{
  "id": 10458253,
  "title": "A Physics-Informed Neural Network Surrogate for Patient-Specific Hepatic Arterial Hemodynamics in Yttrium-90 Radioembolization: Network Architecture, Boundary-Condition Enforcement, and Data Efficiency",
  "url": "https://urgent.news/2026/09/28/a-physics-informed-neural-network-surrogate-for-patient-specific",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-28T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.26.754555v1?rss=1"
  },
  "original_language": "en",
  "account": "A novel approach has been developed to quickly predict the flow distribution of yttrium-90 microspheres during patient-specific trans-arterial radioembolization in liver cancer treatment. This physics-informed neural network (PINN) surrogate uses spatial coordinates to map velocity vectors and pressure fields. By leveraging a converged finite-element CFD solve with 105,284 nodes as reference data, the surrogate is trained on controlled data fractions, ranging from 0 to 0.20, to validate its performance. The NVIDIA PhysicsNeMo framework was employed to compare different network architectures (MLP, Modified Fourier, Multi-Scale Fourier, and SIREN) under both soft and hard boundary condition enforcement. The results showed that the Multi-Scale Fourier network achieved the highest accuracy (R2(u) = 0.978) at a lower computational cost (300,000 steps) compared to other architectures, such as SIREN (R2(u) = 0.976) and MLP (R2(u) = 0.968). The best performance was observed near a supervised data fraction of 0.01, with R2(u) values ranging from approximately 0 to 0.97-0.99. The best Multi-Scale Fourier model achieved R2(u) = 0.985 at the df = 0.20 checkpoint, matching CFD predictions with near-perfect accuracy. Once trained, the network can evaluate the entire 105,284-node field in approximately 2 seconds on a four-core CPU, making it a valuable tool for rapid predictions in patient-specific hepatic arterial hemodynamics during Yttrium-90 radioembolization.",
  "summary": "Trans-arterial radioembolization using yttrium-90 (Y-90) microspheres treats unresectable liver cancer with radiation. The tumor-to-parenchyma dose ratio is mainly governed by the patient-specific hepatic arterial flow distribution, which transports the microspheres. Even though computational fluid dynamics (CFD) simulation can predict this flow distribution, its high computational cost and time…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}