Intelligent Neurovascular Imaging Engine (INIE): Topology-Aware Compressed Sensing and Multimodal Super-Resolution for Real-Time Guidance in Clinically Relevant Porcine Stroke Recanalization

TL;DR

The paper introduces INIE, a new imaging framework that improves fast and accurate neurovascular MRI for stroke diagnosis using advanced sensing and reconstruction techniques.

Contribution

INIE integrates topology-aware compressed sensing and multimodal data to enable real-time, high-fidelity neurovascular imaging in stroke scenarios.

Findings

INIE achieved over 70% acquisition acceleration with high reconstruction quality (PSNR ≈35–36 dB, SSIM ≈0.90–0.92).

Topology-aware analysis reduced Betti number deviation by about twofold compared to baseline methods.

INIE improved large-vessel occlusion detection accuracy to ~93% and reduced decision time to under three minutes.

Abstract

Introduction: Rapid and reliable neurovascular imaging is critical for time-sensitive diagnosis in acute cerebrovascular disorders, yet conventional magnetic resonance imaging (MRI) workflows remain constrained by acquisition speed, motion sensitivity, and limited integration of physiological context. We introduce the Intelligent Neurovascular Imaging Engine (INIE), a sensor-informed, topology-aware framework that jointly optimizes accelerated data acquisition, physics-grounded reconstruction, and cross-scale physiological consistency. Methods: INIE combines adaptive sampling, structured low-rank (Hankel) priors, and topology-preserving objectives with multimodal physiological sensors and scanner telemetry, enabling phase-consistent gating and confidence-weighted reconstruction under realistic operating conditions. The framework was evaluated using synthetic phantoms, a translational porcine stroke recanalization model with repeated measures, and retrospective human datasets. Across Nruns=120 acquisition–reconstruction runs derived from Nanimals=18 pigs with animal-level train/validation/test separation, performance was assessed using image quality, topological fidelity, and cross-modal consistency metrics. Multiple-comparison control was performed using Bonferroni/Holm–Bonferroni procedures. Results: INIE achieved acquisition acceleration exceeding 70% while maintaining high reconstruction fidelity (PSNR ≈35–36 dB, SSIM ≈0.90–0.92). Topology-aware analysis showed an approximately twofold reduction in Betti number deviation relative to baseline accelerated methods. Cross-modal validation in a PET subset demonstrated strong agreement between MRI-derived perfusion parameters and metabolic markers (Pearson r≈0.9). INIE improved large-vessel occlusion detection accuracy to approximately 93% and reduced automated time-to-decision to under three minutes. Conclusions: These results indicate that sensor-informed, topology-aware, closed-loop imaging improves the reliability and physiological consistency of accelerated neurovascular MRI and supports faster, more robust decision-making in acute cerebrovascular imaging workflows.

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