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What must stay consistent across modalities?
A model can only use multiple scans meaningfully if each input voxel represents the intended location and each label still refers to the correct anatomy. Matching array dimensions is not enough: two volumes with the same shape can have different orientation, spacing, origin, or physical coverage.
Keep a record of the modality and sequence, acquisition or time point, dimensions, voxel spacing, coordinate system, intended role, and annotation source for every image. Use a stable case identifier, and check that scans from different studies or time points have not been combined unintentionally.
Preserve geometry during conversion
DICOM series carry position and orientation information. The NIfTI FAQ describes deriving volume geometry from DICOM Pixel Spacing, Image Orientation (Patient), and Image Position (Patient); it also discusses qform as a way to store rigid alignment information. Verify those details against the current FAQ and the converter you use, then inspect the converted volume’s orientation, spacing, origin or affine, slice ordering, and physical coverage. Do not infer correct geometry from array shape alone.
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Keep labels tied to their source image
Record which modality and coordinate frame each annotation was drawn in. If a label is moved into a shared grid, apply the corresponding spatial transform and check its boundary against the image in that grid. A transform ledger should make it possible to trace both inputs and outputs back to their native spaces.
How should you align the scans?
Choose a reference image and grid appropriate to the task and the model’s expectations. If the modalities are not already in the same physical space, register the moving image to the fixed reference. MONAI Physio’s registration API describes the fixed image as the target coordinate system and supports keeping masks and labelmaps in frame with their image.
Select registration for the anatomy and acquisition
Rigid, affine, or deformable registration may be appropriate in different circumstances. Consider cross-modality contrast, anatomy, motion, and whether the segmentation target requires local deformation. No one registration type is established as the best choice for every multimodal task. Inspect the result rather than assuming that a successful registration call means the anatomy is aligned.
Use published examples as examples, not defaults
Guo, Li, Huang, Guo, and Li’s 2017 soft-tissue sarcoma study used rigid registration to transfer tumor annotations between modalities, cropped wider PET/CT coverage to the MR field of view, and linearly interpolated PET to match resolution. Those are choices for that dataset and study, not a general prescription for other anatomies or acquisitions.
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How should you choose a target grid and resample?
Define the target spacing, field of view, and grid with the anatomy, source resolution, coverage, and model constraints in mind. Resample all channels and their labels through a consistent transform chain. Minimize unnecessary repeated resampling, which can alter image values or boundaries.
- Intensity images: use a continuous interpolation method suited to the image type and task.
- Categorical labelmaps: use nearest-neighbor interpolation so resampling does not create fractional class IDs. MONAI Physio documents nearest-neighbor interpolation for masks and labelmaps.
- Outputs: retain the transforms needed to map predictions from the model grid back to the original image space.
Do not assume that matching voxel spacing alone aligns images: the origin, orientation, and field of view must also be considered.
How should multimodal intensities be normalized?
Set an explicit channel order and name each channel. Preserve modality-specific intensity meaning where the task or model depends on it; do not apply one unexplained normalization operation to every input simply because they are stacked into a tensor.
Normalization behavior is model-specific. In nnU-Net v2, channel_names determine preprocessing: CT uses dataset-level foreground-based normalization, while other names default to per-case z-score normalization. Its documentation says normalization is applied per channel and that there is no built-in joint multichannel normalization scheme. These are nnU-Net v2 conventions, not universal requirements for segmentation models.
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How do you meet a model’s input requirements?
Before preprocessing the full dataset, check the exact model or bundle instructions for supported modalities, sequence, orientation, spacing, preprocessing, channel order, label schema, and output grid. An architecture’s ability to accept multiple channels does not mean it can accept any combination of scans without preparation.
Example: MONAI Physio NV-Segment-CTMR
The MONAI Physio documentation lists CT_BODY, MRI_BODY, and MRI_BRAIN input modes. Its MRI_BRAIN mode expects a skull-stripped T1 volume affinely aligned to the LUMIR template; the model does not perform that preparation itself. The documentation states that NV-Segment-CTMR weights use NVIDIA’s OneWay Non-Commercial License and identifies NV-Segment-CT as a commercially licensed CT-only alternative. Confirm the current release, task fit, and license terms before use.
What quality checks should you run?
Review aligned inputs and labels in axial, coronal, and sagittal views, both side by side and as overlays. These are practical workflow checks inferred from geometry and alignment requirements; the sources described here do not establish a universal QC standard.
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- Check for left-right flips, incorrect slice ordering, missing slices, and truncated coverage.
- Confirm corresponding anatomy overlaps across modalities and label boundaries remain on the intended structures.
- Look for registration failures, corrupted labels, unexpected intensity ranges, and an incorrect channel order.
- Trace a few cases through native space, the common grid, the model input tensor, and reconstructed output space.
Record conversion, orientation changes, registration, resampling, cropping, normalization, channel order, and label handling. This transform and preprocessing ledger supports reproducibility and helps diagnose whether an error arose in the source data, alignment, model input, or output reconstruction.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare multimodal preparation choices?
Choose and document the trade-offs that affect this dataset and model rather than relying on a single default.
Quick Recap
- Registration: compare rigid, affine, and deformable approaches for the anatomy, motion, and cross-modality contrast involved.
- Reference grid: weigh the fixed modality, target spacing, coverage, and the model’s expected input space.
- Interpolation: distinguish continuous image resampling from nearest-neighbor label resampling, and avoid unnecessary repeated transformations.
- Intensity handling: account for modality-specific scales and sequence variability, then follow the model’s documented normalization convention.
- Fusion strategy: distinguish early, feature-level, intermediate/classifier-level, and late/decision-level fusion. In the 2017 sarcoma study, feature-level fusion performed best overall in that experiment but was less robust to large errors in any modality; that result should not be generalized as a ranking for other tasks.
- Deployment constraints: check supported inputs, runtime requirements, output label taxonomy, license, and preprocessing the model expects outside its bundle.
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