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name: reconstruction-models description: "Choose and fit Dipy diffusion reconstruction models, compute scalar maps, ODFs, peaks, tiny simulations, and reconstruction CLI workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Reconstruction Models

Use this sub-skill when a task involves Dipy diffusion reconstruction: TensorModel, DKI, CSD/MSMT-CSD, CSA/QBall/OPDT, DSI/DSID, GQI, MAPMRI, SFM, FORECAST, FWDTI, FORCE, RUMBA, SH models, peaks_from_model, scalar maps, ODFs, simulated signals, or dipy_fit_* reconstruction commands.

Route First

• Use ../io-data/ for loading/saving NIfTI images, b-values/b-vectors, GradientTable file ingestion, PAM files, tractograms, and image/gradient shape validation.
• Use ../denoising-preprocessing/ before fitting when the signal needs denoising, Gibbs removal, masking, noise estimation, bias correction, or invalid-signal repair.
• Use ../tracking-segmentation/ when reconstruction outputs become tracking inputs: peaks, PAM files, tensor directions, stopping criteria, tissue masks, segmentation, or tractometry.
• Use ../cli-workflows/ for console-entry discovery, parser behavior, help probing, batch globbing semantics, and translating API recipes into installed dipy_* commands.

Core References

• references/model-selection.md explains how to choose DTI, DKI, CSD, CSA/QBall, DSI, GQI, MAPMRI, SFM, FWDTI, FORECAST, FORCE, RUMBA, IVIM, SDT, and power-map workflows.
• references/api-reference.md lists high-value Python APIs, signatures, shapes, outputs, and validation checks.
• references/workflows.md gives safe API and CLI recipes, output naming, handoff notes, and skip markers.
• references/troubleshooting.md maps reconstruction symptoms to causes, fixes, and validation checks.
• scripts/dipy_tensor_smoke.py runs a deterministic tiny synthetic TensorModel fit and emits JSON with finite FA/eigenvalue validation.

Fast Operating Pattern

1. Confirm the reconstruction question and acquisition class before choosing a model; prefer the simplest scientifically adequate model.
2. Ensure diffusion volumes are on the last axis and len(gtab.bvals) == data.shape[-1]; masks must be exactly data.shape[:-1].
3. Guard signals before fitting: finite numeric data, positive values for log-sensitive models, appropriate b0_threshold, and a mask that excludes background.
4. Fit with a mask when possible, compute model-specific maps or peaks, then validate finite values, expected shapes, and nonzero in-mask coverage.
5. For ODF/peak workflows, record sphere, sh_order_max, response function, thresholds, SH basis settings, and whether parallel was used.
6. For CLI workflows, run help first, set an explicit output directory, and treat full-image CSD/MSMT-CSD/MAPMRI/DSI/FORCE/RUMBA runs as skip-expensive unless authorized.

Safety Defaults

• Prefer tiny synthetic arrays and snr=None simulations for smoke checks; avoid downloads, GUI rendering, neural-network surfaces, and benchmark-scale data by default.
• Do not claim dipy_fit_rumba is installed; use RUMBA as a Python API/specialized workflow surface unless an installed command is verified in the active environment.
• Use dipy.direction.peaks.peaks_from_model; do not import peaks from dipy.reconst.peaks for this Dipy version.