Module sdf 

Spatially Dependent Filtering (SDF) for QSMART

SDF is the background field removal method used in QSMART. It uses variable-radius Gaussian filtering where the kernel size depends on the proximity to the brain boundary. This allows for aggressive filtering in the brain interior while preserving details near the surface.

The algorithm includes optional curvature-based weighting to further reduce artifacts at highly curved brain regions.

Reference: Yaghmaie, N., Syeda, W., et al. (2021). “QSMART: Quantitative Susceptibility Mapping Artifact Reduction Technique.” NeuroImage, 231:117701. https://doi.org/10.1016/j.neuroimage.2020.117701

Reference implementation: https://github.com/wtsyeda/QSMART

Structs

SdfParams

Parameters for SDF background field removal

Functions

convolve_1d_direction 🔒

1D convolution along specified axis with replicate padding Matches MATLAB’s imgaussfilt3 default behavior

convolve_1d_direction_sigma 🔒

1D convolution with specified sigma

gaussian_smooth_3d_masked_f64 🔒

Gaussian smoothing with anisotropic sigma and mask

gaussian_smooth_3d_with_filter_size 🔒

3D Gaussian smoothing with specified filter size

sdf

SDF background field removal

sdf_curvature

SDF with curvature-based weighting (full QSMART pipeline)

sdf_default_stage1

Default SDF parameters for stage 1

sdf_default_stage2

Default SDF parameters for stage 2

sdf_simple

Simple SDF without curvature (faster, for testing)

sdf_with_progress

SDF with progress callback