FOD Estimation with Spherical CNNs
Fiber Orientation Distribution (FOD) Estimation enables accurate white matter tractography and microstructural characterization. The fedi_dmri_fod tool implements a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework optimized for neonatal dMRI data.
For full methodological and validation details, refer to:
Overview
The fedi_dmri_fod tool accurately estimates FODs from data acquired with reduced gradient directions (30% of full protocol), enabling faster acquisitions while maintaining high-quality results.
Key Features
Reduced Acquisition Time: Accurate FOD estimation using only 30% of the full gradient direction protocol
Rotational Equivariance: Respects the rotational symmetry of diffusion signals
Neonatal-Optimized: Trained on 43 neonatal dMRI datasets from the Developing Human Connectome Project (dHCP)
Pretrained Model: Automatically downloaded from Hugging Face Hub
Multi-Shell Support: Works with multi-shell dMRI data (b-values: 400, 1000, 2600 s/mm²)
Methodology
The tool converts multi-shell dMRI signals to spherical harmonic (SH) coefficients, applies rotationally equivariant convolutions, and outputs SH coefficients representing the FOD.
Performance and Validation
The sCNN significantly outperforms Multi-Layer Perceptron (MLP) and produces FODs and tractography that are quantitatively comparable and qualitatively highly similar to Hybrid-CSD ground truth, despite using only 30% of the full acquisition data.
Results and Visualizations
Figure 1. Representative FODs from a test subject. (Left column) FODs estimated by the MLP using the full dHCP dataset. (Middle column) FODs estimated by the sCNN using 30% of the diffusion directions. (Right column) Ground truth FODs estimated using Hybrid-CSD with the full dHCP dataset. The sCNN produces FODs that are visually much more similar to the ground truth than the MLP.
Figure 2. Representative tractography results. (Left) Tractogram generated using MLP-predicted FODs. (Middle) Tractogram generated using sCNN-predicted FODs. (Right) Tractogram generated using ground truth FODs (Hybrid-CSD).
Accessing the FOD Estimation Code
The FOD estimation functionality is implemented in fedi_dmri_fod (see fedi_dmri_fod), with the model in FEDI/models/pytorch/models.py (SphericalCNN_FOD_Neonatal class). The pretrained model is automatically downloaded from Hugging Face Hub (feditoolbox/scnn_neonatal_fod_estimation).
Integration with HAITCH Pipeline
The fedi_dmri_fod tool can be used as part of the HAITCH pipeline (STEP 10) for FOD estimation after motion and distortion correction. For more information, see haitch_starts.