Pipeline

HINEC Tractography Pipeline

A complete MATLAB-based diffusion MRI workflow — from raw NIfTI files to reconstructed white matter fiber tracts. Powered by FSL preprocessing, SPD tensor estimation, and three levels of tractography precision.

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RKF45

High-Order Tracking

RKF45 adaptive step-size control with error-tolerance thresholds. 3x more accurate than FACT in U-fiber and crossing-fiber regions.

SPD

Tensor Precision

SPD-constrained 3x3 diffusion tensor fitting. FA, MD, and principal eigenvector maps computed at every voxel across the whole brain.

10-step

End-to-End

10-step preprocessing through tensor fitting, parcellation, tractography, and 3D visualization in a single YAML-configured run.

Architecture

Pipeline Architecture

End-to-end data flow from raw diffusion MRI to reconstructed fiber tracts and connectivity matrices.

01Data Ingestion
NIfTI DWI (4D)bval / bvecT1 anatomicalYAML config
02Preprocessing
B0 extractionT1-enhanced maskingMP-PCA denoisingFUGUE correctionFSL eddy
03Tensor Estimation
WLS tensor fittingSPD constraintFA / MD mapsEigenvector field
04Tractography
FACT (Euler)RK4 (Runge-Kutta)RKF45 (adaptive)ACT tissue masks
05Output
Atlas parcellationConnectivity matrix3D visualizationTract-density images
Modules

Core Modules

Preprocessing Module

v2.1

B0 extraction, T1-enhanced brain masking via epi_reg, MP-PCA denoising, FUGUE distortion correction, and FSL eddy current/motion correction.

Tensor Estimation Engine

v3.0

Log-linear WLS tensor fitting with SPD constraints. Outputs FA, MD, tensor eigenvalues, and principal eigenvector fields.

FACT Tracker

v2.8

Fiber Assignment by Continuous Tracking using Euler integration along the principal eigenvector with FA and angle threshold termination.

RK4 / RKF45 Tracker

v1.5

High-order Runge-Kutta and adaptive RKF45 fiber tracking with trilinear interpolation for superior accuracy in curved tracts.

Parcellation & Connectomics

v2.3

MNI-T1-DWI atlas registration. Schaefer, AAL, and custom atlas support. Region-to-region connectivity matrices.

Validation Suite

v1.8

ISMRM 2015 challenge scoring, IronTract workflow, and Tractometer metrics for automated accuracy evaluation.

Workflow

How It Works

01

Configure via YAML

Set paths, tracker choice, FA threshold, step size, and seed density in a single config file.

02

Automated Preprocessing

10-step FSL pipeline runs automatically: BET, epi_reg masking, denoising, distortion and eddy correction.

03

Tensor Fit & Tracking

SPD tensor fitting at every voxel, then tractography seeded across the whole-brain WM mask.

04

Parcellation & Export

Atlas registration labels streamline endpoints. Exports .trk fibers, connectivity matrices, and tract-density images.

Specifications

Technical Specs

Input

DWI FormatNIfTI (.nii/.nii.gz)
GradientsFSL bval + bvec
AnatomicalT1w NIfTI (optional)
ConfigYAML parameter file

Algorithms

TrackersFACT, RK4, RKF45
Tensor FitWLS, SPD-constrained
InterpolationTrilinear
Step SizeFixed or adaptive

Output

Fiber Tracts.trk / .mat
Scalar MapsFA, MD, RA NIfTI
Connectivity.mat / .csv matrix
Visualization3D rendering
Applications

Use Cases

Pre-Surgical Planning

Map eloquent white matter tracts prior to tumor resection to minimize post-operative deficits.

Traumatic Brain Injury

Quantify diffuse axonal injury severity from FA reductions along major fiber tracts.

Neurodegeneration Tracking

Monitor tract-specific FA decline that precedes grey matter atrophy by 2-3 years.

Developmental Connectomics

Track myelination and white matter maturation from neonates through adolescence.

Normative Atlas Construction

Population-level connectivity matrices from HCP and UK Biobank datasets.

Tractography Validation

ISMRM and IronTract benchmarks for comparing algorithm accuracy against ground truth.

Ready to Map Every Fiber?

Clone the HINEC pipeline, configure your YAML, and start tracking white matter fiber tracts from your own diffusion MRI data.

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