Volume downsampling

This tutorial downsamples continuous-valued 3-D volumes with an explicit spatial-axis contract. SplineOps operates on array grids; it does not manage medical-image origin, direction, or physical-spacing metadata in version 2.1.

One volume

import numpy as np
from splineops import resize

volume = np.random.default_rng(0).random((80, 256, 256), dtype=np.float32)
coarse = resize(
    volume,
    output_size=(40, 128, 128),
    axes=(0, 1, 2),
    method="cubic-antialiasing",
)

Channels or time points

For an array shaped T x C x Z x Y x X, resize only the last three axes:

coarse_series = resize(
    series,
    output_size=(40, 128, 128),
    axes=(-3, -2, -1),
    method="cubic-antialiasing",
)

The time and channel dimensions remain unchanged and are not filtered.

Repeated geometry

Use ResizePlan when volumes arrive separately but share a shape:

from splineops import ResizePlan

plan = ResizePlan(
    input_shape=(80, 256, 256),
    output_size=(40, 128, 128),
    method="cubic-antialiasing",
)

for volume in volumes:
    coarse = plan(volume)
    consume(coarse)

Data semantics

Projection antialiasing is intended for continuous-valued samples such as intensity fields. Categorical segmentation labels normally require nearest- neighbour resampling. Probability channels may require normalization after resizing, depending on the application.

If an input comes from NIfTI, DICOM, or another physical-space format, update and validate its metadata in the library that owns that metadata. SplineOps 2.1 returns an array, not a physical-space image object.

See Resize for the endpoint-aligned coordinate contract and Performance evidence for measured performance and its limitations.