Voxel Resampling

The primary goal is to reconstruct a volume where voxel dimensions are equal (e.g., 1mm x 1mm x 1mm). Medical scans often have high in-plane resolution but thick slice spacing. Resampling interpolates the data between slices to create a cubic grid, ensuring that distance and texture calculations are not skewed by the original acquisition geometry.

The mathematical technique used to estimate new voxel values significantly impacts feature robustness:

• Nearest Neighbor: Assigns the value of the closest original voxel. Preserves original Hounsfield Units but creates a blocky appearance.
• Trilinear Interpolation: Computes a weighted average of the 8 nearest neighbors. Smooths the image but can alter extreme intensity values.
• B-Spline Interpolation: Uses higher-order basis functions for smoother results, often preferred for isotropic resampling of continuous data to minimize partial volume artifacts.

Texture features extracted from anisotropic volumes are dependent on the patient's orientation in the scanner. A tumor imaged axially will yield different Gray-Level Co-occurrence Matrix (GLCM) values than the same tumor imaged coronally. Isotropic resampling removes this directional bias, making features truly representative of the underlying tissue architecture rather than the scan protocol.

The Image Biomarker Standardisation Initiative (IBSI) mandates explicit reporting of resampling parameters. The standard requires documenting the interpolation method, final voxel size, and intensity rounding strategy. Adherence to these benchmarks is critical for multi-center reproducibility, as inconsistent resampling is a major source of non-biological variance in radiomic studies.

When slice thickness is large, a single voxel may contain a mixture of tissue types (e.g., tumor and necrosis). Resampling alone does not fix this partial volume effect, but it is a necessary step before applying advanced correction algorithms. By creating a finer grid, resampling provides a higher-resolution canvas for subsequent segmentation refinement and tissue classification.

Resampling a 512x512x100 volume to isotropic 0.5mm voxels can increase the grid size by an order of magnitude. This directly impacts memory footprint and downstream processing time. Optimization strategies include performing resampling on-the-fly during feature extraction or using GPU-accelerated interpolation libraries to maintain clinical workflow throughput.

The process of converting continuous image intensity values into a finite number of discrete bins, a critical pre-processing step for texture matrix calculation. Discretization must occur after voxel resampling to ensure that the bin widths represent consistent physical tissue volumes. Common strategies include:

• Fixed bin number: Divides the intensity range into a set number of bins
• Fixed bin width: Uses a constant intensity interval, preserving the relationship between image noise and bin size

The computational process of removing unwanted technical variability from radiomic features caused by differences in scanner models, acquisition protocols, or reconstruction kernels. Voxel resampling to a uniform isotropic grid is the first harmonization step, standardizing the spatial domain before statistical methods like ComBat harmonization are applied to correct residual intensity variations across multi-center datasets.

Quantitative descriptors of a region of interest's three-dimensional geometric properties, including volume, sphericity, and surface-to-volume ratio. These features are highly sensitive to voxel anisotropy; calculating shape features on non-isotropic data produces spatially distorted measurements. Resampling to 1x1x1 mm³ isotropic voxels ensures that morphological characteristics reflect true anatomical geometry rather than acquisition artifacts.

A mathematical decomposition technique that filters an image into different frequency sub-bands to extract multi-scale texture features not visible in the spatial domain. Applying wavelet transforms to isotropic voxel data ensures that the frequency response is directionally unbiased. On anisotropic grids, high-frequency components are sampled differently across axes, introducing orientation-dependent artifacts into the derived texture matrices.