Towards Digital Twins for Optimal Radioembolization

• URL: http://arxiv.org/abs/2509.02607v1
• Date: Sat, 30 Aug 2025 00:30:42 GMT
• Title: Towards Digital Twins for Optimal Radioembolization
• Authors: Nisanth Kumar Panneerselvam, Guneet Mummaneni, Emilie Roncali,
• Abstract summary: Radioembolization is a localized liver cancer treatment that delivers radioactive microspheres to tumors via a catheter inserted in the hepatic arterial tree.<n> optimization is challenging due to complex hepatic artery anatomy, variable blood flow, and uncertainty in microsphere transport.<n>This work outlines a framework for a liver radioembolization digital twin using high-fidelity computational fluid dynamics (CFD) and/or recent physics-informed machine learning approaches.
• Score: 0.4893896929103368
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Radioembolization is a localized liver cancer treatment that delivers radioactive microspheres (30 micron) to tumors via a catheter inserted in the hepatic arterial tree. The goal is to maximize therapeutic efficacy while minimizing damage to healthy liver tissue. However, optimization is challenging due to complex hepatic artery anatomy, variable blood flow, and uncertainty in microsphere transport. The creation of dynamic, patient-specific digital twins may provide a transformative solution to these challenges. This work outlines a framework for a liver radioembolization digital twin using high-fidelity computational fluid dynamics (CFD) and/or recent physics-informed machine learning approaches. The CFD approach involves microsphere transport calculations in the hepatic arterial tree with individual patient data, which enables personalized treatment planning. Although accurate, traditional CFD is computationally expensive and limits clinical applicability. To accelerate simulations, physics-informed neural networks (PINNs) and their generative extensions play an increasingly important role. PINNs integrate governing equations, such as the Navier-Stokes equations, directly into the neural network training process, enabling mesh-free, data-efficient approximation of blood flow and microsphere transport. Physics-informed generative adversarial networks (PI-GANs), diffusion models (PI-DMs), and transformer-based architectures further enable uncertainty-aware, temporally resolved predictions with reduced computational cost. These AI surrogates not only maintain physical fidelity but also support rapid sampling of diverse flow scenarios, facilitating real-time decision support. Together, CFD and physics-informed AI methods form the foundation of dynamic, patient-specific digital twin to optimize radioembolization planning and ultimately improve clinical outcomes.

Related papers

• A Patient-Specific Digital Twin for Adaptive Radiotherapy of Non-Small Cell Lung Cancer [5.3024618575567235]
  We developed a temporal digital twin architecture for safe radiotherapy.<n>A GRU autoencoder was employed to learn organ specific latent trajectories, which were classified via logistic regression to predict toxicity.<n>Our findings revealed a viable AI driven early warning window, as increasing risk ratings occurred from several fractions before clinical toxicity.
  arXiv  Detail & Related papers  (2026-02-15T01:47:31Z)
• From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement [35.368152968098194]
  FastFOD-Net is an end-to-end deep learning framework enhancing FODs with superior performance and delivering training/inference efficiency for clinical use.<n>This work will facilitate the more widespread adoption of, and build clinical trust in, deep learning based methods for diffusion MRI enhancement.
  arXiv  Detail & Related papers  (2025-08-13T17:56:29Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Convolutional Deep Operator Networks for Learning Nonlinear Focused Ultrasound Wave Propagation in Heterogeneous Spinal Cord Anatomy [0.0]
  Focused ultrasound therapy is a promising tool for optimally targeted treatment of spinal cord injuries.<n>Current approaches rely on computer simulations to solve the governing wave propagation equations.<n>We propose a convolutional deep operator network (DeepONet) to rapidly predict FUS pressure fields in patient spinal cords.
  arXiv  Detail & Related papers  (2024-12-20T18:03:38Z)
• Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction [0.0]
  We use computational fluid dynamics (CFD) to predict the risk of aortic aneurysm growth.<n>We exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization.<n>Our method confirms as a valid alternative that overcomes the curse of dimensionality.
  arXiv  Detail & Related papers  (2024-10-04T09:39:15Z)
• Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces [42.72938925647165]
  Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia. Current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. We are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs.
  arXiv  Detail & Related papers  (2024-09-06T17:48:44Z)
• Classification of Heart Sounds Using Multi-Branch Deep Convolutional Network and LSTM-CNN [7.136933021609078]
  This study develops and evaluates novel deep learning architectures that offer fast, accurate, and cost-effective methods for automatic diagnosis of cardiac diseases.<n>We propose two innovative methodologies: first, a Multi-Branch Deep Convolutional Neural Network (MBDCN) that emulates human auditory processing by utilizing diverse convolutional filter sizes and power spectrum input for enhanced feature extraction.<n>Second, a Long Short-Term Memory-Convolutional Neural (LSCN) model that integrates LSTM blocks with MBDCN to improve time-domain feature extraction.
  arXiv  Detail & Related papers  (2024-07-15T13:02:54Z)
• L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection [44.016805074560295]
  Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), have limitations. We introduce hbox EmoL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors.
  arXiv  Detail & Related papers  (2024-06-07T12:01:37Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Physics-informed neural networks for improving cerebral hemodynamics predictions [0.0]
  In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with fast computational fluid dynamics simulations. Our framework employs in-vivo real-time TCD velocity measurements at several locations in the brain and the baseline vessel cross-sectional areas acquired from 3D images. We validated the predictions of our model against in-vivo velocity measurements obtained via 4D MRI scans.
  arXiv  Detail & Related papers  (2021-08-25T22:19:41Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.