Spatio-temporal regularization of flow-fields

Bostan, Emrah; Vardoulis, Orestis; Piccini, Davide; Tafti, Pouya D.; Stergiopulos, Nikolaos; Unser, Michaël

doi:10.1109/isbi.2013.6556605

Cited by 8 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there should be no divergence as blood is an incompressible fluid. Therefore, different techniques exist to filter divergence components [TDGSU11, BVP*13, SLB*16]. Mainly, they try to regularize the flow field by considering its physical properties such as curl, divergence or the flow's rotation behaviour.…”

Section: Flow Data Generation Pipelinementioning

confidence: 99%

Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends and Future Challenges

Oeltze-Jafra

Meuschke

Neugebauer

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This survey focuses on three main application areas. (1) Computational fluid dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D phase‐contrast (4D PC) magnetic resonance imaging of aortic haemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties and providing a more fine‐granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature‐based visualization, the visual comparison of different simulation results and the interactive visual analysis of the flow field and derived characteristics.

show abstract

Section: Flow Data Generation Pipelinementioning

confidence: 99%

Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends and Future Challenges

Oeltze-Jafra

Meuschke

Neugebauer

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Bostan et al. [BVP*13] additionally incorporated conditions about the flow's rotational behaviour and assumed that flow varies smoothly over time. They introduced a flow field regularization that improved the visualization of helical patterns in 4D PC‐MRI data of the aorta.…”

Section: D Pc‐mr Imagingmentioning

confidence: 99%

A Survey of Cardiac 4D PC‐MRI Data Processing

Köhler

Born

Pelt

et al. 2016

Computer Graphics Forum

View full text Add to dashboard Cite

Cardiac four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) acquisitions have gained increasing clinical interest in recent years. They allow to non-invasively obtain extensive information about patient-specific hemodynamics, and thus have a great potential to improve the diagnosis, prognosis and therapy planning of cardiovascular diseases. A dataset contains time-resolved, three-dimensional blood flow directions and strengths, making comprehensive qualitative and quantitative data analysis possible. Quantitative measures, such as stroke volumes, help to assess the cardiac function and to monitor disease progression. Qualitative analysis allows to investigate abnormal flow characteristics, such as vortices, which are correlated to different pathologies. Processing the data comprises complex image processing methods, as well as flow analysis and visualization. In this work, we mainly focus on the aorta. We provide an overview of data measurement and pre-processing, as well as current visualization and quantification methods. This allows other researchers to quickly catch up with the topic and take on new challenges to further investigate the potential of 4D PC-MRI data.

show abstract

“…Accordingly, curl and divergence operators are frequently used since they control the rotational and laminar characteristics, respectively. Combined with vector -norms, these regularizers have been effectively used for denoising fields with discontinuities (occurring at interfaces between different fluids and object boundaries) [3]- [5] and overperformed their quadratic counterparts [6]- [8]. Another approach is based on modeling multi-channel data (for example, color and hyperspectral images) as vectorvalued functions.…”

Section: Improved Variational Denoising Of Flow Fields Withmentioning

confidence: 99%

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

Bostan

Lefkimmiatis

Vardoulis

et al. 2015

IEEE Signal Process. Lett.

Self Cite

View full text Add to dashboard Cite

We propose a new variational framework for the problem of reconstructing flow fields from noisy measurements. The formalism is based on regularizers penalizing the singular values of the Jacobian of the field. Specifically, we rely on the nuclear norm. Our method is invariant with respect to fundamental transformations and can be efficiently solved. We conduct numerical experiments on several phantom data and report improved performance compared to existing vectorial extensions of total variation and curl-divergence regularizations. Finally, we apply our reconstruction method to an experimentally-acquired phase-contrast MRI recording for enhancing the data visualization.

show abstract

Spatio-temporal regularization of flow-fields

Cited by 8 publications

References 9 publications

Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends and Future Challenges

Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends and Future Challenges

A Survey of Cardiac 4D PC‐MRI Data Processing

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

Contact Info

Product

Resources

About