The paper introduces a physics-informed deep learning framework that reconstructs 3D coronary geometry from dual-view angiography and predicts velocity and pressure fields using a decoupled network with embedded physical priors. Across 32 patients and four flow conditions, the model achieved a trans‑stenotic pressure‑drop error of 2.02% and velocity/pressure relative‑L2 errors of 0.054 and 0.023, respectively, while matching hospital‑measured FFR with 93.8% diagnostic accuracy. The pipeline completes the full angiography‑to‑hemodynamics conversion in about 20 minutes per patient and supports sparse‑data assimilation for revascularization planning.
By Xi Chen, Jianchuan Yang, Hongde Li, Guangxin He, Qiuyu Ye, Qiang Luo, Mao Chen, Wenqi Hu
arXiv:2609.15104v1 Announce Type: cross
Abstract: We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodyna...
By Adri\'an Robles Arques, Mart\'in Ruiz Fernandez, Javier Sanchis, Miguel A. Teruel, Juan Trujillo
arXiv:2606. 06313v1 Announce Type: cross Abstract: Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise computation of near-wall velocity gradients.
By Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza, Amirhossein Arzani
arXiv:2605. 08832v3 Announce Type: replace Abstract: Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary conditions, to solution fields.
By Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert, Benedikt Wiestler, Julian Suk
arXiv:2602. 23035v2 Announce Type: replace Abstract: Cardiac blood flow patterns contain rich information about disease severity and clinical interventions, yet current imaging and computational methods fail to capture underlying relational structures of coherent flow features.
By Viraj Patel, Marko Grujic, Philipp Aigner, Theodor Abart, Marcus Granegger, Deblina Bhattacharjee, Katharine Fraser
The paper presents a Deep Operator Network that reconstructs full‑field 4D mitral regurgitation hemodynamics from sparse planar velocity data and boundary pressure traces. The network is pretrained on a URANS database of eleven orifice phantoms and then fine‑tuned on new cases, achieving rapid predictions in minutes. While the adaptation improves flow topology in the observed plane, reconstruction error increases sharply with distance from that plane, limiting physical consistency in the surrounding volume.
By Jakob Marcel Hoffmann, Yosuke Hasegawa, Alexander Stroh
arXiv:2609.26426v1 Announce Type: new
Abstract: Finite Element Analysis (FEA) is widely used for transient mechanical simulations, but its high computational cost limits real-time and high-resolution...
By Georgios Triantafyllou, Panagiotis G. Kalozoumis, Dimitris K. Iakovidis
arXiv:2505.14717v2 Announce Type: replace-cross
Abstract: Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape...
By Xigui Li, Yuanye Zhou, Feiyang Xiao, Xin Guo, Chen Jiang, Tan Pan, Xingmeng Zhang, Cenyu Liu, Zeyun Miao, Xiansheng Wang, Qimeng Wang, Yichi Zhang, Wenbo Zhang, Hongwei Zhang, Ruoxi Jiang, Fengping Zhu, Limei Han, Chensen Lin, Yuan Cheng
The paper introduces Anatomy-Informed Neural Networks (AINN), which embed soft and hard anatomical priors into the loss function and network architecture to prevent anatomically impossible predictions. AINN is applied to a clinical scenario of aortoiliac deformation caused by a guidewire, modeling vessel and wire dynamics in SE(3) and training with a Wasserstein-2 loss from 2D angiograms. The study verifies the kinematics and loss against ground truth but does not yet train a network, outlining future work to apply the model to real CT data.
By David P. Stonko
The paper presents a deep learning super‑resolution framework for 4D flow MRI of carotid arteries, using convolutional block attention modules to focus on clinically relevant spatial features and reduce noise. Trained on 120 patients with 240 stenosed carotid arteries, the model leverages patient‑specific CFD simulations as high‑resolution ground truth. Results show a significant reduction in RMSE and improved reconstruction of complex flow patterns compared to a baseline without attention.
By Ali Mokhtari, Dominik Obrist
arXiv:2509. 15900v2 Announce Type: replace-cross Abstract: This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models.
By Simon Klaes, Axel Klawonn, Natalie Kubicki, Martin Lanser, Kengo Nakajima, Takashi Shimokawabe, Janine Weber
FlowMoDL is an unrolled neural network designed for highly accelerated 4D flow MRI reconstruction, optimizing both anatomical magnitude and phase-derived velocity accuracy. It alternates a learned (3+1)D spatiotemporal denoiser with conjugate‑gradient data‑consistency updates, using a dual‑pathway conditioning scheme to handle acceleration factors from 10× to 50×. Trained with a deep‑supervision composite loss that penalizes velocity magnitude and angular errors, FlowMoDL outperforms classical and deep‑learning baselines on the multi‑center CMRx4DFlow dataset, achieving superior gradient‑step efficiency and robust convergence across all acceleration factors.
By Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter