Improved visualization and quantification of 4D flow data using divergence-free wavelets

Frank Ong,Shreyas S Vasanawala,Umar Tariq,Michael Lustig,Albert Hsiao,Martin Uecker

doi:10.1186/1532-429x-15-s1-e26

Frank Ong, Shreyas S Vasanawala + Show 4 more

Open Access

https://doi.org/10.1186/1532-429x-15-s1-e26

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Abstract

Background 4D flow MRI has the potential to provide global quantification of cardiac flow in a single acquisition [Hsiao, 2012]. However, 4D flow data are often compromised by low velocity-to-noise ratio, potentially caused by MRI acceleration or unfitting vencs. Since blood flow is approximately divergence-free, noise level can be reduced by removing divergence from noisy flow data [Song, 1993] [Busch, 2012]. On the other hand, strict enforcement of divergence-free condition distorts flow around edges as discrete approximation of flow near edges creates divergence. In this current work, we aim to provide an adjustable and fast operation of imposing multi-scale divergence-free conditions on flow data by using divergence-free wavelets. In addition, we utilize the sparsity of flow data in divergencefree wavelet domain [Deriaz, 2006] for further denoising by performing wavelet shrinkage [Donoho, 1995].

Highlights

4D flow MRI has the potential to provide global quantification of cardiac flow in a single acquisition [Hsiao, 2012]
We utilize the sparsity of flow data in divergencefree wavelet domain [Deriaz, 2006] for further denoising by performing wavelet shrinkage [Donoho, 1995]
Flow inconsistency was defined as the absolute difference between flow rates in the aorta and pulmonary trunk and should equal zero for noiseless data

Summary

Introduction

4D flow MRI has the potential to provide global quantification of cardiac flow in a single acquisition [Hsiao, 2012]. 4D flow data are often compromised by low velocity-to-noise ratio, potentially caused by MRI acceleration or unfitting vencs. Since blood flow is approximately divergence-free, noise level can be reduced by removing divergence from noisy flow data [Song, 1993] [Busch, 2012]. Strict enforcement of divergence-free condition distorts flow around edges as discrete approximation of flow near edges creates divergence. In this current work, we aim to provide an adjustable and fast operation of imposing multi-scale divergence-free conditions on flow data by using divergence-free wavelets. We utilize the sparsity of flow data in divergencefree wavelet domain [Deriaz, 2006] for further denoising by performing wavelet shrinkage [Donoho, 1995]

Objectives

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