longitudinal/REGISTRATION_BENCHMARK.md

Registration Pipeline Benchmark — CT → T1c (M6)

Benchmarks five rigid, cross-modality registration pipelines 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. All pipelines register on the full CT / T1c volumes (no head box) and 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, synthmorph needs none. All use fixed = CT, moving = T1c except impact, which keeps fixed = T1c, moving = CT: the CT FOV (~300×300×251 mm) is much larger than the T1c crop (~220×220×150 mm), so with CT as the fixed image too many CT samples map outside the T1c buffer and the IMPACT metric aborts ("too many samples map outside moving image buffer"). The comparison is on each toolkit's native optimizer / metric / global-search capability.

The five pipelines

Listed alphabetically. All register on the full volumes (no head box) 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, and synthmorph needs none (global deep method). All use fixed = CT, moving = T1c except impact, which keeps fixed = T1c, moving = CT (swapping breaks its TotalSegmentator metric — see below).

#	pipeline	fixed / moving	init	metric	transform	status in this env
1	antspyx	CT / T1c	auto COM	Mattes MI (multi-res)	ANTs Rigid	✅ ran (main env; stochastic MI subsampling)
2	elastix	CT / T1c	auto CenterOfGravity	Mattes MI (10 bins)	EulerTransform rigid (5.x), ASGD, 3 resolutions	✅ ran (main env, in-process python API — no CLI)
3	impact	T1c / CT	auto CenterOfGravity	IMPACT semantic (TotalSegmentator TS/M730.pt, L2, Jacobian/GPU)	EulerTransform rigid, ASGD, 4 scales	✅ ran (main env, Docker + GPU)
4	simpleitk	CT / T1c	identity	Mattes MI (32 bins)	Euler3D rigid, pyramid [4,2,1]	✅ ran
5	synthmorph	CT / T1c	none (global)	SynthMorph anatomy-aware contrast-invariant (deep, pretrained)	6-DOF rigid matrix	✅ ran (main env, Docker + GPU)

simpleitk has two budgets: --simpleitk-mode benchmark (single pyramid, centered start — the default, comparable to the other tools) and --simpleitk-mode production (the full multi-start tilt grid from 02_build_m6_dataset.py, ~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)

Production QA, crop-independent:

• 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. Aligned ⇔ 0 ≤ mid ≤ 20 mm and std ≤ 12 mm. mid measures translational concentricity; std measures rotational concentricity.
• tv_in_ct_frac — fraction of the dilated warped GTV inside the warped-CT head tissue (the slab must straddle the lesion). Acceptance needs ≥ 0.5.
• tv_nvox — warped GTV volume.
• accepted — tv_nvox > 0 and tv_in_ct_frac ≥ 0.5 and aligned (the production acceptance rule).
• 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). This is the registration's achieved metric read off as a standalone alignment score: 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.
• reg_wall_s — wall-clock registration time (excludes I/O + scoring).

Results (10 cases)

pipeline	toolkit	registered	accepted	gap mid (med/mean)	gap std (med/mean)	s/reg (med)	tv-in-CT	MI (med/mean, nats)
antspyx	ANTsPy 0.6.3	9	4/10 (0.40)	12.8 / 15.6	15.6 / 16.5	36.7	0.93	0.652 / 0.644
elastix	ITK-Elastix 0.25.4	9	3/10 (0.30)	13.6 / 15.7	14.8 / 17.0	2.6	0.93	0.641 / 0.640
impact	Docker Elastix 5.2 + IMPACT	9	4/10 (0.40)	11.6 / 11.2	16.2 / 17.3	833.5	0.93	0.511 / 0.493
simpleitk	SimpleITK 2.5.6	9	4/10 (0.40)	14.4 / 16.6	17.9 / 17.5	9.7	0.93	0.651 / 0.641
synthmorph	Docker SynthMorph (TensorFlow)	9	2/10 (0.20)	16.7 / 21.8	21.8 / 21.5	25.0	0.93	0.238 / 0.308

(antspyx and impact are stochastic (random metric/patch subsampling), so their accepted counts are one draw each — see Interpretation. impact's s/reg is ~14 min/case on GPU (full volumes), orders of magnitude above the intensity-metric tools. elastix, simpleitk and synthmorph are deterministic at fixed threads.)

Per-case (mid / std, OK = accepted):

case	antspyx	elastix	impact	simpleitk	synthmorph
m6_22GD5VL2_20230724	incomplete-CT	incomplete-CT	incomplete-CT	incomplete-CT	incomplete-CT
m6_22IRRKU6_20240115	24.6 / 28.6	24.0 / 28.3	22.7 / 27.0	26.3 / 29.6	10.4 / 23.7
m6_23XIMO4M_20230222	22.2 / 23.9	22.3 / 23.5	9.1 / 16.2	21.9 / 23.2	20.3 / 23.3
m6_24V62QQM_20230223	9.5 / 15.6	11.6 / 14.8	8.6 / 7.2 OK	14.4 / 23.2	11.1 / 21.8
m6_24V62QQM_20231017	11.7 / 7.3 OK	12.2 / 7.4 OK	4.6 / 6.6 OK	11.7 / 7.5 OK	11.5 / 8.4 OK
m6_24V62QQM_20240820	10.8 / 9.1 OK	11.4 / 9.0 OK	11.6 / 4.9 OK	11.5 / 9.1 OK	43.4 / 15.8
m6_26TYQI3C_20221027	12.3 / 4.4 OK	10.6 / 3.1 OK	11.9 / 4.5 OK	11.3 / 4.6 OK	11.8 / 2.9 OK
m6_2BBN5XA4_20250411	19.1 / 7.7 OK	18.2 / 14.1	−1.7 / 29.3	18.8 / 7.5 OK	52.4 / 53.0
m6_2C4TKI6H_20240506	12.8 / 17.2	13.6 / 20.0	15.4 / 23.2	14.0 / 17.9	16.7 / 28.2
m6_2CDNN7BH_20240102	17.5 / 34.6	17.6 / 32.8	18.9 / 37.1	19.3 / 35.0	18.9 / 16.8

Interpretation

• Translation is recovered well by all five (mid gaps mostly 4–16 mm, within the ≤ 20 mm band, except the grossly-drifted cases). tv-in-CT is identical (~0.93): the warped GTV lands inside the CT head for every pipeline, so the target is in the right place.
• The binding failure is rotation, and removing the head box makes it harder. On the repositioned cases std > 12 mm — the ~30° inter-session tilt produces local minima a single auto-start locks onto, and without the head-box crop the full-volume metric sees more neck/air confounders. Notably, the hardest tilt case m6_22IRRKU6 is now rejected by all five (std 23–30 mm), which the head-box config got through with IMPACT. This is exactly why production 02_build_m6_dataset.py uses a multi-start tilt grid.
• The easy cases are now accepted by nearly all pipelines. m6_24V62QQM_20231017, m6_24V62QQM_20240820 and m6_26TYQI3C are accepted by ANTs/Elastix/IMPACT/SimpleITK (SynthMorph accepts the two cleanest). The native automatic inits (center-of-gravity / center-of-mass) place the heads close enough that the optimizers converge on the well-positioned cases.
• SimpleITK, ANTs and IMPACT tie at 4/10; Elastix 3/10; SynthMorph 2/10. ANTs is no longer the weakest — on the full volumes its native center-of-mass init works well (median std 15.6, up from the head-box+manual-pre-shift 31.7). It is stochastic (random MI subsampling), so the count varies by draw.
• IMPACT (semantic metric) is 4/10 but the slowest (~14 min/case on full volumes) and lost its standout on the hardest tilt (m6_22IRRKU6 now mid 22.7 / std 27.0 vs mid 8.3 / std 13.2 with the head box). Matching anatomical semantics still gives it the best median mid (11.6) and a competitive MI (0.511), but the full-volume registration is a larger problem for the local ASGD. It also drifts grossly on m6_2BBN5XA4 (mid −1.7, the transform flips to the far side).
• Speed (median wall, 4 cases in parallel, 24 threads): Elastix ~2.6 s (warm workers), SimpleITK ~9.7 s, ANTs ~36.7 s (CPU-bound, full volumes), SynthMorph ~25 s (GPU: a single pretrained-model forward pass per case), IMPACT ~834 s (GPU-bound: every ASGD iteration runs the TotalSegmentator extractor on the sampled patches with backprop).
  • Mattes MI (warped-CT ~ MR) is an independent, intensity-based confirmation: medians 0.651 (SimpleITK) / 0.641 (Elastix) / 0.511 (IMPACT) / 0.652 (ANTs) / 0.238 (SynthMorph). The intensity-metric tools cluster at ~0.64 (a genuine alignment signal), while SynthMorph's 0.238 reflects its tilt failures. A high MI means the CT skull/tissue intensities fall on the matching MR structures after the inverse transform.
• SynthMorph (deep, pretrained) is near-weakest of the five on these cross-modality CT pairs. Its rigid model is trained on brain MRI (T1/T2, anatomy-aware but intensity-domain MRI), so it recovers gross translation well (mid 10–20 mm on most cases) but is repeatedly defeated by the ~30° inter-session tilt: 7 of 9 cases have std > 15 mm (rotation error) and two drift grossly (m6_2BBN5XA4 mid 52 / std 53, m6_24V62QQM_20240820 mid 43). It accepts the clean m6_26TYQI3C (mid 11.8 / std 2.9, MI 0.60) and m6_24V62QQM_20231017 (mid 11.5 / std 8.4) — where it is competitive with the intensity tools. Notably it needs no centroid init (it is a global deep method that recovers the gross offset on its own). Its low median MI (0.238) reflects the tilt failures. On same-modality MRI it would likely rank much higher; this CT benchmark is an out-of-distribution stress test for it. It is deterministic (one fixed pretrained model, no random subsampling), so unlike ANTs/IMPACT the per-case numbers are stable run to run.
• 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 (RSGD wall time is thread-sensitive, convergence is not); the in-process Elastix ASGD is deterministic given its fixed seed. IMPACT is the most stochastic: its Jacobian mode draws random patches every iteration, so the final pose — and even which cases get accepted — changes run to run (two independent 10-case draws both accepted 3/10 but on different cases). Treat its per-case numbers as one sample from a distribution.

Reproducing

# main env — SimpleITK + Elastix + ANTsPy (all pip-installed here)
source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
python scripts/bench_registration.py --pipelines simpleitk elastix antspyx \
    --n-cases 10 --workers 4 --threads 24 --simpleitk-mode benchmark

# Docker pipelines (need the pulled image; see the SynthMorph / IMPACT
# sections below for the one-time setup)
python scripts/bench_registration.py --pipelines synthmorph --n-cases 10 \
    --workers 4 --threads 24
python scripts/bench_registration.py --pipelines impact --n-cases 10 \
    --workers 4 --threads 24

# 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, --simpleitk-mode {benchmark,production}, --synthmorph-model {rigid,affine}, --synthmorph-gpu <int|-1>, --synthmorph-extent <int>, --impact-model <rel|abs>, --impact-gpu <int|-1>, --impact-iters N, --impact-samples N, --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, Mattes MI, and the skull-ring gap (mid/std), so a glance tells you both the achieved-metric score and whether the visuals agree. 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 --n-cases 10 --qa-images
python scripts/bench_registration.py \
    --pipelines synthmorph --n-cases 10 --qa-images
python scripts/bench_registration.py \
    --pipelines impact --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 -T LTA (a LINEAR_RAS_TO_RAS 4×4 in shared world space; with fixed=CT/moving=T1c the -T matrix is the T1c→CT contract transform) 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.

IMPACT pipeline (Docker) — one-time setup

The impact pipeline runs the vboussot/elastix_impact image: Elastix 5.2.0 with the IMPACT semantic similarity metric compiled in as a native Elastix metric, plus a TorchScript TotalSegmentator feature extractor. It is GPU-based and Docker-based (alongside synthmorph). It is also the one pipeline that keeps fixed = T1c, moving = CT (the reverse of the others): the CT FOV (~300×300×251 mm) is much larger than the T1c crop (~220×220×150 mm), so with CT as the fixed image too many CT samples map outside the T1c buffer and the IMPACT metric aborts ("too many samples map outside moving image buffer").

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 (~23.5 GB, one-time)
docker pull vboussot/elastix_impact

# 3. Fetch the TorchScript model (~123 MB, one-time) into the repo's model
#    cache (data/impact_models/). M730.pt is the recommended TotalSegmentator.
mkdir -p /mnt/t24/Public/xfr/longitudinal/impact_models/TS
curl -L -o /mnt/t24/Public/xfr/longitudinal/impact_models/TS/M730.pt \
    https://huggingface.co/VBoussot/impact-torchscript-models/resolve/main/TS/M730.pt

Other models (M258/M291/M850, MIND, SAM2.1, …) are selectable with --impact-model <HF-relative-path-or-abs>; see the impact-torchscript-models repo for the full list and FOV/layer tables.

How the adapter drives the image

The image's CMD.sh is a generic launcher, so the adapter calls the in-image binary directly (/lib/elastix-install/bin/elastix):

1. Full volumes — fixed=T1c and moving=CT are used as-is (no head box). T1c must be the fixed image: the CT FOV is much larger, so with CT fixed too many CT samples map outside the T1c buffer and the metric aborts.
2. Auto init (no -t0) — the parameter map sets AutomaticTransformInitialization "true" with method CenterOfGravity, so elastix aligns the intensity centroids; AutomaticScalesEstimation sets the optimizer scales.
3. Parameter map — rigid EulerTransform (6-DOF), Impact metric in Jacobian mode (differentiable, per the recommended config), 4 semantic scales ImpactVoxelSize 6/3/1.5/1 mm (scaled to the T1c base spacing) with no image resampling (all-1 pyramid schedules, as the IMPACT docs require), ASGD, Random sampler. GPU auto-spread across the host GPUs by pid (--impact-gpu -1 forces CPU).
4. Read-back — the result TransformParameters.0.itk.txt is an Euler3DTransform in the world physical frame (the AutomaticTransformInitialization is folded in by elastix); the adapter runs it through the usual fix_orientation gate.

Notes / caveats

• The benchmark compares each toolkit in its native configuration: all register on the full volumes (no head box) 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 acceptance (2/10) is a genuine cross-modality / tilt weakness, not variance.
• fix_orientation (elastix/ants/synthmorph/impact) 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. With the fixed=CT/moving=T1c swap, elastix's combination transform is now the inverse of the contract, so it reports auto_inverted: True (the gate corrects it); antspyx/synthmorph/impact return the contract direction directly (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 (~834 s).
• 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=CT/moving=T1c the combination transform is CT→T1c (the inverse of the contract), so fix_orientation reports auto_inverted: True and inverts it. 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).
  • Registration wall time is ~2.4 s/case median at full budget (ASGD 500/500/250 over 3 resolutions).
• IMPACT specifics (Docker vboussot/elastix_impact, Elastix 5.2.0, GPU, Jacobian mode):
  • Slow, by design. Every ASGD iteration runs the TotalSegmentator extractor on the sampled patches with backpropagation, so the recommended 500-iter / 2000-sample budget is ~14 min/case on full volumes (vs 2–37 s for the other tools). --impact-iters / --impact-samples trade accuracy for speed.
  • Keeps fixed=T1c/moving=CT. IMPACT is the one pipeline that does NOT follow the shared fixed=CT/moving=T1c convention: the CT FOV (~300×300×251 mm) is much larger than the T1c crop (~220×220×150 mm), so with CT as the fixed image too many CT samples map outside the T1c buffer and the metric aborts ("too many samples map outside moving image buffer"). The returned transform still satisfies the contract (T1c→CT).
  • Auto init. AutomaticTransformInitialization (CenterOfGravity) aligns the intensity centroids; no manual -t0 translation is used.
  • Rugged single-init convergence. The semantic landscape has more local minima than Mattes-MI: on full volumes it no longer nails the hardest repositioned case (m6_22IRRKU6, now mid 22.7 / std 27.0 — the head box used to get it to mid 8.3 / std 13.2). A multi-start or higher-iteration budget would likely raise its acceptance; that is out of scope for the single-init head-to-head.
  • Stochastic. Jacobian mode resamples patches each iteration, so results vary run to run (see Stochasticity).
  • Elastix 5.x differences vs the in-process elastix pipeline: the image bundles 5.2.0, so the in-file multi-Transform chain and UseScalingFactors are gone. The result is read back as an Euler3DTransform (.itk.txt). The image's own CMD.sh is a generic launcher, so the adapter invokes /lib/elastix-install/bin/elastix directly.
  • Models are not in the image. The TorchScript model(s) must be present under the mounted /Data/Models (the repo caches them in data/impact_models/, bind-mounted read-only).
  • CPU fallback: --impact-gpu -1 runs without --gpus and with ImpactGPU -1 / mixed precision off (much slower).