Multi-Algorithm Consensus Trajectory Reconstruction

Hybrid Clustering Approach for SEEG Electrode Pathway Analysis

Novel Innovation

Advanced trajectory analysis module combining DBSCAN spatial clustering with Louvain community detection for robust electrode pathway identification, addressing electrode bending and imaging artifacts where single-method approaches fail.

Consensus Algorithm Framework

DBSCAN

Density-Based Spatial Clustering of Applications with Noise

Spatial density clustering based on 3.5mm inter-contact spacing geometry

Louvain

Modularity Optimization

Community detection through graph modularity optimization and resolution tuning

Algorithm Parameters

Epsilon (ε)

Value: 7.5mm (2× inter-contact spacing)
Purpose: Tolerates electrode bending and anatomical constraints
Rationale: DIXI Medical electrodes have 3.5mm spacing

Min Samples

Value: 3 contacts minimum
Purpose: Ensures statistical validity for trajectory fitting
Rationale: Minimum viable electrode segment

Adaptive Optimization

Formula: Score = 0.7×P_valid + 0.3×P_clustered
Purpose: Automatic parameter tuning
Iterations: Maximum 10 refinement steps

Quality Scoring

Components: Multi-weighted assessment
Metrics: Contact count, linearity, spacing, angles
Threshold: Linearity >0.8 acceptable

Performance & Validation

75.0% Overall success rate
(66/88 trajectories)

0.87±0.12 Average linearity
scores

89.4% Spacing validity
(3.0-5.0mm range)

4-10min Processing time
per patient

Patient-Specific Results

Patient	Success Rate	Trajectories	Notes
P6	88.9%	8/9	Best performance - unilateral case
P8	84.2%	16/19	Bilateral implantation - complex anatomy
P7	40.0%	4/10	Challenging case - significant bending
Overall	75.0%	66/88	8-patient validation cohort

Key Capabilities

Robust against electrode bending and anatomical variations
Handles varying contact counts (5-18 contacts per electrode)
Accommodates bilateral implantations and complex trajectories
Eliminates manual parameter tuning through adaptive optimization
Complete pathways from cortical entry to deep brain targets
Automatic quality scoring and geometric consistency validation

Comprehensive Quality Assessment

Linearity Analysis

L = λ₁/(λ₁+λ₂+λ₃)

Method: PCA-based geometric consistency
Threshold: >0.8 acceptable
Purpose: Detect excessive curvature

Curvature Detection

θ = 180° - arccos(v⃗ᵢ·v⃗ᵢ₊₁/|v⃗ᵢ||v⃗ᵢ₊₁|)

Method: Consecutive segment analysis
Threshold: >40° flagged for review
Purpose: Identify sharp bends

Spacing Validation

R = 1 - σ_d/μ_d

Method: Inter-contact distance regularity
Expected: 3.0-5.0mm range (DIXI)
Purpose: Verify electrode integrity

Master Quality Score

Score = Σwᵢ×Sᵢ

Components: Weighted multi-metric
Factors: Count, linearity, spacing, angles
Purpose: Overall trajectory assessment

Clinical Integration

Automated detection of trajectory quality issues requiring manual review
Spline interpolation for smooth pathway visualization (50 points per trajectory)
Compatibility with 3D Slicer's clinical workflow and DICOM standards
Export capabilities for surgical planning and electrical source imaging

Methodological Advantages

The hybrid approach addresses limitations of single-algorithm methods:

DBSCAN Alone

Struggles with variable density electrodes and bending artifacts. Fixed epsilon parameter cannot adapt to all anatomical configurations.

Graph Methods Alone

Sensitive to noise and outliers. Can incorrectly connect contacts from different electrodes in bilateral cases.

Hybrid Consensus

Combines spatial density (DBSCAN) with connectivity structure (Louvain) for robust trajectory identification across varying anatomies.

Adaptive Framework

Automatic parameter optimization eliminates manual tuning while maintaining high success rates across patient cohort.