Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach

Klemens, Fabian; Schuhmann, Sebastian; Balbierer, Roland; Guthausen, Gisela; Nirschl, Hermann; Thäter, Gudrun; Krause, Mathias J.

doi:10.1016/j.compfluid.2019.104391

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…Full-order optimization has also been tried in merging multip. 18 modality blood flow data [73,74]. It is also possible to leverage neural networks for the optimization problem involved in combining ROM models from different datasets [75].…”

Section: Discussionmentioning

confidence: 99%

Integrating multi-fidelity blood flow data with reduced-order data assimilation

Habibi¹,

D’Souza²,

Dawson³

et al. 2021

Preprint

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High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is challenging. Direct blood flow measurement inside the body with in-vivo measurement modalities such as 4D flow magnetic resonance imaging (4D flow MRI) suffer from low resolution and acquisition noise. In-vitro experimental modeling and patient-specific computational fluid dynamics (CFD) models are subject to uncertainty in patient-specific boundary conditions and model parameters. Furthermore, collecting blood flow data in the near-wall region (e.g., wall shear stress) with experimental measurement modalities poses additional challenges. In this study, a computationally efficient data assimilation method called reduced-order modeling Kalman filter (ROM-KF) was proposed, which combined a sequential Kalman filter with reduced-order modeling using a linear model provided by dynamic mode decomposition (DMD). The goal of ROM-KF was to overcome low resolution and noise in experimental and uncertainty in CFD modeling of cardiovascular flows. The accuracy of the method was assessed with 1D Womersley flow, 2D idealized aneurysm, and 3D patient-specific cerebral aneurysm models. Synthetic experimental data were used to enable direct quantification of errors using benchmark datasets. The accuracy of ROM-KF in reconstructing near-wall hemodynamics was assessed by applying the method to problems where near-wall blood flow data were missing in the experimental dataset. The ROM-KF method provided blood flow data that were more accurate than the computational and synthetic experimental datasets and improved near-wall hemodynamics quantification.

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Section: Discussionmentioning

confidence: 99%

Integrating multi-fidelity blood flow data with reduced-order data assimilation

Habibi¹,

D’Souza²,

Dawson³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Flow data assimilation is another active research field, where the reduced-order Kalman filter (Habibi et al, 2021) and local ensemble Kalman filter (Gaidzik et al, 2019(Gaidzik et al, , 2021 have been used. Incorporating CFD simulation using interiorpoint optimization framework (Töger et al, 2020) and lattice Boltzmann-based topology optimization (Klemens et al, 2020). While these works attempted to improve 4D-flow MRI by merging CFD data, it requires expensive CFD simulation for each new acquisition.…”

Section: Related Work On D-flow Mri and Cfdmentioning

confidence: 99%

SRflow: Deep learning based super-resolution of 4D-flow MRI data

Shit

Zimmermann

Ezhov

et al. 2022

Front. Artif. Intell.

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Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10 and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities.

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A Novel Approach of Unsteady Adjoint Lattice Boltzmann Method Based on Circular Function Scheme

Khouzani

Moghadam

2020

J Sci Comput

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Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach

Cited by 9 publications

References 33 publications

Integrating multi-fidelity blood flow data with reduced-order data assimilation

Integrating multi-fidelity blood flow data with reduced-order data assimilation

SRflow: Deep learning based super-resolution of 4D-flow MRI data

A Novel Approach of Unsteady Adjoint Lattice Boltzmann Method Based on Circular Function Scheme

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