Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings
It addresses the accessibility barrier of high-fidelity anatomical digital twins for surgical planning in resource-constrained settings by enabling client-side rendering without deep learning or external dependencies.
The paper presents a browser-native WebGPU architecture for decentralized direct volume rendering of MRI-based digital twins, achieving a Time to First Pixel under 920ms and stable interactivity at ≥82 FPS on low-cost integrated GPUs, eliminating the need for server-side rendering or expensive workstations.
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.