Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings

TL;DR

This paper introduces a decentralized, browser-native GPU architecture for interactive MRI digital twins that operates efficiently on low-cost edge devices, enabling accessible, real-time medical visualization without server reliance.

Contribution

It presents a novel WebGPU-based framework for client-side MRI rendering, bypassing server-side pipelines and enabling real-time interaction in resource-limited settings.

Findings

Achieves under 920ms Time to First Pixel for MRI visualization.

Maintains >= 82 FPS for interactive manipulation.

Operates fully in-browser without external computational dependencies.

Abstract

Digital Twin (DT) technology holds immense potential for surgical planning and personalized medicine. However, generating interactive, patient-specific anatomical twins currently relies on computationally heavy Server-Side Rendering (SSR) or expensive local workstations, creating significant barriers to deployment, especially in resource-constrained settings (RCS). This paper presents a decentralized, client-side WebGPU architecture that democratizes access to high-fidelity anatomical Digital Twins. By bypassing standard server-side rendering pipelines, the framework executes deterministic single-pass raymarching and morphological gradient calculations directly on low-cost integrated edge GPUs. Eliminating the network latency inherent to cloud-rendered solutions, the system achieves a Time to First Pixel (TTFP) of under 920.0ms and maintains stable interactivity at >= 82.0 FPS. Continuous Interaction Fidelity is maintained via uniform buffers, enabling zero-latency manipulation of tissue parameters for dynamic clinical decision-making. By proving that complex 3D medical simulations of patient-specific MRI scan can be executed natively in the browser without deep learning or external computational dependencies, this architecture provides a scalable, affordable foundation for the widespread clinical adoption of healthcare Digital Twins.