[FEAT] MEDIC distortion correction via warpkit by vanandrew · Pull Request #541 · nipreps/sdcflows

vanandrew · 2026-04-26T04:31:17Z

Summary

Adds multi-echo dynamic distortion correction (MEDIC) to sdcflows, backed by warpkit. MEDIC estimates a per-volume B0 fieldmap directly from a multi-echo, mag+phase BOLD series, capturing breathing- and motion-driven field changes that a single static fieldmap can't.

Revives #435 / #438 with the simpler pure-Python warpkit (post vanandrew/warpkit#16, no Julia/C++ setup required). Closes #36.

What's new

Workflows

init_medic_wf (sdcflows/workflows/fit/medic.py) — multi-echo phase + magnitude → 4D Hz fieldmap (one volume per timepoint, on the EPI grid) + brain-extracted reference + brain mask. Two-stage warpkit call (UnwrapPhase → ComputeFieldmap) so the per-frame masks stay accessible. Single fmap output is 4D for MEDIC, leaving the 3D-vs-4D dispatch to the apply consumer.
init_dynamic_unwarp_wf (sdcflows/workflows/apply/dynamic.py) — per-volume apply path built on sdcflows.transform.apply_dynamic_unwarp, a per-frame extension of the same scipy/nitransforms-backed resampling that powers the static init_unwarp_wf. No warpkit needed at runtime. Includes Jacobian determinant intensity correction (jacobian=True default) sharing transform.fieldmap_jacobian with the static path.

Interfaces

sdcflows/interfaces/warpkit.py — thin LibraryBaseInterface wrappers around the two MEDIC stages SDCFlows actually drives: UnwrapPhase (ROMEO) and ComputeFieldmap. Lazy import — sdcflows imports warpkit only when these interfaces actually run.

Detection / dispatch

EstimatorType.MEDIC added. FieldmapEstimation.__attrs_post_init__ detects MEDIC inputs (part-{phase,mag} on bold/epi/sbref sources), enforces matched echo cardinality (≥2 phase, equal mag count), and rejects partial or mixed part sets. get_workflow instantiates init_medic_wf with EchoTime-sorted lists (BIDS doesn't guarantee echo entity == numeric order).
Wrangler seeds MEDIC detection from both part='phase' and part='mag' queries (some datasets carry IntendedFor only on one side); dedup walks the full sibling set, no reliance on pybids ordering.
force_medic opt-in on find_estimators — auto-discover MEDIC estimators from complex multi-echo BOLD even when neither IntendedFor nor B0FieldIdentifier is set. Pairing is unambiguous because the part-mag/part-phase echoes of the same run are MEDIC sources by construction. Intended for public datasets that ship the required echoes without the metadata the default discovery path needs.

Plumbing

init_fmap_preproc_wf skips fmap_coeff for MEDIC (the fieldmap is on the EPI grid by construction, no B-spline rep).
init_fmap_derivatives_wf uses MergeSeries(allow_4D=True) so MEDIC's 4D fmap passes through the same ds_fieldmap sink as the 3D static fmaps.

Packaging

New warpkit extra in pyproject.toml, explicitly excluded from [all] because warpkit ships under a non-commercial WUSTL license. Default pip install sdcflows stays Apache-clean.
tox.ini: the veryslow env pulls the warpkit extra so MEDIC end-to-end tests only run there.
CI: datalad-fetches sub-04/ses-2 of ds007637 and sub-a01 of ds006926 as MEDIC fixtures (cache key bumped to v3).

Validation

Three-layer multi-echo guard: wrangler seed query (echo=Query.REQUIRED), FieldmapEstimation cardinality check (len(phase_files) < 2), and _unpack_metadata runtime guard inside init_medic_wf.
Construction tests for both workflows run on every CI lane (no warpkit needed).
test_apply_dynamic_unwarp_matches_static pins apply_dynamic_unwarp to the same Hz→VSM + scipy.ndimage convention as the static _sdc_unwarp path — catches drift in sign / pe_info handling.
test_wrangler_filter / test_wrangler_URIs parametrized with a 3-session × 3-echo × {mag,phase} BIDS skeleton.
End-to-end runs on ds006926 (3-echo, 64×64×40, 205 frames) and ds007637 (5-echo, 110×110×72, 237 frames) confirm Jacobian factor sits at ~1.0 in the brain mask (std 0.087 / 0.118) with ~1–2% of voxels in compression/expansion regions and total signal change <2% — physically sensible.

Known compromises

Default wrangler MEDIC discovery still keys off IntendedFor / B0FieldIdentifier — same constraint as the existing single-PE EPI branch. Datasets missing both can opt into discovery via force_medic=True on find_estimators (see Detection / dispatch above).
The single fmap output is 4D for MEDIC. Downstream tools that expect 3D field maps need to either dispatch on dimensionality or block MEDIC-based estimators until they do.

Test plan

pytest sdcflows/utils/tests/test_wrangler.py — wrangler MEDIC detection paths (including force_medic)
pytest sdcflows/workflows/fit/tests/test_medic.py — init_medic_wf construction + _unpack_metadata guards
pytest sdcflows/workflows/apply/tests/test_dynamic.py — init_dynamic_unwarp_wf construction + jacobian flag + per-frame resampling vs. static path
pytest -m veryslow with pip install sdcflows[warpkit] and ds006926 / ds007637 fixtures present — full MEDIC fit + dynamic apply

codecov · 2026-04-26T15:44:34Z

Codecov Report

❌ Patch coverage is 85.65217% with 33 lines in your changes missing coverage. Please review.
✅ Project coverage is 84.21%. Comparing base (fcfe039) to head (aab352e).

Files with missing lines	Patch %	Lines
sdcflows/fieldmaps.py	56.25%	14 Missing ⚠️
sdcflows/workflows/apply/dynamic.py	74.07%	7 Missing ⚠️
sdcflows/transform.py	86.95%	3 Missing and 3 partials ⚠️
sdcflows/utils/wrangler.py	85.71%	3 Missing and 1 partial ⚠️
sdcflows/workflows/base.py	77.77%	1 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #541      +/-   ##
==========================================
+ Coverage   84.17%   84.21%   +0.04%     
==========================================
  Files          30       33       +3     
  Lines        2938     3104     +166     
  Branches      391      326      -65     
==========================================
+ Hits         2473     2614     +141     
- Misses        388      414      +26     
+ Partials       77       76       -1

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Three additions targeting the codecov/patch failure on nipreps#541: * sdcflows/interfaces/tests/test_warpkit.py (new) — instantiates every warpkit-backed interface so its spec class body is hit at import (was 0% on codecov despite running locally; likely an xdist worker-merge artefact). Also covers ``_as_str_list``, the ``_pkg`` invariant, and the ``border_filt=(1, 5)`` traits default regression. * sdcflows/workflows/tests/test_outputs.py (new) — direct construction test for ``init_fmap_derivatives_wf``, exercising both the default and ``write_dynamic=True`` paths. The MEDIC dynamic sinks branch was only reached transitively from the ``test_fmap_wf`` slow test, hence 0% patch coverage on outputs.py. * sdcflows/workflows/fit/tests/test_medic.py — fill the remaining helper gaps: empty-metadata rejection, ``sloppy=True`` zooms_min, ``_first``, and ``_temporal_mean`` for both 3D and 4D inputs. The ``_run_interface`` method bodies in interfaces/warpkit.py remain uncovered in the fast/slow envs since they require warpkit to import; the existing ``veryslow`` MEDIC fixtures exercise them when the optional dependency is installed.

vanandrew · 2026-05-14T13:20:12Z

I have no idea why test (3.13, pre, fast) is failing on the cache step.

It looks like an out-of-space error on the runner (but the other jobs with the same steps succeeded, so very confused):

[test (3.13, pre, fast)](https://github.com/nipreps/sdcflows/actions/runs/25840682073/job/75990934202)
System.IO.IOException: No space left on device : '/home/runner/actions-runner/cached/2.334.0/_diag/Worker_20260514-125354-utc.log'
   at System.IO.RandomAccess.WriteAtOffset(SafeFileHandle handle, ReadOnlySpan`1 buffer, Int64 fileOffset)
   at System.IO.StreamWriter.Flush(Boolean flushStream, Boolean flushEncoder)
   at System.Diagnostics.TextWriterTraceListener.Flush()
   at GitHub.Runner.Common.HostTraceListener.WriteHeader(String source, TraceEventType eventType, Int32 id)
   at System.Diagnostics.TraceSource.TraceEvent(TraceEventType eventType, Int32 id, String message)
   at GitHub.Runner.Worker.Worker.RunAsync(String pipeIn, String pipeOut)
   at GitHub.Runner.Worker.Program.MainAsync(IHostContext context, String[] args)
System.IO.IOException: No space left on device : '/home/runner/actions-runner/cached/2.334.0/_diag/Worker_20260514-125354-utc.log'
   at System.IO.RandomAccess.WriteAtOffset(SafeFileHandle handle, ReadOnlySpan`1 buffer, Int64 fileOffset)
   at System.IO.StreamWriter.Flush(Boolean flushStream, Boolean flushEncoder)
   at System.Diagnostics.TextWriterTraceListener.Flush()
   at GitHub.Runner.Common.HostTraceListener.WriteHeader(String source, TraceEventType eventType, Int32 id)
   at System.Diagnostics.TraceSource.TraceEvent(TraceEventType eventType, Int32 id, String message)
   at GitHub.Runner.Common.Tracing.Error(Exception exception)
   at GitHub.Runner.Worker.Program.MainAsync(IHostContext context, String[] args)
Unhandled exception. System.IO.IOException: No space left on device : '/home/runner/actions-runner/cached/2.334.0/_diag/Worker_20260514-125354-utc.log'
   at System.IO.RandomAccess.WriteAtOffset(SafeFileHandle handle, ReadOnlySpan`1 buffer, Int64 fileOffset)
   at System.IO.StreamWriter.Flush(Boolean flushStream, Boolean flushEncoder)
   at System.Diagnostics.TextWriterTraceListener.Flush()
   at System.Diagnostics.TraceSource.Flush()
   at GitHub.Runner.Common.Tracing.Dispose(Boolean disposing)
   at GitHub.Runner.Common.Tracing.Dispose()
   at GitHub.Runner.Common.TraceManager.Dispose(Boolean disposing)
   at GitHub.Runner.Common.TraceManager.Dispose()
   at GitHub.Runner.Common.HostContext.Dispose(Boolean disposing)
   at GitHub.Runner.Common.HostContext.Dispose()
   at GitHub.Runner.Worker.Program.Main(String[] args)

tsalo · 2026-05-15T16:26:01Z

@vanandrew I think someone must have rerun that failing job and now it's passing.

tsalo

Thanks for this! I made a quick pass through the code.

vanandrew · 2026-05-16T02:59:06Z

Pushed four commits addressing your feedback (noise_frames, ctor inputs, nitransforms switch + interface cleanup, unified fmap output).

vanandrew · 2026-05-20T01:16:27Z

I can't tell if these test failures are something wrong with my dataset additions or if it's just another one-off error:

update(error): . (dataset) [Fetch failed: CommandError(CommandError: 'git -c diff.ignoreSubmodules=none -c core.quotepath=false fetch --verbose --progress --no-recurse-submodules --
prune origin' failed with exitcode 128 under /home/runner/sdcflows-tests/brain-extraction-tests [err: 'fatal: unable to access 'https://gin.g-node.org/nipreps-data/brain-extraction-
tests/': The requested URL returned error: 403'])] [CommandError: 'git -c diff.ignoreSubmodules=none -c core.quotepath=false fetch --verbose --progress --no-recurse-submodules --
prune origin' failed with exitcode 128 under /home/runner/sdcflows-tests/brain-extraction-tests [err: 'fatal: unable to access 'https://gin.g-node.org/nipreps-data/brain-extraction-
tests/': The requested URL returned error: 403']]
Error: Process completed with exit code 1.

vanandrew · 2026-05-25T17:58:19Z

Looks like the CI keeps failing because it's running out of space (I added two datasets for tests so that might have pushed it over the edge).

I pushed 6f5cfff2 adding a jlumbroso/free-disk-space@main step at the top of the test job:

- name: Free disk space
  uses: jlumbroso/free-disk-space@main
  with:
    tool-cache: false
    android: true
    dotnet: true
    haskell: true
    large-packages: false
    swap-storage: false

This gets rid of some unneeded development tools on the github runner to free room for data.

vanandrew · 2026-06-02T01:40:18Z

@tsalo Alright. I made changes based on our convo today:

find_estimators discovers a fieldmap estimator for every fieldmap declared in BIDS intent metadata: grouping inputs by B0FieldIdentifier (Step 1), or falling back to IntendedFor heuristics (Step 2) only when no B0FieldIdentifier exists. (no_medic suppresses MEDIC via either route). It adds a sort that always returns dynamic estimators (i.e. MEDIC) first.
Simplified fmap_ref and fmap_mask to be the first echo magntude and MEDIC fmap masks respectively. These likely won't be used by downstream consumers.
Removed force_medic and replaced with no_medic

Three additions targeting the codecov/patch failure on nipreps#541: * sdcflows/interfaces/tests/test_warpkit.py (new) — instantiates every warpkit-backed interface so its spec class body is hit at import (was 0% on codecov despite running locally; likely an xdist worker-merge artefact). Also covers ``_as_str_list``, the ``_pkg`` invariant, and the ``border_filt=(1, 5)`` traits default regression. * sdcflows/workflows/tests/test_outputs.py (new) — direct construction test for ``init_fmap_derivatives_wf``, exercising both the default and ``write_dynamic=True`` paths. The MEDIC dynamic sinks branch was only reached transitively from the ``test_fmap_wf`` slow test, hence 0% patch coverage on outputs.py. * sdcflows/workflows/fit/tests/test_medic.py — fill the remaining helper gaps: empty-metadata rejection, ``sloppy=True`` zooms_min, ``_first``, and ``_temporal_mean`` for both 3D and 4D inputs. The ``_run_interface`` method bodies in interfaces/warpkit.py remain uncovered in the fast/slow envs since they require warpkit to import; the existing ``veryslow`` MEDIC fixtures exercise them when the optional dependency is installed.

vanandrew · 2026-06-03T18:56:20Z

@tsalo I did a rebase to the latest main, hopefully to quash the 3.13 test error that happened in the last CI run: https://github.com/nipreps/sdcflows/actions/runs/26792754492/job/79155816998#step:22:320

effigies

There's a significant conceptual problem here. I'm not sure if it had been done correctly and then the distinction was lost after the latest refactor. If so, apologies for not getting to this sooner and saving you the time.

There is a comment that says the caller is responsible for ensuring that the fieldmap has already been projected into the moving (source) space, but we project them into the fixed (target) space.

To explain: We are resampling into the target space, so we need a VSM to correspond with every coordinate in the target space, and to then apply this shift in the source space. For a fixed fieldmap, this value is the same, so we can save computation by interpolating the fieldmap exactly once into the target space. For a dynamic fieldmap, there are no savings to be had here (and we don't know how to map the fieldmap into the target space without using the fieldmap to unwarp itself) and we need to interpolate the values in the target space. So if we want to have a single resampling function, it needs to have the logic:

if not dynamic:
    vsm = fmap_hz * pe_info[1]
else:
    vsm = ndi.map_coordinates(fmap_hz, coordinates, ...) * pe_info[1]

coordinates[pe_info[0], ...] += vsm

resampled = ndi.map_coordinates(data, coordinates)

As it's a simple function, it might be cleaner to do two separate functions, one with a fieldmap in target space and one with it in source space. I don't know if that makes the wrapping code easier or harder.

I started making some other comments, but stopped when I got to this point. Happy to discuss further on this thread or on a call.

vanandrew · 2026-07-20T20:12:46Z

@effigies Thanks for the review! I'll take a closer look at the comments later this week.

As for your conceptual concern, I think that comment might be a typo. I'm pretty sure I'm doing everything in the target space, not the moving space. But I'll take a look again and let you know for sure.

vanandrew · 2026-07-20T20:27:11Z

There is a comment that says the caller is responsible for ensuring that the fieldmap has already been projected into the moving (source) space, but we project them into the fixed (target) space.

@effigies Can you point out the line numbers of this comment?

effigies · 2026-07-20T21:04:49Z

sdcflows/sdcflows/transform.py

Lines 594 to 598 in a68a1e4

    
               Shared core of :meth:`B0FieldTransform.apply`. The caller is responsible 
        
               for producing ``fmap_hz`` already on the ``moving`` grid — B-spline 
        
               reconstruction plus coregistration for static estimators, or a pre-gridded 
        
               per-frame field for dynamic estimators — and for ensuring ``moving`` has 
        
               positive cosines.

vanandrew · 2026-07-23T03:50:22Z

@effigies I took a closer look at this. It's more of a badly phrased docstring. I reworded it in 7b02d9a to make it clearer, but there are two separate concepts:

Grid (lattice): fmap_hz is co-gridded with moving — same voxel lattice — so the per-voxel scaling and coordinate shift broadcast element-wise. The sampling coordinates come straight from moving's own grid (transform.py:642, nt.linear.Affine(reference=moving)), which is why the field has to share that lattice. This is a precondition for the pull-resample, not a statement about distortion space.
Semantics: the field is valued in the undistorted (target) sense. In _sdc_unwarp the shift is applied exactly once, on the target lattice (transform.py:95-97):
```
# voxcoords is the deformation field, i.e., the target position of each voxel
vsm = fmap_hz * pe_info[1]
coordinates[pe_info[0], ...] += vsm
```
No source-space treatment, no inversion.

So we agree the apply path works in target space — the old docstring just described it as "moving/source," which is exactly the contradiction you flagged. MEDIC hands in a field that's already target-valued and on the EPI grid, so it skips fit() (transform.py:508); the static estimators reach the same state via B-spline reconstruction + coregistration inside fit() (transform.py:521).

Three additions targeting the codecov/patch failure on nipreps#541: * sdcflows/interfaces/tests/test_warpkit.py (new) — instantiates every warpkit-backed interface so its spec class body is hit at import (was 0% on codecov despite running locally; likely an xdist worker-merge artefact). Also covers ``_as_str_list``, the ``_pkg`` invariant, and the ``border_filt=(1, 5)`` traits default regression. * sdcflows/workflows/tests/test_outputs.py (new) — direct construction test for ``init_fmap_derivatives_wf``, exercising both the default and ``write_dynamic=True`` paths. The MEDIC dynamic sinks branch was only reached transitively from the ``test_fmap_wf`` slow test, hence 0% patch coverage on outputs.py. * sdcflows/workflows/fit/tests/test_medic.py — fill the remaining helper gaps: empty-metadata rejection, ``sloppy=True`` zooms_min, ``_first``, and ``_temporal_mean`` for both 3D and 4D inputs. The ``_run_interface`` method bodies in interfaces/warpkit.py remain uncovered in the fast/slow envs since they require warpkit to import; the existing ``veryslow`` MEDIC fixtures exercise them when the optional dependency is installed.

vanandrew · 2026-08-17T22:32:49Z

I did a reorg of the git history for this since it was getting kind of intractable (~60 commits -> 8 commits). Also, rebased everything to the latest main branch.

vanandrew · 2026-09-24T20:38:54Z

Thought I give this a ping. Is there anything that you need from my end to get this merged in? @effigies @tsalo @mgxd

I think I've addressed all the current comments, but let me know if we need to discuss.

effigies · 2026-09-25T03:08:17Z

Thanks. This is still on my to-do list. I think we're realistically going to need to focus on preserving old behavior and allow users to detect bugs in the new stuff, because I don't know that I have the bandwidth to review this as carefully as I would like.

vanandrew · 2026-09-26T13:01:01Z

@effigies Alright. Then I'll work on re-implementing this as an optional separate pathway, while preserving the original code.

effigies · 2026-09-26T13:35:06Z

@vanandrew Sorry, I didn't mean to imply that. I think keeping it as one pathway makes sense, and my review will just focus less on the correctness of the 4D branches and ensure the 3D branches are still doing what's expected.

Generalize _resample_with_fieldmap so a per-volume (4D) fieldmap runs through the same code path as a static 3D one, selecting the matching frame for each volume. fieldmap_jacobian now takes the voxel shift map directly instead of recomputing it, so the static and dynamic callers share a single definition. Document the fmap_hz preconditions.

Add UnwrapPhase and ComputeFieldmap, wrapping warpkit's multi-echo phase unwrapping and fieldmap computation. Both follow the nipype num_threads convention. warpkit becomes a core dependency (>= 1.5.0). It carries a Washington University non-commercial license, so commercial use requires a separate WUSTL OTM agreement.

Add EstimatorType.MEDIC together with the is_dynamic property that tells per-volume fieldmaps apart from static ones. MEDIC estimation validates its file set up front, rejecting incomplete magnitude/phase part sets and single-echo input rather than failing deep inside the workflow.

Discover MEDIC estimators from B0FieldIdentifier and IntendedFor metadata only, never from directory structure alone. Cover the magnitude side of IntendedFor and de-duplicate estimators that both fields point at. Add --no-medic to opt out of MEDIC discovery entirely.

Fit workflow chaining UnwrapPhase into ComputeFieldmap to produce a per-volume fieldmap from complex multi-echo BOLD. fmap_ref comes from the raw first-echo magnitude and fmap_mask from warpkit's masks by way of init_dynamic_magnitude_wf, so dynamic estimators expose the same outputs as static ones.

Apply workflow that unwarps a BOLD series volume by volume against its per-volume fieldmap, with Jacobian intensity correction and the phase-encoding direction sign forwarded through to the fieldmap conversion. Resampling goes through nitransforms.

…rivatives Gate the static-versus-dynamic branch in init_fmap_preproc_wf on FieldmapEstimation.is_dynamic, and align the merges so non-MEDIC estimators keep working alongside MEDIC ones. The derivatives writer dismisses the task entity on dynamic ref/mask sinks and routes the dynamic magnitude reference through the fieldmap suffix.

Stage ds006926 and ds007637 (multi-echo magnitude + phase BOLD) in the cache-test-data job and run the MEDIC end-to-end tests on the veryslow lane. Free runner disk before the data cache is restored, since the added datasets push the root disk past its limit. Skip both datasets in conftest's layout auto-index -- indexing them stalls collection. Add Andrew Van to .zenodo.json.

Copilot

Copilot review overview

🟡 Changes recommended

MEDIC has confirmed workflow-construction and reporting failures, and its dependency packaging contradicts the stated opt-in design.

Review effort: Balanced
Findings: 7 · 6

Open (13)

Forward no_medic in normal CLI execution · New
Validate matching phase and magnitude echoes · New
Skip coefficient outputs for dynamic estimators · New
Validate pre-gridded map shape and affine · New
Bound queued coordinate-grid task memory · New
Seed outer MEDIC inputs from the estimator · New
Provide matching 3D MEDIC report inputs · New
Move warpkit to an opt-in extra · New
Implement the missing force_medic discovery path · New
Apply session filters to identifier file lookups · New
Preserve IntendedFor MEDIC runs with other identifiers · New
Pass omp_nthreads to warpkit interfaces · New
Compute each echo's readout time with get_trt · New

What changed in this PR

This PR adds MEDIC fieldmap estimation and per-volume distortion correction to sdcflows for multi-echo magnitude and phase data.

Changes:

Adds MEDIC detection, warpkit interfaces, and a fieldmap estimation workflow.
Adds 4D fieldmap resampling and connects it to derivative and report workflows.
Adds tests and CI fixtures; the current packaging makes warpkit a required dependency rather than the described opt-in extra.

File	Description
`sdcflows/workflows/tests/test_outputs.py`	Checks 4D fieldmap merge configuration.
`sdcflows/workflows/outputs.py`	Allows 4D fieldmaps in derivative output.
`sdcflows/workflows/fit/tests/test_medic.py`	Tests MEDIC construction and metadata handling.
`sdcflows/workflows/fit/medic.py`	Builds the MEDIC estimation workflow.
`sdcflows/workflows/base.py`	Connects dynamic estimators to preprocessing outputs.
`sdcflows/workflows/apply/tests/test_dynamic.py`	Tests dynamic apply construction and resampling.
`sdcflows/workflows/apply/dynamic.py`	Adds the per-volume correction workflow.
`sdcflows/utils/wrangler.py`	Discovers and orders MEDIC estimators.
`sdcflows/utils/tests/test_wrangler.py`	Tests MEDIC discovery and opt-out behavior.
`sdcflows/transform.py`	Supports per-frame fieldmaps in resampling.
`sdcflows/tests/test_fieldmaps.py`	Tests MEDIC estimator validation.
`sdcflows/interfaces/warpkit.py`	Wraps warpkit’s estimation stages.
`sdcflows/interfaces/tests/test_warpkit.py`	Checks warpkit interface specifications.
`sdcflows/fieldmaps.py`	Adds the MEDIC estimator type and workflow dispatch.
`sdcflows/conftest.py`	Provides MEDIC test fixtures.
`sdcflows/config.py`	Adds MEDIC opt-out configuration.
`sdcflows/cli/parser.py`	Adds the `--no-medic` option.
`sdcflows/cli/main.py`	Passes the option through dry-run discovery.
`pyproject.toml`	Adds warpkit as a dependency.
`.zenodo.json`	Adds contributor metadata.
`.github/workflows/build-test-publish.yml`	Fetches MEDIC test data in CI.

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                layout=config.execution.layout,
                subject=subject,
                fmapless=config.workflow.fmapless,
+                no_medic=config.workflow.no_medic,


+            if len(phase_files) != len(mag_files):
+                raise ValueError(
+                    f'MEDIC requires matched magnitude/phase pairs per echo; '
+                    f'got {len(phase_files)} phase and {len(mag_files)} '
+                    'magnitude file(s).'
+                )


+        elif self.method == EstimatorType.MEDIC:
+            from .workflows.fit.medic import init_medic_wf
+


+            fmap_img, _ = ensure_positive_cosines(self.mapped)
+            fmap_hz = np.asanyarray(fmap_img.dataobj, dtype='float32')


+        jacobian=jacobian,
+        num_threads=num_threads,


    sessions: list[str] | None = None,
    fmapless: bool | set = True,
    force_fmapless: bool = False,
+    no_medic: bool = False,


+            (
+                listify(ids)
+                for ids in layout.get_B0FieldIdentifiers(session=sessions, **base_entities)
+            ),


+    # MEDIC estimator). This block is the legacy fallback: when no
+    # ``B0FieldIdentifier`` is present, a complex multi-echo BOLD that declares
+    # ``IntendedFor`` is picked up here. Skipped entirely when ``no_medic`` is
+    # set or when ``B0FieldIdentifier`` metadata already drove discovery.
+    if not no_medic and not b0_ids:


+    unwrap = pe.Node(
+        UnwrapPhase(debug=debug),
+        name='unwrap',
+        n_procs=omp_nthreads,
+    )
+
+    # ComputeFieldmap doesn't expose a ``debug`` input — only UnwrapPhase
+    # does, so the asymmetry is intentional.
+    compute_fmap = pe.Node(
+        ComputeFieldmap(),
+        name='compute_fmap',
+        n_procs=omp_nthreads,
+    )


+    echo_times = [float(m['EchoTime']) * 1000.0 for m in metadata]
+    total_readout_time = float(metadata[0]['TotalReadoutTime'])
+    phase_encoding_direction = metadata[0]['PhaseEncodingDirection']


vanandrew mentioned this pull request May 8, 2026

ENH: Add optional warpkit MEDIC support for multi-echo BOLD distortion correction nipreps/fmriprep#3645

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vanandrew force-pushed the feat/medic-warpkit branch from 57ccc47 to 21ab1c6 Compare May 10, 2026 03:21

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vanandrew changed the title ~~[WIP] [FEAT] MEDIC distortion correction via warpkit~~ [FEAT] MEDIC distortion correction via warpkit May 11, 2026

vanandrew mentioned this pull request May 11, 2026

FIX: correctness fixes for MEDIC pipeline DVSneuro/fmriprep#1

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		elif self.method == EstimatorType.MEDIC:
		from .workflows.fit.medic import init_medic_wf

		fmap_img, _ = ensure_positive_cosines(self.mapped)
		fmap_hz = np.asanyarray(fmap_img.dataobj, dtype='float32')

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Summary

What's new

Workflows

Interfaces

Detection / dispatch

Plumbing

Packaging

Validation

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