S7 Data Structures for Diffusion MRI Tractography • fiber

Overview

The fiber package provides two S7 classes — streamline and bundle — for representing diffusion MRI tractography data in R, together with a concise set of methods:

Function	Description
`new_streamline()`	Construct a single streamline
`new_bundle()`	Construct a bundle of streamlines
`bind_bundles()`	Combine streamlines and/or bundles
`reparametrize()`	Resample onto a uniform arc-length grid
`get_euclidean_length()`	Straight-line distance between endpoints
`get_curvilinear_length()`	Total arc-length
`get_sinuosity()`	Curvilinear / Euclidean length ratio
`get_curvature()`	Curvature profile or summary scalar
`get_torsion()`	Torsion profile or summary scalar
`get_hausdorff_distance()`	Symmetric Hausdorff distance

Classes

A streamline stores:

@points — an $n \times 3$ numeric matrix with columns "X", "Y", "Z".
@point_data — a named list of per-point numeric vectors (length $n$ ).
@streamline_data — a named list of per-streamline numeric scalars.

A bundle stores:

@streamlines — a list of streamline objects.
@bundle_data — a named list of bundle-level metadata.

Installation

You can install the development version of fiber from GitHub with:

# install.packages("pak")
pak::pak("astamm/fiber")

Quick start

library(fiber)

# Build a helix streamline (50 points)
t <- seq(0, 2 * pi, length.out = 50)
sl <- new_streamline(
  points = cbind(X = cos(t), Y = sin(t), Z = t / (2 * pi))
)
sl
#> <streamline [50 pts]>

# Shape descriptors
get_curvilinear_length(sl)
#> [1] 6.358015
get_sinuosity(sl)
#> [1] 6.358015
head(get_curvature(sl))
#>           s    curvature
#> 1 0.0000000 1.537338e-05
#> 2 0.1297554 1.625127e-02
#> 3 0.2595108 4.592627e-02
#> 4 0.3892662 8.636675e-02
#> 5 0.5190216 1.352354e-01
#> 6 0.6487770 1.906608e-01

# Bundle two streamlines
sl2 <- new_streamline(
  points = cbind(X = cos(t) * 1.1, Y = sin(t) * 1.1, Z = t / (2 * pi))
)
b <- bind_bundles(sl, sl2)
b
#> <bundle [2 streamlines | 50–50 pts/streamline]>

# Reparametrize to 20 points each
b20 <- reparametrize(b, n_points = 20L)
b20
#> <bundle [2 streamlines | 20–20 pts/streamline]>

# Hausdorff distance
get_hausdorff_distance(sl, sl2)
#> [1] 0.1