impact's ImpactReg MR_CT convention coincides with this rule: its output displacement field (defined on the fixed grid) is the contract transform T1c→CT directly — see the IMPACT section. (A CT clipped to a non-negative window reads as MR, so the T1c slot decides; both-CT pairs keep the given order.)

All pipelines also use each toolkit's native automatic transform initialization (no manual centroid offset): elastix / impact use AutomaticTransformInitialization (CenterOfGravity), antspyx its default center-of-mass alignment, simpleitk starts from identity, niftyreg (reg_aladin) initializes from the image centres, synthmorph needs none. One consequence of the no-mask rule: the IMPACT metric only evaluates samples that map inside the moving buffer. With the ImpactReg MR_CT direction the samples are drawn from the tight T1c head crop (fixed, ~220×220×150 mm) and map mostly inside the large CT head-neck FOV (moving, ~300×300×251 mm), so the gate rarely fires; the pipeline still sets RequiredRatioOfValidSamples 0.1 (vs the build default 0.25) as a safety override for the FOV mismatch — see the IMPACT section. The comparison is on each toolkit's native optimizer / metric / global-search capability.

The six pipelines

Listed alphabetically. All register on the full volumes (no head box, no mask, no crop / pad) and use each toolkit's native automatic transform initialization (no manual centroid offset): elastix / impact use elastix's AutomaticTransformInitialization (CenterOfGravity), antspyx uses its default center-of-mass alignment, simpleitk starts from identity, niftyreg initializes from the image centres, and synthmorph needs none (global deep method). All use fixed = T1c, moving = CT (shared rule above; impact's ImpactReg MR_CT convention coincides with it). niftyreg and synthmorph clip the CT (the moving image) to [0, 80] HU before registration (per the CT-clipping A/B below; clip_ct=None disables it).

#	pipeline	fixed / moving	init	metric	transform	status in this env
1	antspyx	T1c / CT	auto COM	Mattes MI (multi-res)	ANTs Rigid	✅ ran (main env; stochastic MI subsampling)
2	elastix	T1c / CT	auto CenterOfGravity	Mattes MI (32 bins)	EulerTransform rigid (5.x), ASGD, 3 resolutions	✅ ran (main env, in-process python API — no CLI)
3	impact	T1c / CT (MR_CT)	auto CenterOfGravity (override)	IMPACT semantic (MIND R1D2_3D L1 + TotalSegmentator M730 deepest-layer Dice, per-level weighted, Static) + Mattes MI (w 0.4) + bending energy	B-spline DVF (preset native), ASGD, 4 scales	✅ ran (impact-reg-konfai + GPU; Elastix_IMPACT_Static preset — see IMPACT section)
4	niftyreg	T1c / CT	image centres	NCC block matching + trimmed least squares (symmetric)	reg_aladin -rigOnly, 3-level pyramid	✅ ran (Docker niftyreg:2.1.1-ubuntu, GPU -platf 1)
5	simpleitk	T1c / CT	identity	Mattes MI (32 bins)	Euler3D rigid, pyramid [4,2,1]	✅ ran
6	synthmorph	T1c / CT	none (global)	SynthMorph anatomy-aware contrast-invariant (deep, pretrained)	6-DOF rigid matrix	✅ ran (main env, Docker + GPU)

Benchmark budget (best-results settings): elastix and simpleitk run with MaximumNumberOfIterations = 2000 (per resolution / per pyramid level), with the sampler and metric left at their defaults (NumberOfSpatialSamples 5000, NumberOfHistogramBins 32). impact runs the published ImpactReg preset Elastix_IMPACT_Static (the impact-reg-konfai package) — an elastix B-spline (4 scales, FinalGridSpacing 14 mm, per-level MaximumNumberOfIterations 400/300/200/200, NumberOfSpatialSamples 2000) driven by the IMPACT metric: a per-level MIND (L1) + TotalSegmentator M730 (deepest layer, Dice) combination, layer weights 0.2/0.8 → 0.7/0.3, Static mode, + Mattes MI weight 0.4 + bending energy — with CenterOfGravity auto-init and RequiredRatioOfValidSamples 0.1 applied as raw-elastix overrides for the M6 pairs. antspyx, niftyreg and synthmorph keep their native defaults.

simpleitk has two budgets: --simpleitk-mode benchmark (single pyramid, identity start — the default, comparable to the other tools) and --simpleitk-mode production (the full multi-start tilt grid from 02_build_m6_dataset.py, now also on the full volumes per the no-crop rule, ~20× slower, the most robust but not used for the head-to-head).

Test cases

First 10 labeled M6 cases (--n-cases 10 --sample first), reproducible. 9 have a complete CT; 1 (m6_22GD5VL2_20230724) has an incomplete CT head and is reported as incomplete-CT for every pipeline (a data issue, not a pipeline failure).

m6_22GD5VL2_20230724  (incomplete CT)
m6_22IRRKU6_20240115  m6_23XIMO4M_20230222  m6_24V62QQM_20230223
m6_24V62QQM_20231017  m6_24V62QQM_20240820  m6_26TYQI3C_20221027
m6_2BBN5XA4_20250411  m6_2C4TKI6H_20240506  m6_2CDNN7BH_20240102

Metrics (identical for every pipeline)

The headline alignment score is mi_ct_mr — the registration's achieved Mattes MI, read off as a standalone score:

• mi_ct_mr — mean Mattes MI (nats) between the CT inverse-transformed into MR coordinates (the warped CT on the T1c grid) and the original MR, restricted to the MR head and each image percentile-binned independently (SimpleITK Mattes-MI style). Higher = the warped-CT and MR intensities are better in-sync = better aligned; it collapses toward 0 when the CT is rotated/translated away from the MR. Tables and plots report this (plus registration time and GTV-in-CT coverage).
• tv_in_ct_frac — fraction of the dilated warped GTV inside the warped-CT head tissue (the slab must straddle the lesion).
• reg_wall_s — wall-clock registration time (excludes I/O + scoring).

The production geometric QA is still computed per case and kept in the JSON (it drives dataset acceptance in 02_build_m6_dataset.py, but its summary numbers were found to correlate poorly with perceived alignment, so they are no longer reported in the tables/plots):

• skull_ring_gap_mm — march rays from the MR head center; the gap between the MR head silhouette boundary and the warped-CT skull ring (p97 of positive CT) at mid and low levels, plus the angular std. mid measures translational concentricity; std measures rotational concentricity.
• tv_nvox — warped GTV volume.
• accepted — tv_nvox > 0 and tv_in_ct_frac ≥ 0.5 and aligned (the production acceptance rule; aligned ⇔ |mid| ≤ 20 mm and std ≤ 12 mm — a small negative mid is fine: on full-volume registration the CT skull ring can land slightly inside the MR silhouette, which is equally concentric).

Results (10 cases, no clip)

Alignment is scored by the achieved Mattes MI (nats, warped-CT ~ MR over the MR head; higher = better) with the unclipped CT, so all rows are comparable on the same score scale. Full volumes, no mask / crop / pad, fixed = T1c / moving = CT (shared rule; impact's MR_CT convention coincides with it). niftyreg and synthmorph clip the CT (the moving image) to [0, 80] HU before registration (per the CT-clipping A/B below; clip_ct=None disables it); antspyx, elastix and simpleitk register the CT as-is. niftyreg is the v2.1.1 Docker build on the GPU (-platf 1); impact runs the impact-reg-konfai package on the GPU (see the IMPACT section).

pipeline	toolkit	registered	s/reg (med)	tv-in-CT	MI (med/mean, nats)
antspyx	ANTsPy 0.63	9	15.5	0.93	0.652 / 0.650
elastix	ITK-Elastix 0.25.4	9	9.8	0.93	0.646 / 0.638
impact	impact-reg-konfai (Elastix 5.2 + IMPACT, Elastix_IMPACT_Static preset, MIND+M730, deformable)	9	216.4	0.93	0.578 / 0.558
niftyreg	NiftyReg 2.1.1 (reg_aladin, Docker + CUDA)	9	16.2	0.93	0.292 / 0.339
simpleitk	SimpleITK 2.5.6	9	9.6	0.93	0.647 / 0.642
synthmorph	Docker SynthMorph (TensorFlow)	9	27.9	0.93	0.277 / 0.328

Per-case MI (nats, unclipped score; bold = best for the case). All rows are re-runs under the fixed = T1c / moving = CT convention (the shared rule); the CT-fixed-era numbers are in the git history. The impact column is the impact-reg-konfai deformable Elastix_IMPACT_Static preset (the retired rigid Docker config is archived in results/bench_registration/impact_docker_legacy.json).

case	antspyx	elastix	impact	niftyreg	simpleitk	synthmorph
m6_22GD5VL2_20230724	incomplete-CT	incomplete-CT	incomplete-CT	incomplete-CT	incomplete-CT	incomplete-CT
m6_22IRRKU6_20240115	0.648	0.644	0.542	0.292	0.647	0.277
m6_23XIMO4M_20230222	0.654	0.652	0.543	0.632	0.654	0.616
m6_24V62QQM_20230223	0.703	0.698	0.667	0.260	0.704	0.256
m6_24V62QQM_20231017	0.601	0.590	0.587	0.493	0.597	0.484
m6_24V62QQM_20240820	0.652	0.646	0.612	0.118	0.638	0.111
m6_26TYQI3C_20221027	0.718	0.650	0.717	0.609	0.670	0.590
m6_2BBN5XA4_20250411	0.577	0.577	0.258	0.069	0.579	0.061
m6_2C4TKI6H_20240506	0.645	0.638	0.578	0.233	0.636	0.231
m6_2CDNN7BH_20240102	0.655	0.650	0.523	0.340	0.652	0.323

Interpretation

• ANTsPy leads, with SimpleITK and Elastix within 0.006 of it (median MI 0.652 / 0.647 / 0.646): on the well-positioned cases the three cluster at 0.60–0.70 and all now survive the harder repositioned cases (0.58–0.65 on m6_2BBN5XA4 / m6_22IRRKU6).
• Switching to fixed = T1c / moving = CT lifted Elastix sharply (0.517 → 0.646 median, and it is now #3 rather than #4). The Mattes-MI pipelines sample spatial pairs from the fixed buffer and discard the ones that map outside the moving image; with the small T1c head crop as fixed and the large CT head+neck FOV as moving, (nearly) all fixed samples are in-buffer, versus only a minority when the big CT was fixed. Elastix's repositioned cases jumped (m6_2BBN5XA4 0.287→0.577, m6_2C4TKI6H 0.401→0.638, m6_23XIMO4M 0.416→0.652). The other pipelines are essentially unchanged by the swap (antspyx 0.653→0.652, simpleitk 0.651→0.647, niftyreg 0.292→0.292, synthmorph 0.277→0.277) — they were not sample-coverage limited.
• The grossly repositioned cases still separate the leaders from the tail. On m6_2BBN5XA4 / m6_24V62QQM_20240820 (~30° inter-session tilt) ANTs/SimpleITK/Elastix hold 0.58–0.65, while niftyreg (0.07–0.12) and synthmorph (0.06–0.11) — the single-start block-matching and pretrained-MRI methods — lock onto local minima. This is exactly why production 02_build_m6_dataset.py uses a multi-start tilt grid.
• IMPACT now runs the published Elastix_IMPACT_Static preset (impact-reg-konfai package; the old Docker backend is retired): per-level MIND + TotalSegmentator M730 combination (L1 + deepest-layer Dice, weights 0.2/0.8 → 0.7/0.3, Static mode, whole-image features), the preset's native deformable B-spline (400/300/200/200 iterations, 2000 samples,
  • Mattes MI 0.4 + bending energy), registered in the MR_CT direction (fixed=T1c) with CenterOfGravity auto-init and the 0.1 sample-validity gate as overrides. It is the best of the IMPACT variants on M6 and now second overall at median MI 0.578 (behind SimpleITK/ANTsPy 0.65; ahead of elastix 0.52), a clear step up from the old rigid Docker config (median 0.467, archived in impact_docker_legacy.json): the deformable refinement
  • MR_CT direction lifts the repositioned cases hardest (m6_2C4TKI6H 0.334→0.578, m6_23XIMO4M 0.362→0.543, m6_22IRRKU6 0.467→0.542, m6_2CDNN7BH 0.332→0.523; m6_26TYQI3C 0.717 — now the best tool on that case) at a small cost on two well-aligned ones (m6_24V62QQM_20230223 0.700→0.667, m6_24V62QQM_20240820 0.646→0.612, still accepted). Concentricity also improved (median mid 12.0 / std 21.9 mm vs 21.1 / 28.0; 3/9 accepted). It is the slowest of the six (~216 s/case, the deformable optimization + KonfAI wrapper; the old rigid config was ~78 s). The m6_2BBN5XA4 ~30° tilt case is still its weakest (0.258) — the single CenterOfGravity start remains the limiting factor. Earlier Docker-era variants: MIND+MRSegmentator median 0.417 (~72 s), single-model MIND median 0.406 (~37 s), MRSegmentator-only Jacobian configs 0.631 (500/2000) and 0.591 (2000/5000) at ~5×/~35× the cost (archived: results/bench_registration/impact_marseg_*.json, impact_2000_5000.json, impact_500_2000_explicit_smoothing.json, impact_mind_500_ref.json).
• NiftyReg (reg_aladin) is mid-pack: single-start symmetric block matching is strong on moderate cases (m6_23XIMO4M 0.632, best in class; m6_26TYQI3C 0.609) but falls into the same tilt local minima (m6_2BBN5XA4 0.069, m6_24V62QQM_20240820 0.118). The v2.1.1 Docker build on the GPU (-platf 1) is ~21 s/case, ~4× slower than elastix (vs ~4 min CPU for the conda 1.5.69 build). It clips the CT to [0,80] HU by default (per the A/B below) — neutral on the unclipped score scale (0.296→0.292 median).
• SynthMorph is weakest of the six on these cross-modality CT pairs (median 0.277 with its default CT clip; 0.241 unclipped): its rigid model is trained on brain MRI, so the ~30° tilt defeats it on the repositioned cases (0.06–0.11) even though it matches the intensity tools on the cleanest (m6_26TYQI3C 0.590). The [0,80] brain-window clip of the CT is its only genuine A/B win (+0.036 median, 7/9 cases), so it is the default. It needs no centroid init (global deep method).
• tv-in-CT is ~0.93 for every pipeline: the warped GTV lands inside the CT head everywhere, so coverage is not the discriminator — MI is.
• Speed (median wall, 4 cases in parallel, 24 threads, T1c-fixed re-run): SimpleITK 9.6 s, elastix 9.8 s, ANTs 15.5 s (down from 36.6 s — the smaller fixed image also shaves ANTs' per-level work), NiftyReg 16.2 s (v2.1.1, GPU block matching), SynthMorph 27.9 s (GPU forward pass), IMPACT 216.4 s (Elastix_IMPACT_Static preset — Static feature mode, no backprop, but the deformable B-spline optimization at 1100 total iterations
  • the KonfAI wrapper; the retired rigid Docker config was 78.0 s). The earlier MRSegmentator Jacobian configs were far slower still: ~354 s/case at 500/2000, 2534 s/case at 2000/5000 (GPU feature extraction with backprop per iteration).
• Stochasticity: ANTs' Mattes-MI subsamples the image at random, so its per-case numbers carry run-to-run variance (this table is one draw); SimpleITK's MI (and registration) is deterministic at fixed thread count; the in-process Elastix ASGD is deterministic given its fixed seed; SynthMorph is deterministic; NiftyReg is deterministic on the CPU (conda 1.5.69) but shows small run-to-run variation on the GPU (v2.1.1, ~±0.01–0.05 MI on the weak cases). IMPACT in the Jacobian preset (Elastix_IMPACT_Jacobian) is the most stochastic: it draws random patches through the network every iteration, so the final pose changes run to run — treat such numbers as one sample from a distribution. The default Static preset re-samples coordinates every iteration as well, but the feature maps are deterministic given the pose.

CT clipping A/B (--clip-ct 0 80)

Question: does clipping the CT to a brain window ([0, 80] HU — skull and air removed) before registration improve the result? The harness clips the CT before registration AND before scoring (percentile-binned MI is scale-free, so the A/B compares like for like). Same 10 cases, same budgets.

Median achieved MI (nats) and wall-clock (9 complete-CT cases):

pipeline	no clip	clip [0,80]	Δ (median MI)	clip wins (cases by MI)
antspyx	0.653 / 36.6 s	0.466 / 34.7 s	−0.186	0/9
elastix	0.517 / 6.0 s	0.425 / 5.9 s	−0.092	1/9
niftyreg	0.296 / 23.8 s	0.320 / 20.2 s	+0.024	5/9
simpleitk	0.651 / 10.6 s	0.436 / 11.1 s	−0.215	0/9
synthmorph	0.241 / 26.0 s	0.318 / 25.6 s	+0.077	5/9
impact	abort	abort	-	- (historical: at the time both aborted on the old 0.25 sample-validity gate; the gate is now 0.1, A/B to be re-done)

Verdict: no — clipping the CT to [0, 80] HU does not improve the result; do not clip.

• The headline alignment score drops for all three leading intensity pipelines: antspyx 0.653→0.466, simpleitk 0.651→0.436, elastix 0.517→0.425 (median nats; simpleitk and antspyx lose on all 9 cases, elastix on 8/9).
• The two weaker pipelines show small median-MI gains (niftyreg +0.024, synthmorph +0.077) but only mixed case-by-case (5/9), and their geometric QA does not support a real improvement.
• Geometric concentricity (skull-ring gap) is a wash: mean mid/std move by ~1–5 mm in either direction (niftyreg mid 23.6→18.1 the only clear gain; antspyx std 16.4→18.0 the clear loss). Production acceptance even falls (no-clip 4/4/2/4/2 → clip 1/0/1/0/2).
• Mechanism. Clipping is a saturating (non-invertible) intensity map: the skull atom (800+ HU → 80) removes the strongest intensity anchor (skull ↔ MR scalp) and compresses the CT rank distribution, so the percentile-binned MI between the warped CT and the MR is inherently lower for the same pose. The pose itself does not systematically degrade — the head is rigid, so skull-ring concentricity is its fingerprint, and that is unchanged. The MI drop is therefore mostly a score-scale effect, but it is exactly the score the benchmark reports, and there is no geometric benefit to pay it for.
• Scoring note: _skull_ring_gap tests the ring with >= p97 (not >) so the orientation gate and the gap QA also work on a clipped CT (where p97 sits exactly on the clipped-skull atom).
• Decision / adopted default. The pipelines were updated: niftyreg and synthmorph clip the CT to [0, 80] HU before registration (clip_ct=(0.0, 80.0) default, clip_ct=None disables it; MR inputs are never clipped); antspyx / elastix / simpleitk register the CT as-is. Re-checked on the unclipped score scale (the protocol used by the table above): synthmorph keeps a genuine gain (0.241→0.277 median, 7/9 cases), niftyreg is neutral (0.296→0.292; its apparent +0.024 above is mostly the clip-scoring effect). All rows of the main table are scored on the unclipped warped CT, so pipelines remain comparable.

Reproducing

# main env — SimpleITK + Elastix + ANTsPy + NiftyReg (niftyreg via Docker v2.1.1)
source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
python scripts/bench_registration.py --pipelines simpleitk elastix antspyx niftyreg \
    --n-cases 10 --workers 4 --threads 24 --simpleitk-mode benchmark
# force the conda 1.5.69 CPU build: --niftyreg-backend conda

# impact (pip; GPU; ~120-220 s/case; one-time elastix shim on hosts too old
# for the stock binary — see the IMPACT section). --impact-preset selects
# another published preset (e.g. Elastix_IMPACT_Jacobian, Generic_Rigid);
# --impact-iters / --impact-samples are no-ops for the default preset's
# per-resolution matrix
python scripts/bench_registration.py --pipelines impact --n-cases 10 \
    --workers 3 --threads 24

# synthmorph (Docker, needs the pulled image; see the SynthMorph section)
python scripts/bench_registration.py --pipelines synthmorph --n-cases 10 \
    --workers 4 --threads 24

# A/B: does clipping the CT to a brain window help? (--clip-ct clips the CT
# for ALL pipelines, before registration and scoring; niftyreg/synthmorph
# already clip by default, so this only affects antspyx/elastix/simpleitk)
python scripts/bench_registration.py --n-cases 10 --workers 4 --threads 24 \
    --out-dir results/bench_reg_noclip
python scripts/bench_registration.py --n-cases 10 --workers 4 --threads 24 \
    --clip-ct 0 80 --out-dir results/bench_reg_clip0_80
python scripts/bench_registration.py --merge --out-dir results/bench_reg_noclip
python scripts/bench_registration.py --merge --out-dir results/bench_reg_clip0_80

# combine the per-pipeline JSONs accumulated across envs into one table + plot
python scripts/bench_registration.py --merge

Other flags: --sample random --seed 0 (reproducible random 10), --keys k1,k2,... (explicit cases), --n-cases N, --workers W, --threads T (also the OpenMP thread count for reg_aladin), --simpleitk-mode {benchmark,production}, --synthmorph-model {rigid,affine}, --synthmorph-gpu <int|-1>, --synthmorph-extent <int>, --impact-preset <name> (default Elastix_IMPACT_Static), --impact-gpu <int|-1>, --impact-iters N (preset max_iterations override; no-op for matrix presets), --impact-samples N (preset spatial_samples override), --out-dir <dir>, --qa-images / --qa-out <dir> (visual QA, below).

Visual QA images (checkerboard + color overlay)

--qa-images writes a checkreg-style PNG per (pipeline, case) to <out-dir>/qa/<pipeline>__<case>.png for eyeballing the alignment. Each image is a 3×2 grid: rows are the three orthogonal mid-slices through the head center (mid-axial, mid-coronal, mid-sagittal), and columns are:

• left — checkerboard blend: alternating blocks of the CT inverse-transformed into MR coordinates and the original MR; a clean registration gives a seamless mosaic, a misregistration shows hard edges at the block boundaries.
• right — additive color overlay: CT in red, MR in green. Structures that coincide blend to yellow; rotation/translation error appears as red/green fringing at the skull and brain edges.

The CT is shown in a brain window (L40/W80 = 0–80 HU) so the brain parenchyma is visible and comparable to the MR (the skull, ~800+ HU, clips to white); the MR uses a 2–98 % head window. The coronal and sagittal views are oriented superior-up to match the axial view.

The title carries the pipeline, case key and the achieved Mattes MI, so a glance tells you whether the visuals agree with the score. Generate for the available pipelines with:

source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
python scripts/bench_registration.py \
    --pipelines simpleitk elastix antspyx niftyreg --n-cases 10 --qa-images
python scripts/bench_registration.py \
    --pipelines synthmorph --n-cases 10 --qa-images

(9 images each; the incomplete-CT case is skipped.)

SynthMorph pipeline (Docker) — one-time setup

The synthmorph pipeline runs the freesurfer/synthmorph image: SynthMorph (Hoffmann et al., IEEE TMI 2022), an anatomy-aware, acquisition-agnostic deep registration (TensorFlow VxmAffineFeatureDetector with the pretrained synthmorph.rigid.1.h5 weights). No skull-stripping or intensity preprocessing is needed: each volume is normalized to an isotropic 1-mm LIA network space internally. It is the second Docker-based pipeline and the only deep-learning one.

Prerequisites (check with RP.availability(); a missing piece raises PipelineUnavailable with the exact command):

# 1. Docker (with NVIDIA container toolkit so --gpus works)
docker --version

# 2. Pull the image (~11 GB, one-time)
docker pull freesurfer/synthmorph

No extra model assets: the rigid weights ship inside the image (/freesurfer/models).

How the adapter drives the image

1. Rigid matrix model, no init — register -m rigid on GPU when one is visible (auto-spread over the host GPUs by pid; --synthmorph-gpu -1 forces CPU). SynthMorph is a global deep method, so it recovers the gross inter-session offset on its own and needs no initial transform.
2. Read-back — the model estimates first→second, so the pair is passed as (fixed=T1c, moving=CT) and the -T LTA (a LINEAR_RAS_TO_RAS 4×4 in shared world space) is the T1c→CT contract transform directly; it is parsed into a SimpleITK AffineTransform and run through the usual fix_orientation gate.
3. GPU sharing — the container is run as the host uid (clean scratch removal, TF still reaches the GPU) with TF_FORCE_GPU_ALLOW_GROWTH set so concurrent benchmark workers can share a GPU.

NiftyReg pipeline — one-time setup

The niftyreg pipeline runs reg_aladin from the NiftyReg suite (KCL-BMEIS, the actively-maintained NiftyReg): symmetric block matching + trimmed least squares (Modat et al., J. Med. Img. 1(2) 024003, 2014). Instead of a histogram MI objective it matches 4-voxel image blocks by normalised cross-correlation at each level of a 3-level Gaussian pyramid and solves a robust (trimmed) least-squares for the rigid transform — a genuinely different algorithmic family, and cross-modality tolerant because NCC is invariant to intensity scaling.

Default backend: the official v2.1.1 release in Docker. The v2.x release binaries need GLIBC_2.34, which this host (Ubuntu 20.04, glibc 2.31) lacks, so the v2.1.1 Ubuntu zip is packaged in the niftyreg:2.1.1-ubuntu image (nvidia/cuda ubuntu22.04 base for the CUDA plugin). GPU runs use docker run --gpus all + -platf 1 -gpuid N and are ~20× faster than CPU; the v2.1.1 CUDA path supports linear interpolation only (-interp 1), so GPU runs use linear and CPU/conda runs use cubic (-interp 3) — the poses they recover on these cases agree to ~4 decimal places. The conda-forge build (1.5.69, CPU) remains the fallback when the Docker image is absent (--niftyreg-backend conda).

Prerequisites (check with RP.availability(); a missing piece raises PipelineUnavailable with the exact command):

# default: build the v2.1.1 Docker image (one-time)
docker build -t niftyreg:2.1.1-ubuntu niftyreg/   # from NiftyReg-Ubuntu-v2.1.1.zip

# fallback: conda-forge build (provides reg_aladin + reg_f3d, NiftyReg 1.5.69)
conda install -c conda-forge niftyreg

How the adapter drives the tool

1. Rigid 6-DOF, full volumes — reg_aladin -ref T1c -flo CT -rigOnly (the T1c is the reference/fixed image, the CT the floating/moving one; the CT is clipped to [0,80] HU first). Symmetric variant on by default, which matters: the non-symmetric mode drifts grossly on these repositioned pairs. -ln 3 pyramid, -maxit 15 TLS iterations per level (the tool's default of 5 is too coarse), -interp 1 (GPU) / -interp 3 (CPU) internal warping, default initialisation (image centres). GPU: --gpus all, -platf 1, -gpuid <auto by pid>; CPU/conda: -platf 0 (v2.1.1) or no -platf (1.5.69).
2. Read-back — the output affine (-aff) is a 4×4 row-major matrix, documented as Affine * Reference = Floating; the adapter parses it into a SimpleITK AffineTransform and runs it through the usual fix_orientation gate (skull-ring concentricity), which resolves the direction empirically rather than trusting the docs. Under -ref T1c -flo CT the file's affine comes out the other way, so the gate inverts it on the M6 cases (8/9 auto_inverted: True) to the contract transform (T1c→CT).

IMPACT pipeline (impact-reg-konfai) — one-time setup

The impact pipeline runs the impact-reg-konfai package: IMPACT registration presets from the VBoussot/ImpactReg Hugging Face repo, driven through the KonfAI runtime — no Docker. The default preset is Elastix_IMPACT_Static: Elastix 5.2 with the IMPACT semantic similarity metric — a per-level combination of the MIND handcrafted descriptor (MIND/R1D2_3D.pt, L1, subset 32) and TotalSegmentator TS/M730.pt (deepest layer, Dice, subset 64), layer weights 0.2/0.8 → 0.7/0.3 from coarse to fine, Static mode (whole-image features, no backprop), a 4-scale B-spline (FinalGridSpacing 14 mm), 400/300/200/200 iterations per level, 2000 spatial samples, + Mattes MI (w 0.4) + bending energy — the published successor of the MR_CT_TS reference parameter map this repo used to hand-build (and replace the vboussot/elastix_impact Docker image). Other presets are selectable with --impact-preset (Elastix_IMPACT_Jacobian, Generic_Rigid, Generic_Rigid_BSpline, FireANTs_Anatomix/IMPACT/SyN, ConvexAdam_Coarse/Composite; the live database is listed by impact-reg-konfai register --help). It is GPU-based (like synthmorph), but pip-installed.

Prerequisites (check with RP.availability(); a missing piece raises PipelineUnavailable with the exact command):

# in the longitudinal-313 env (Python >= 3.11):
pip install impact-reg-konfai          # CLI + KonfAI runtime (pulls h5py etc.)
bash scripts/setup_impact_konfai.sh    # one-time, this host only: the elastix shim

The CLI then auto-downloads the one-time assets on first register: the preset app (HF VBoussot/ImpactReg), the TorchScript feature models (HF VBoussot/impact-torchscript-models: MIND ~15 KB + TS/M730 ~200 MB) and the elastix-impact 1.0.0 binary (GitHub vboussot/ImpactElastix, ~40 MB) into ~/.cache (HF cache + ~/.cache/konfai).

Why the elastix shim (this host only)

The stock binary needs glibc ≥ 2.35 and links LibTorch 2.8.0; this host (Ubuntu 20.04, glibc 2.31, env torch 2.14) satisfies neither, so scripts/setup_impact_konfai.sh builds a self-contained shim at ~/.cache/konfai/elastix-impact-shim: the glibc 2.35 loader + libs extracted from the niftyreg:2.1.1-ubuntu image (docker is needed once for that copy only), the self-contained LibTorch 2.8.0 cu128 from download.pytorch.org (~3.8 GB zip), and a wrapper bin/elastix that runs the stock binary through the shim loader with the LibTorch 2.8.0 library path (stripping the inherited LD_LIBRARY_PATH, so the env's torch 2.14 cannot leak in). The pipeline selects the shim via KONFAI_ELASTIX_DIR (_impact_elastix_root); a host new enough to run the stock binary directly needs no shim at all.

How the adapter drives the CLI

1. Full volumes, fixed=T1c (MR), moving=CT — the ImpactReg MR_CT convention, which coincides with the shared rule (the T1c is the fixed image): the preset's output is a displacement field on the fixed grid mapping fixed→moving, so the field is the contract transform T1c→CT directly (displacement fields cannot be inverted with GetInverse, which a fixed=CT direction would require). It also keeps the metric's sample-validity gate healthy: samples drawn from the tight T1c head crop (~220×220×150 mm) map mostly inside the large CT head-neck FOV (~300×300×251 mm), unlike fixed=CT where only ~14% were in-buffer at the auto-init.
2. M6 robustness overrides — the preset's map is tuned for well-aligned pairs, so the adapter forwards raw elastix lines through the package's parameter_overrides knob: AutomaticTransformInitialization=true + AutomaticTransformInitializationMethod=CenterOfGravity (the preset's GeometricalCenter start leaves the intensity centroids ~50-100 mm apart on the head+neck FOV mismatch) and RequiredRatioOfValidSamples 0.1 (build default 0.25; keeps the divergence gate while letting the FOV-mismatched runs proceed).
3. --fields-only — the adapter derives the warp itself (the package's derived "Moved" image would be an extra full-size resample of the same content).
4. GPU auto-spread across the host GPUs by pid (--impact-gpu -1 forces CPU; the engine then turns mixed precision off).
5. Read-back — the transform comes from <out>/P000/Transform.h5 (an ITK-form HDF5 DisplacementFieldTransform on the T1c grid; the SimpleITK build here has no ITK HDF5 transform reader, so it is parsed with h5py) and direction-checked by _orient_impact: the skull-ring gap of the field vs a sign-flipped (first-order reverse) proxy — the fix_orientation analogue for a field with no GetInverse.
6. Intensity normalization happens inside the metric / model (no pipeline-side normalization): the MIND descriptor normalizes each 5-voxel neighbourhood to [0, 1] locally (modality-agnostic by construction); the segmentation models (TS/MRSeg) are standardized to zero mean / unit variance per the model card. Pre-normalizing the volumes would be an exact no-op for the z-scored models and would only corrupt the HU scale used by MI scoring and the QA brain-window images.

Notes / caveats

• The benchmark compares each toolkit in its native configuration: all register on the full volumes (no head box, no mask, no crop / pad) and use each toolkit's native automatic transform initialization (no manual centroid_offset) — elastix / impact AutomaticTransformInitialization (CenterOfGravity), antspyx's center-of-mass alignment, simpleitk identity, synthmorph none. Production uses --simpleitk-mode production's multi-start tilt grid, which raises SimpleITK's acceptance at ~20× the cost; that is out of scope for the head-to-head but is the config the dataset actually ships with.
• synthmorph is deterministic (one fixed pretrained model, no random subsampling), so its per-case numbers are stable run to run — unlike ANTs / IMPACT. Its low MI (median 0.238; 0.06–0.10 on the repositioned cases) is a genuine cross-modality / tilt weakness, not variance.
• fix_orientation (elastix/ants/niftyreg/synthmorph) chooses between a transform and its inverse by skull-ring concentricity — a correctness gate that makes the result independent of each toolkit's transform-direction convention, so the fixed/moving swap is absorbed by the gate rather than re-derived per toolkit. Under the shared fixed=T1c / moving=CT rule the raw transforms come out in the contract direction for elastix (combination transform = fixed→moving), antspyx (ANTs forward transform) and synthmorph (the -T LTA, model estimates first→second) — completed runs report auto_inverted: False for those; niftyreg's -aff file comes out the other way and the gate inverts it (8/9 auto_inverted: True). impact returns a displacement field (no GetInverse in ITK), so it uses _orient_impact — the same skull-ring decision against a sign-flipped (first-order reverse) proxy field; its MR_CT field is in the contract direction (auto_inverted: False).
• SynthMorph specifics (Docker freesurfer/synthmorph, TensorFlow, GPU):
  • Cross-modality / tilt weakness. The rigid model is trained on brain MRI, so on these CT↔T1c pairs it recovers translation but is repeatedly defeated by the ~30° inter-session tilt (7/9 cases have std > 15 mm). It is a legitimate data point for how far a pretrained MRI deep model generalizes out-of-domain to CT — not a bug.
  • No centroid init needed. Being a global deep method it recovers the gross offset on its own; the -i initial-transform path is not used (which also sidesteps this image's float64/float32 bug on the -i code path).
  • GPU sharing. The container runs as the host uid with TF_FORCE_GPU_ALLOW_GROWTH so concurrent benchmark workers share a GPU.
• ~25 s/case (GPU: one pretrained forward pass), between SimpleITK (~10 s) / ANTs (~37 s) and IMPACT (~216 s, deformable preset).
• NiftyReg specifics (conda-forge niftyreg 1.5.69, reg_aladin, symmetric block matching + TLS, CPU/OpenMP):
  • Different algorithmic family. No histogram MI objective: 4-voxel blocks are matched by normalised cross-correlation and the rigid transform is solved by a trimmed least squares, iterated per pyramid level (-ln 3 -maxit 15). NCC's scale invariance is what makes it cross-modality tolerant.
  • Symmetric mode matters. The default symmetric variant (optimising forward and backward transforms concurrently) is the robust one on these repositioned pairs; -noSym drifts grossly (gap std 45 mm on a clean case every other tool passes).
  • -maxit 5 (the tool default) is too coarse — on the clean case m6_26TYQI3C it reaches MI 0.41 vs 0.61 at 15 TLS iterations/level; 15 is the benchmark setting.
  • GPU is the default now (v2.1.1, -platf 1 via docker run --gpus all): ~20–25 s/case on the full volumes vs ~4 min CPU (120–440 s, -omp thread-bound) for the conda 1.5.69 build. --niftyreg-gpu -1 forces the CPU platform (-platf 0).
  • Version note. The upstream KCL-BMEIS release binaries (v2.x) need glibc ≥ 2.34; this host (Ubuntu 20.04, glibc 2.31) runs them in the niftyreg:2.1.1-ubuntu Docker image (nvidia/cuda ubuntu22.04 base; the CUDA plugin needs libcusolver.so.11, provided by the image). The conda-forge 1.5.69 build remains as the no-Docker fallback and gives essentially identical poses (MI matches to ±0.001 on the 10 cases).
• Elastix specifics (ITK-Elastix 0.25.4, elastix 5.3.1, in-process python API — no CLI binary involved):
  • Elastix 5.x renamed components vs the classic parameter files: the rigid 3D transform is EulerTransform (6-DOF, Rigid3DTransform is gone from the component set), the metric key is Metric (not ImageMetric), the random sampler is Random (not RandomSampler). The adapter uses the 5.x names and is not compatible with elastix ≤ 4.x parameter files.
  • AutomaticTransformInitialization "true" (CenterOfGravity) provides the start (no manual centroid_offset); AutomaticScalesEstimation sets the optimizer scales. An output directory is still set (SetOutputDirectory) for the C++ filter to run; the adapter uses a scratch dir under data/m6reg/elastix/itkelastix/.
  • WriteResultImage must be the string "false" (Python False); the default (absent) makes the filter raise "Could not read result image" after a successful registration.
• With fixed=T1c/moving=CT the combination transform is T1c→CT — the contract direction — so fix_orientation keeps it as-is (auto_inverted: False). The result is recovered by point-mapping (GetCombinationTransform().TransformPoint) into an affine — independent of elastix's Euler-angle parameterization — and wrapped in a SimpleITK AffineTransform(3).
  • Benchmark budget: MaximumNumberOfIterations 2000 per resolution (ASGD 2000/2000/2000 over 3 resolutions) with the sampler/metric at their defaults (NumberOfSpatialSamples 5000, NumberOfHistogramBins 32).
• IMPACT specifics (impact-reg-konfai 1.8.6, KonfAI runtime, elastix-impact 5.2, GPU; preset Elastix_IMPACT_Static):
  • Runs on M6 with the T1c-fixed direction + relaxed sample-validity gate. Full volumes, no mask, no crop / pad, fixed=T1c/moving=CT (the shared rule; the ImpactReg MR_CT convention coincides with it): the preset's output field is on the fixed grid, so that direction is the contract transform (T1c→CT) directly, and the metric's samples (drawn from the tight T1c crop) map mostly inside the large CT FOV. The package's generated map still defaults to RequiredRatioOfValidSamples 0.25, so the adapter overrides it to 0.1 (a genuinely diverging transform, <10% in-buffer, still aborts). In the old Docker era (fixed=CT) the gate aborted the no-mask M6 pairs until the same 0.1 relaxation. The transform contract holds; _orient_impact decides the direction as usual (completed runs report auto_inverted: False — the MR_CT field is already the contract).
  • Auto init. AutomaticTransformInitialization (CenterOfGravity) is applied as an override (the preset's GeometricalCenter start is too crude for the head+neck FOV mismatch); no manual translation is used.
  • Deformable, not rigid. The preset's native transform is a RecursiveBSpline (FinalGridSpacing 14 mm, 4 scales) regularized by the bending-energy metric; the old pipeline forced a rigid EulerTransform in place of it. The benchmark contract (warp the CT concentric with the MR head) is transform-agnostic, so this is a native-config comparison point like every other row.
  • History: single-init convergence. The semantic landscape has more local minima than Mattes-MI; earlier (masked, fixed=CT) runs no longer nailed the hardest repositioned case (m6_22IRRKU6 mid 22.7 / std 27.0; the pre-mask head-box flow got it to mid 8.3 / std 13.2). The no-mask MRSegmentator runs showed the same pattern (0.635 MI on m6_22IRRKU6 at 2000/5000 but mid 26.1 / std 28.6 there; accepted on m6_2BBN5XA4, mid 15.1 / std 10.7). The single CenterOfGravity start on the ~30° tilt pairs remains IMPACT's limiting factor — a multi-start (or --tta flips through the preset app) would be the next step.
  • Stochastic. The Jacobian preset (Elastix_IMPACT_Jacobian) resamples patches through the network each iteration, so results vary run to run; the default Static preset re-samples coordinates each iteration but the feature maps are deterministic given the pose (see Stochasticity).
  • The elastix shim (this host). The stock binary needs glibc ≥ 2.35
    • LibTorch 2.8.0; the host (glibc 2.31, torch 2.14) runs it through ~/.cache/konfai/elastix-impact-shim (built by scripts/setup_impact_konfai.sh, selected via KONFAI_ELASTIX_DIR). Same glibc story as the niftyreg v2.x binaries.
  • Models / presets are not in the repo. They auto-download from HF (VBoussot/ImpactReg, VBoussot/impact-torchscript-models) into ~/.cache/huggingface on first use; the old data/impact_models/ cache is obsolete.
  • CPU fallback: --impact-gpu -1 (no --gpu flag; the engine runs the feature models on CPU with mixed precision off — much slower).