Module bias_correction 

Bias field correction (homogeneity correction)

Implements the makehomogeneous algorithm for correcting RF receive field inhomogeneities. This uses the “boxsegment” approach with box filter Gaussian approximation.

Reference: Eckstein, K., Trattnig, S., Robinson, S.D. (2019). “A Simple Homogeneity Correction for Neuroimaging at 7T.” Proc. ISMRM 27th Annual Meeting.

Reference implementation: https://github.com/korbinian90/MriResearchTools.jl

Structs

HomogeneityParams

Parameters for inhomogeneity correction (bias field removal).

Functions

box_filter_line 🔒

1D box filter on a line (in-place), matching Julia’s boxfilterline!

box_filter_line_weighted 🔒

1D weighted box filter on a line (in-place), matching Julia’s weighted boxfilterline!

box_segment 🔒

Box segmentation for finding tissue regions

check_box_sizes 🔒

Check and adjust box sizes to fit image dimensions

fill_and_smooth 🔒

Fill holes and smooth the lowpass field with weighted smoothing

fill_holes

Fill holes in a binary mask

flood_fill_component 🔒

Find connected component using flood fill (6-connectivity in 3D)

gaussian_smooth_3d

3D Gaussian smoothing using box filter approximation

gaussian_smooth_3d_boxsizes

Simplified smoothing with explicit box sizes (for robustmask post-processing)

get_box_sizes 🔒

Calculate box sizes to approximate Gaussian with given sigma using n box filters

get_sensitivity

Get sensitivity (bias field) from magnitude

idx3d 🔒

Index into 3D array (Fortran/column-major order)

makehomogeneous

Make magnitude homogeneous by dividing by bias field

nan_box_filter_line 🔒

1D box filter with NaN handling (for masked smoothing) Matches Julia’s nanboxfilterline!

robust_mask

Create robust mask from magnitude using quantile-based thresholding

rss_combine

RSS (Root Sum of Squares) magnitude combination