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REVIEW 2 major objections 2 minor 58 references

Topology optimization recovers vascular geometry and blood flow jointly from CTA sinograms.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-06-28 03:05 UTC pith:MLHACTVC

load-bearing objection Joint TO recovery of geometry and flow from sinograms is a new angle on the usual pipeline, but the abstract shows no numbers and the steady-flow assumption looks like a real weakness. the 2 major comments →

arxiv 2606.05487 v2 pith:MLHACTVC submitted 2026-06-03 cs.CE

VASTO: Simultaneous recovery of vascular geometry and blood flow via differentiable topology optimization

Pramod Thombre , Rahul Kumar Padhy , Roshan M. D'Souza , Krishnan Suresh This is my paper
classification cs.CE
keywords vascular reconstructiontopology optimizationblood flowCTA sinogramsadvection-diffusionhemodynamicsdifferentiable projection
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a reconstruction technique that optimizes both the three-dimensional shape of blood vessels and the velocity of blood flowing through them at the same time, starting from time-resolved projection data rather than from already-reconstructed images. Standard pipelines first build the vessel geometry with filtered back-projection or iterative methods and only afterward run separate fluid simulations; because the geometry is locked in place, missing branches or stenoses cannot be discovered. The new formulation places a steady incompressible flow model and a transient advection-diffusion model for contrast inside a topology-optimization loop whose output is passed through a differentiable projection operator that matches the measured sinograms, allowing the geometry parameters and the velocity field to be updated together. A reader should care because the recovered velocities already contain hemodynamic quantities such as wall shear stress and flow splits, removing the need for a downstream CFD stage.

Core claim

A fluid-physics-constrained reconstruction framework that leverages topology optimization to jointly recover vascular geometry and blood velocity directly from time-resolved CTA sinograms by coupling a steady incompressible flow model with a transient advection-diffusion contrast transport model mapped through a differentiable projection operator.

What carries the argument

Differentiable topology optimization loop that parameterizes vascular geometry, solves the coupled steady incompressible flow and transient advection-diffusion equations, and back-projects the resulting contrast field into sinogram space for direct comparison with measured data.

Load-bearing premise

The steady incompressible flow equations plus the advection-diffusion transport model are assumed to capture the essential physics of blood motion and contrast propagation inside the vessels being imaged.

What would settle it

Application of the method to real patient CTA sinograms for which independent catheter-based velocity measurements or high-resolution 3-D angiograms exist, followed by quantitative comparison showing large discrepancies in recovered branch locations or velocity magnitudes.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Recovered velocity fields supply wall-shear-stress and flow-distribution estimates without requiring a separate CFD computation.
  • Unknown anatomical features such as missing branches or stenoses can be recovered because geometry is not fixed before flow estimation.
  • Performance remains stable on synthetic phantoms across a range of projection sparsity and noise levels.
  • The same velocity solution can be used directly for downstream hemodynamic analysis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same differentiable-projection idea could be tested on other modalities such as time-resolved MR angiography if analogous forward operators are available.
  • Joint geometry-flow optimization may reduce the total number of imaging and simulation steps needed in a clinical vascular workflow.
  • If the topology parameterization proves too restrictive on highly tortuous vessels, the method could be extended by relaxing the density-based representation while keeping the physics coupling intact.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript presents VASTO, a differentiable topology optimization framework for jointly recovering vascular geometry and blood velocity directly from time-resolved CTA sinograms. It couples a steady incompressible flow model with a transient advection-diffusion contrast transport model, mapped to sinogram space via a differentiable projection operator. The recovered fields are intended to support downstream hemodynamic quantities such as wall shear stress without a separate CFD step. The approach is demonstrated on synthetic phantoms with varying sparsity and noise, plus representative projection data.

Significance. If the central coupling and optimization succeed with quantitative accuracy, the work would enable physics-constrained joint reconstruction of anatomy and flow, potentially recovering missing branches or stenoses that sequential FBP/IR + CFD pipelines cannot address. The explicit use of topology optimization and end-to-end differentiability through the projection operator is a clear technical strength.

major comments (2)
  1. [Abstract / coupling description] The formulation (abstract and coupling description) assumes a single steady incompressible velocity field suffices for the transient advection-diffusion contrast model. This risks systematic mismatch in contrast arrival times and spatial distribution when in vivo CTA data reflect pulsatile hemodynamics over cardiac cycles; the optimizer could then compensate by altering recovered topology or velocity. No verification that the recovered steady field matches time-averaged or phase-specific ground-truth flow is described.
  2. [Abstract / Results] The abstract states that the method is demonstrated on synthetic phantoms under varying sparsity and noise levels but reports no quantitative error metrics, reconstruction errors, or comparisons against FBP/IR baselines. Without these, the performance claims cannot be evaluated.
minor comments (2)
  1. Specify the exact parameterization chosen for vascular geometry within the topology optimization (e.g., density-based, level-set) and any regularization terms applied to enforce vessel-like structures.
  2. Clarify how the steady-flow assumption is justified for the chosen synthetic phantoms versus the pulsatile nature of real CTA acquisitions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. We address each major comment below and indicate the corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract / coupling description] The formulation (abstract and coupling description) assumes a single steady incompressible velocity field suffices for the transient advection-diffusion contrast model. This risks systematic mismatch in contrast arrival times and spatial distribution when in vivo CTA data reflect pulsatile hemodynamics over cardiac cycles; the optimizer could then compensate by altering recovered topology or velocity. No verification that the recovered steady field matches time-averaged or phase-specific ground-truth flow is described.

    Authors: We acknowledge that the steady incompressible flow assumption is an approximation whose validity depends on the hemodynamics present in the data. All experiments in the manuscript use synthetic phantoms whose ground-truth velocity fields are steady, so the recovered fields are directly comparable to that ground-truth within the problem setting we consider. For in-vivo pulsatile CTA, the mismatch noted by the referee is a genuine concern and could bias the recovered topology. We will add a new paragraph in the Discussion section that explicitly states this modeling choice, relates the steady solution to time-averaged flow, and outlines the limitations for cardiac-cycle-resolved data. revision: partial

  2. Referee: [Abstract / Results] The abstract states that the method is demonstrated on synthetic phantoms under varying sparsity and noise levels but reports no quantitative error metrics, reconstruction errors, or comparisons against FBP/IR baselines. Without these, the performance claims cannot be evaluated.

    Authors: The abstract is intentionally concise and therefore omits numerical values. The body of the manuscript (Sections 4 and 5) already contains quantitative reconstruction errors for both geometry and velocity, as well as direct comparisons against FBP and iterative reconstruction baselines under the same sparsity and noise conditions. To address the referee’s concern, we will expand the abstract by one sentence that reports the key quantitative metrics (e.g., relative L2 errors and Dice scores) obtained on the synthetic phantoms. revision: yes

Circularity Check

0 steps flagged

No circularity: standard physics-constrained optimization from external data

full rationale

The paper defines an optimization problem that minimizes mismatch between observed time-resolved CTA sinograms and projections of a coupled steady incompressible flow plus transient advection-diffusion model. The derivation chain consists of standard Navier-Stokes, advection-diffusion, and differentiable projection operators applied to external projection data; no equation reduces to a fitted parameter renamed as prediction, no self-definitional loop, and no load-bearing self-citation chain is present in the provided abstract or description. The central claim is an independent computational method whose validity rests on external data fidelity rather than internal redefinition.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The claim rests on standard fluid dynamics models and differentiability of the projection operator; free parameters likely exist in the optimization objective but are not detailed.

free parameters (1)
  • optimization hyperparameters
    Weights balancing data fidelity, flow constraints, and topology regularization are expected to be tuned but not specified.
axioms (2)
  • domain assumption Blood flow modeled as steady incompressible
    Invoked directly in the flow model coupling.
  • domain assumption Contrast transport follows advection-diffusion equation
    Used for the transient transport model.

pith-pipeline@v0.9.1-grok · 6116 in / 1215 out tokens · 62948 ms · 2026-06-28T03:05:42.976436+00:00 · methodology

0 comments
read the original abstract

Computed Tomography Angiography (CTA) is widely used to reconstruct vascular geometry from projection measurements, with conventional approaches such as Filtered Back-Projection (FBP) and Iterative Reconstruction (IR) forming the clinical standard. Blood flow is subsequently estimated through Computational Fluid Dynamics (CFD) simulations, which require vascular geometry and boundary conditions to be specified a priori. Since the geometry is fixed prior to flow estimation, the recovery of unknown anatomical features (e.g., missing branches or stenoses) is precluded. In this work, we present a fluid-physics-constrained reconstruction framework that leverages topology optimization (TO) to jointly recover vascular geometry and blood velocity directly from time-resolved CTA sinograms. The formulation couples a steady incompressible flow model with a transient advection-diffusion contrast transport model, mapped to sinogram space through a differentiable projection operator. The recovered velocity fields provide hemodynamic information and can support downstream estimation of wall shear stress and flow distribution, without requiring a separate CFD pipeline. The proposed method is demonstrated on synthetic phantoms under varying sparsity and noise levels, and on representative projection data.

Figures

Figures reproduced from arXiv: 2606.05487 by Krishnan Suresh, Pramod Thombre, Rahul Kumar Padhy, Roshan M. D'Souza.

Figure 1
Figure 1. Figure 1: Graphical abstract of the proposed framework: the predicted sinograms generated from flow and transient [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Conventional CT pipeline on a 2D bifurcating artery phantom with sparse-noisy acquisition. (a) Phantom 2D [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Design-domain representation used in the proposed topology reconstruction framework. (a) Computational [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Optimization loop of the proposed framework. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Schematic illustration of parallel-beam CT forward projection and sinogram generation. A time-resolved [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Joint topology and inlet-flow reconstruction setup. (a) Fluid flow boundary conditions. (b) Transient transport [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Bifurcated-artery benchmark. (a) Sparse sinogram frame at [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Bifurcated-artery benchmark: convergence of the objective. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Arterial stenosis benchmark with 50% lumen narrowing. (a) Fluid flow boundary conditions. (b) Transient [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Idealized symmetric arterial stenosis benchmark with 50% lumen narrowing. (a) Sparse sinogram frame at [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Idealized carotid-bifurcation benchmark with an asymmetric stenosis in one daughter branch. (a) Fluid flow [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Idealized carotid-bifurcation benchmark with an asymmetric stenosis in one daughter branch. (a) Sparse-view [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Reconstruction from noisy sparse-view sinogram measurements. The left column shows the ground-truth [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

58 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    Global Burden of Cardiovascular Diseases and Risks 2023 Collaborators. Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990-2023.Journal of the American College of Cardiology, 86(22):2167–2243, 2025

    2023

  2. [2]

    A comprehensive review of mock circulation loop systems for experimental hemodynamics of cardiovascular diseases.Fluids, 10(7):166, 2025

    Weichen Hong, Vijay Tewari, Jun Chen, Alan P Sawchuk, and Huidan Yu. A comprehensive review of mock circulation loop systems for experimental hemodynamics of cardiovascular diseases.Fluids, 10(7):166, 2025

    2025

  3. [3]

    Hemodynamics.Comprehensive physiology, 6(2):975–1003, 2016

    Timothy W Secomb. Hemodynamics.Comprehensive physiology, 6(2):975–1003, 2016

    2016

  4. [4]

    A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases.Scientific reports, 10(1):6905, 2020

    Zahra Keshavarz-Motamed. A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases.Scientific reports, 10(1):6905, 2020

    2020

  5. [5]

    Effective hemodynamic monitoring.Critical Care, 26(1):294, 2022

    Michael R Pinsky, Maurizio Cecconi, Michelle S Chew, Daniel De Backer, Ivor Douglas, Mark Edwards, Olfa Hamzaoui, Glenn Hernandez, Greg Martin, Xavier Monnet, et al. Effective hemodynamic monitoring.Critical Care, 26(1):294, 2022

    2022

  6. [6]

    Meiying Gao, Haining Zheng, Rui Zhao, Yan Liu, Lizhu Fang, Yigang Du, Chaoyang Wen, and Yisha Tong. Wall shear stress and oscillatory shear index measured using ultrasound vector flow imaging in the femoropopliteal artery of adults without peripheral artery disease.Frontiers in Surgery, 12:1597407, 2025

    2025

  7. [7]

    The impact of patient-specific inlet conditions on oscillatory shear and helicity in haemodynamic simulations of aneurysmal type-b aortic dissection.bioRxiv, pages 2023–02, 2023

    C Stokes, D Ahmed, N Lind, F Haupt, D Becker, J Hamilton, V Muthurangu, H von Tengg-Kobligk, G Papadakis, S Balabani, et al. The impact of patient-specific inlet conditions on oscillatory shear and helicity in haemodynamic simulations of aneurysmal type-b aortic dissection.bioRxiv, pages 2023–02, 2023

    2023

  8. [8]

    LMML Bakker, N Xiao, S Lynch, AAF van de Ven, A UpdePac, M Schaap, N Buls, J de Mey, FN van de V osse, and CA Taylor. Preclinical validation of the advection diffusion flow estimation method using computational patient specific coronary tree phantoms.International Journal for Numerical Methods in Biomedical Engineering, 39(9):e3746, 2023

    2023

  9. [9]

    Geometric uncertainty of patient-specific blood vessels and its impact on aortic hemodynamics: A computational study

    Domagoj Bošnjak, Richard Schussnig, Sascha Ranftl, Gerhard A Holzapfel, and Thomas-Peter Fries. Geometric uncertainty of patient-specific blood vessels and its impact on aortic hemodynamics: A computational study. Computers in Biology and Medicine, 190:110017, 2025

    2025

  10. [10]

    Inverse problems in blood flow modeling: A review.International journal for numerical methods in biomedical engineering, 38(8):e3613, 2022

    David Nolte and Cristóbal Bertoglio. Inverse problems in blood flow modeling: A review.International journal for numerical methods in biomedical engineering, 38(8):e3613, 2022

    2022

  11. [11]

    Contrast enhanced tomographic reconstruction of vascular blood flow based on the navier-stokes equation.Inverse Problems in Science and Engineering, 28(9):1287–1306, 2020

    Bruno Sixou, M Sigovan, and L Boussel. Contrast enhanced tomographic reconstruction of vascular blood flow based on the navier-stokes equation.Inverse Problems in Science and Engineering, 28(9):1287–1306, 2020

    2020

  12. [12]

    Springer Science & Business Media, 2009

    Gabor T Herman.Fundamentals of computerized tomography: image reconstruction from projections. Springer Science & Business Media, 2009

    2009

  13. [13]

    Physics- informed 4d x-ray image reconstruction from ultra-sparse spatiotemporal data.arXiv preprint arXiv:2504.03469, 2025

    Zisheng Yao, Yuhe Zhang, Zhe Hu, Robert Klöfkorn, Tobias Ritschel, and Pablo Villanueva-Perez. Physics- informed 4d x-ray image reconstruction from ultra-sparse spatiotemporal data.arXiv preprint arXiv:2504.03469, 2025

    work page arXiv 2025

  14. [14]

    Huang Shusong, Sigovan Monica, and Sixou Bruno. Deep learning methods for blood flow reconstruction in a vessel with contrast enhanced x-ray computed tomography.International Journal for Numerical Methods in Biomedical Engineering, 40(1):e3785, 2024

    2024

  15. [15]

    Jinyuxuan Guo, Gurnoor Singh Khurana, Alejandro Gonzalo Grande, Juan C del Alamo, and Francisco Contijoch. Computed tomography (ct)-derived cardiovascular flow estimation using physics-informed neural networks improves with sinogram-based training: A simulation study.arXiv preprint arXiv:2511.03876, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  16. [16]

    Guillaume Fahrni, Giuseppe Gullo, Aisha Touray, Stéphane Fournier, Anne-Marie Jouannic, Henri Lu, Damien Racine, Olivier Muller, Chiara Pozzessere, Salah D Qanadli, et al. Investigating the influence of high-speed gantry rotation in cardiac ct on motion artifacts in aortic stenosis patients not premedicated with β-blockers: The fast-cct randomized trial p...

    2023

  17. [17]

    Image-based blood flow estimation using a semi-analytical solution to the advection–diffusion equation in cylindrical domains.Journal of Fluid Mechanics, 924:A18, 2021

    LMML Bakker, N Xiao, AAF Van De Ven, M Schaap, FN Van De V osse, and CA Taylor. Image-based blood flow estimation using a semi-analytical solution to the advection–diffusion equation in cylindrical domains.Journal of Fluid Mechanics, 924:A18, 2021

    2021

  18. [18]

    Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.Science, 367(6481):1026–1030, 2020

    Maziar Raissi, Alireza Yazdani, and George Em Karniadakis. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.Science, 367(6481):1026–1030, 2020

    2020

  19. [19]

    Joint reconstruction and segmentation of noisy velocity images as an inverse navier–stokes problem.Journal of Fluid Mechanics, 944:A40, 2022

    Alexandros Kontogiannis, Scott V Elgersma, Andrew J Sederman, and Matthew P Juniper. Joint reconstruction and segmentation of noisy velocity images as an inverse navier–stokes problem.Journal of Fluid Mechanics, 944:A40, 2022

    2022

  20. [20]

    Generating optimal topologies in structural design using a homoge- nization method.Computer methods in applied mechanics and engineering, 71(2):197–224, 1988

    Martin Philip Bendsøe and Noboru Kikuchi. Generating optimal topologies in structural design using a homoge- nization method.Computer methods in applied mechanics and engineering, 71(2):197–224, 1988

    1988

  21. [21]

    Topology optimization of fluids in stokes flow.International journal for numerical methods in fluids, 41(1):77–107, 2003

    Thomas Borrvall and Joakim Petersson. Topology optimization of fluids in stokes flow.International journal for numerical methods in fluids, 41(1):77–107, 2003

    2003

  22. [22]

    A detailed introduction to density-based topology optimisation of fluid flow problems with implementation in matlab.Structural and Multidisciplinary Optimization, 66(1):12, 2023

    Joe Alexandersen. A detailed introduction to density-based topology optimisation of fluid flow problems with implementation in matlab.Structural and Multidisciplinary Optimization, 66(1):12, 2023

    2023

  23. [23]

    Toflux: a differentiable topology optimization framework for multiphysics fluidic problems: R

    Rahul Kumar Padhy, Krishnan Suresh, and Aaditya Chandrasekhar. Toflux: a differentiable topology optimization framework for multiphysics fluidic problems: R. padhy et al.Structural and Multidisciplinary Optimization, 69(3):56, 2026

    2026

  24. [24]

    Shusong Huang, Monica Sigovan, and Bruno Sixou. Pod method for acceleration of blood flow reconstruction in a vessel with contrast enhanced x-ray ct.Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(4):1478–1490, 2023

    2023

  25. [25]

    Sadman R Sadid, Mohammed S Kabir, Samreen T Mahmud, Md Saiful Islam, AHM Waliul Islam, and M Tarik Arafat. Segmenting 3d geometry of left coronary artery from coronary ct angiography using deep learning for hemodynamic evaluation.Biomedical Physics & Engineering Express, 8(6):065033, 2022

    2022

  26. [26]

    Automatic coronary artery segmentation of ccta images using unet with a local contextual transformer.Frontiers in physiology, 14:1138257, 2023

    Qianjin Wang, Lisheng Xu, Lu Wang, Xiaofan Yang, Yu Sun, Benqiang Yang, and Stephen E Greenwald. Automatic coronary artery segmentation of ccta images using unet with a local contextual transformer.Frontiers in physiology, 14:1138257, 2023

    2023

  27. [27]

    A level-set approach for inverse problems involving obstacles fadil santosa.ESAIM: Control, Optimisation and Calculus of Variations, 1:17–33, 1996

    Fadil Santosa. A level-set approach for inverse problems involving obstacles fadil santosa.ESAIM: Control, Optimisation and Calculus of Variations, 1:17–33, 1996

    1996

  28. [28]

    A survey on level set methods for inverse problems and optimal design

    Martin Burger and Stanley J Osher. A survey on level set methods for inverse problems and optimal design. European journal of applied mathematics, 16(2):263–301, 2005

    2005

  29. [29]

    Simultaneous tomographic reconstruction and segmentation with class priors.Inverse Problems in Science and Engineering, 24(8):1432–1453, 2016

    Mikhail Romanov, Anders Bjorholm Dahl, Yiqiu Dong, and Per Christian Hansen. Simultaneous tomographic reconstruction and segmentation with class priors.Inverse Problems in Science and Engineering, 24(8):1432–1453, 2016

    2016

  30. [30]

    Simultaneous segmentation and reconstruction: A level set method approach for limited view computed tomography.Medical physics, 37(5):2329–2340, 2010

    Sungwon Yoon, Angel R Pineda, and Rebecca Fahrig. Simultaneous segmentation and reconstruction: A level set method approach for limited view computed tomography.Medical physics, 37(5):2329–2340, 2010

    2010

  31. [31]

    Dart: a practical reconstruction algorithm for discrete tomography.IEEE transactions on image processing, 20(9):2542–2553, 2011

    Kees Joost Batenburg and Jan Sijbers. Dart: a practical reconstruction algorithm for discrete tomography.IEEE transactions on image processing, 20(9):2542–2553, 2011

    2011

  32. [32]

    A mini review on fluid topology optimization.Materials, 16(18):6073, 2023

    He Li, Cong Wang, Xuyu Zhang, Jie Li, Jianhu Shen, and Shiwei Zhou. A mini review on fluid topology optimization.Materials, 16(18):6073, 2023

    2023

  33. [33]

    Parametric level set methods for inverse problems.SIAM Journal on Imaging Sciences, 4(2):618–650, 2011

    Alireza Aghasi, Misha Kilmer, and Eric L Miller. Parametric level set methods for inverse problems.SIAM Journal on Imaging Sciences, 4(2):618–650, 2011

    2011

  34. [34]

    Fluto: Graded multi-scale topology optimiza- tion of large contact area fluid-flow devices using neural networks.Engineering with Computers, 40(2):971–987, 2024

    Rahul Kumar Padhy, Aaditya Chandrasekhar, and Krishnan Suresh. Fluto: Graded multi-scale topology optimiza- tion of large contact area fluid-flow devices using neural networks.Engineering with Computers, 40(2):971–987, 2024

    2024

  35. [35]

    V Subramaniam, T Dbouk, and J-L Harion. Topology optimization of conjugate heat transfer systems: A competition between heat transfer enhancement and pressure drop reduction.International Journal of Heat and Fluid Flow, 75:165–184, 2019

    2019

  36. [36]

    Tomatoes: Topology and material optimization for latent heat thermal energy storage devices.Applied Energy, 406:127322, 2026

    Rahul Kumar Padhy, Krishnan Suresh, and Aaditya Chandrasekhar. Tomatoes: Topology and material optimization for latent heat thermal energy storage devices.Applied Energy, 406:127322, 2026

    2026

  37. [37]

    Numerical instabilities in topology optimization: a survey on procedures dealing with checkerboards, mesh-dependencies and local minima.Structural optimization, 16(1):68–75, 1998

    Ole Sigmund and Joakim Petersson. Numerical instabilities in topology optimization: a survey on procedures dealing with checkerboards, mesh-dependencies and local minima.Structural optimization, 16(1):68–75, 1998. 19

    1998

  38. [38]

    Infill optimization for additive manufactur- ing—approaching bone-like porous structures.IEEE transactions on visualization and computer graphics, 24(2):1127–1140, 2017

    Jun Wu, Niels Aage, Rüdiger Westermann, and Ole Sigmund. Infill optimization for additive manufactur- ing—approaching bone-like porous structures.IEEE transactions on visualization and computer graphics, 24(2):1127–1140, 2017

    2017

  39. [39]

    Principles of ct and ct technology.Journal of nuclear medicine technology, 35(3):115–128, 2007

    Lee W Goldman. Principles of ct and ct technology.Journal of nuclear medicine technology, 35(3):115–128, 2007

    2007

  40. [40]

    Simultaneous vessel segmentation and unenhanced prediction using self-supervised dual-task learning in 3d cta (svsup).Computer Methods and Programs in Biomedicine, 224:107001, 2022

    Wenjian Huang, Weizheng Gao, Chao Hou, Xiaodong Zhang, Xiaoying Wang, and Jue Zhang. Simultaneous vessel segmentation and unenhanced prediction using self-supervised dual-task learning in 3d cta (svsup).Computer Methods and Programs in Biomedicine, 224:107001, 2022

    2022

  41. [41]

    Adaptive determination of the finite element stabilization parameters

    Tayfun E Tezduyar. Adaptive determination of the finite element stabilization parameters. InProceedings of the ECCOMAS computational fluid dynamics conference, volume 2001, 2001

    2001

  42. [42]

    Streamline upwind/full galerkin method for solution of convection dominated solute transport problems.Transport in porous media, 16(1):53–74, 1994

    J Noorishad, CF Tsang, Pierre Perrochet, and André Musy. Streamline upwind/full galerkin method for solution of convection dominated solute transport problems.Transport in porous media, 16(1):53–74, 1994

    1994

  43. [43]

    Jax: composable transformations of python+ numpy programs

    James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, et al. Jax: composable transformations of python+ numpy programs. 2018

    2018

  44. [44]

    Moto: Topology optimization for large deformations via an implicit material point method.arXiv preprint arXiv:2603.14596, 2026

    Rahul Kumar Padhy, Aaditya Chandrasekhar, and Krishnan Suresh. Moto: Topology optimization for large deformations via an implicit material point method.arXiv preprint arXiv:2603.14596, 2026

    work page arXiv 2026

  45. [45]

    Efficient and modular implicit differentiation.Advances in neural information processing systems, 35:5230–5242, 2022

    Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert. Efficient and modular implicit differentiation.Advances in neural information processing systems, 35:5230–5242, 2022

    2022

  46. [46]

    Minimal repetition dynamic checkpointing algorithm for unsteady adjoint calculation.SIAM Journal on Scientific Computing, 31(4):2549–2567, 2009

    Qiqi Wang, Parviz Moin, and Gianluca Iaccarino. Minimal repetition dynamic checkpointing algorithm for unsteady adjoint calculation.SIAM Journal on Scientific Computing, 31(4):2549–2567, 2009

    2009

  47. [47]

    Topology optimization of viscoelastic structures using a time-dependent adjoint method.Computer Methods in Applied Mechanics and Engineering, 285:166–187, 2015

    Kai A James and Haim Waisman. Topology optimization of viscoelastic structures using a time-dependent adjoint method.Computer Methods in Applied Mechanics and Engineering, 285:166–187, 2015

    2015

  48. [48]

    The method of moving asymptotes—a new method for structural optimization.International journal for numerical methods in engineering, 24(2):359–373, 1987

    Krister Svanberg. The method of moving asymptotes—a new method for structural optimization.International journal for numerical methods in engineering, 24(2):359–373, 1987

    1987

  49. [49]

    Bo Zhou, S Kevin Zhou, James S Duncan, and Chi Liu. Limited view tomographic reconstruction using a cascaded residual dense spatial-channel attention network with projection data fidelity layer.IEEE transactions on medical imaging, 40(7):1792–1804, 2021

    2021

  50. [50]

    Modelling of coronary artery stenosis and study of hemodynamic under the influence of magnetic fields.Computers in Biology and Medicine, 184:109464, 2025

    Chandra Shekhar Maurya and Abhijeet Kumar. Modelling of coronary artery stenosis and study of hemodynamic under the influence of magnetic fields.Computers in Biology and Medicine, 184:109464, 2025

    2025

  51. [51]

    Jiayu Lu, Jing Wang, Weinan Zhao, and Daguang Li. Numerical analysis of stenosis geometry and stent effects on the hemodynamics of blood flow through the artery.Journal of The Institution of Engineers (India): Series C, 105(3):671–681, 2024

    2024

  52. [52]

    Mathematical modeling of non-newtonian fluid in arterial blood flow through various stenoses.Advances in Difference Equations, 2021(1):340, 2021

    Pinyo Owasit and Somchai Sriyab. Mathematical modeling of non-newtonian fluid in arterial blood flow through various stenoses.Advances in Difference Equations, 2021(1):340, 2021

    2021

  53. [53]

    Carotid bifurcation atherosclerosis

    Christopher K Zarins, Don P Giddens, BK Bharadvaj, Vikrom S Sottiurai, Robert F Mabon, and Seymour Glagov. Carotid bifurcation atherosclerosis. quantitative correlation of plaque localization with flow velocity profiles and wall shear stress.Circulation research, 53(4):502–514, 1983

    1983

  54. [54]

    Artificial intelligence in computed tomography image reconstruction: a review of recent advances.Journal of Computer Assisted Tomography, 49(4):521–530, 2025

    Ran Zhang, Timothy P Szczykutowicz, and Giuseppe V Toia. Artificial intelligence in computed tomography image reconstruction: a review of recent advances.Journal of Computer Assisted Tomography, 49(4):521–530, 2025

    2025

  55. [55]

    Learning a microlocal prior for limited-angle tomography.IMA Journal of Applied Mathematics, 88(6):888–916, 2023

    Siiri Rautio, Rashmi Murthy, Tatiana A Bubba, Matti Lassas, and Samuli Siltanen. Learning a microlocal prior for limited-angle tomography.IMA Journal of Applied Mathematics, 88(6):888–916, 2023

    2023

  56. [56]

    Timing-invariant ct angiography derived from ct perfusion imaging in acute stroke: a diagnostic performance study.American Journal of Neuroradiology, 36(10):1834–1838, 2015

    EJ Smit, E-j V onken, FJA Meijer, JW Dankbaar, AD Horsch, B van Ginneken, B Velthuis, I Van Der Schaaf, and M Prokop. Timing-invariant ct angiography derived from ct perfusion imaging in acute stroke: a diagnostic performance study.American Journal of Neuroradiology, 36(10):1834–1838, 2015

    2015

  57. [57]

    A threshold selection method from gray-level histograms.IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62–66, 1979

    Nobuyuki Otsu. A threshold selection method from gray-level histograms.IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62–66, 1979

    1979

  58. [58]

    Simvascular: an open source pipeline for cardiovascular simulation.Annals of biomedical engineering, 45(3):525– 541, 2017

    Adam Updegrove, Nathan M Wilson, Jameson Merkow, Hongzhi Lan, Alison L Marsden, and Shawn C Shadden. Simvascular: an open source pipeline for cardiovascular simulation.Annals of biomedical engineering, 45(3):525– 541, 2017. 20

    2017