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name: data-loading description: "Load and inspect fastMRI HDF5 data, datasets, masks, transforms, and data sampling controls." disable-model-invocation: true metadata: disco-role: operating license: MIT

fastMRI Data Loading

Use this sub-skill when the task is about reading fastMRI HDF5 files, validating split/key layout, constructing SliceDataset, CombinedSliceDataset, or AnnotatedSliceDataset, choosing mask functions, applying UnetDataTransform or VarNetDataTransform, or debugging dataset sampling/cache behavior.

Route Here

• The user has knee or brain fastMRI .h5 data and needs to identify whether it is singlecoil or multicoil, train/val/test/challenge, or missing target reconstructions.
• The user needs Python objects such as SliceDataset, CombinedSliceDataset, AnnotatedSliceDataset, RandomMaskFunc, EquiSpacedMaskFunc, EquispacedMaskFractionFunc, UnetDataTransform, or VarNetDataTransform.
• The user sees loader errors involving challenge, sample_rate, volume_sample_rate, dataset_cache.pkl, HDF5 keys, or ismrmrd_header metadata.
• The user wants safe inspection or tiny synthetic fastMRI-like HDF5 fixtures before training, inference, or submission work.

Use These Files

• references/data-formats.md explains fastMRI HDF5 split layouts, required keys, target reconstruction expectations, metadata, and practical directory checks.
• references/api-reference.md summarizes the dataset, mask, transform, sampling, cache, and annotation APIs with exact constructor signatures.
• references/troubleshooting.md maps common loader, mask, transform, cache, and optional dependency failures to fixes.
• scripts/inspect_fastmri_h5.py safely summarizes .h5 keys, k-space shapes, reconstruction keys, attrs, masks, and ISMRMRD crop metadata.
• scripts/create_tiny_fastmri_h5.py creates deterministic lightweight fastMRI-like HDF5 files for loader/schema validation.

Boundary Routing

• For FFT, complex tensor conventions, rss, crop math, or operator shape debugging, route to mri-operators.
• For FastMriDataModule, dataloaders, Lightning trainers, DDP samplers, and full training loops, route to lightning-training.
• For saved reconstructions, file metrics, leaderboard-style outputs, or submission HDF5 files, route to evaluation-submission.

Quick Decision Path

1. Inspect one or more .h5 files with scripts/inspect_fastmri_h5.py and confirm kspace, optional mask, optional reconstruction target, attrs, and ismrmrd_header crop metadata.
2. Choose challenge="singlecoil" for single-coil knee data or challenge="multicoil" for multicoil knee/brain data; invalid challenge names raise a ValueError.
3. Use SliceDataset for one split, CombinedSliceDataset for multiple roots, or AnnotatedSliceDataset when fastMRI+ annotations are needed.
4. Use either slice sampling (sample_rate) or volume sampling (volume_sample_rate), never both; use raw_sample_filter and num_cols for metadata/shape filtering.
5. Choose a mask with create_mask_for_mask_type, then pass it to UnetDataTransform or VarNetDataTransform only when synthetic undersampling is needed.