Name
Title | ||
|---|---|---|
Polynomial regularization for robust MRI-based estimation of blood flow velocities and pressure gradients. |
Abstract | ||
|---|---|---|
In cardiovascular diagnostics, phase-contrast MRI is a valuable technique for measuring blood flow velocities and computing blood pressure values. Unfortunately, both velocity and pressure data typically suffer from the strong image noise of velocity-encoded MRI. In the past, separate approaches of regularization with physical a-priori knowledge and data representation with continuous functions have been proposed to overcome these drawbacks. In this article, we investigate polynomial regularization as an exemplary specification of combining these two techniques. We perform time-resolved three-dimensional velocity measurements and pressure gradient computations on MRI acquisitions of steady flow in a physical phantom. Results based on the higher quality temporal mean data are used as a reference. Thereby, we investigate the performance of our approach of polynomial regularization, which reduces the root mean squared errors to the reference data by 45% for velocities and 60% for pressure gradients. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6091684 | EMBC |
Keywords | Field | DocType |
root mean squared errors,polynomial regularization,physical phantom,image representation,time-resolved 3d velocity measurement,blood pressure measurement,image noise,data representation,knowledge representation,biomedical mri,blood flow measurement,pressure gradient computation,mri acquisition,robust mri-based estimation,blood pressure gradient,velocity-encoded mri,blood flow velocity,phantoms,medical image processing,mean square error methods,cardiovascular diagnostics,magnetic resonance imaging,blood pressure,three dimensional,noise,pressure gradient,polynomials,a priori knowledge,reference data,magnetic resonance image,root mean square error,biomedical imaging,phase contrast | Reference data (financial markets),Computer vision,Blood flow,Polynomial,Computer science,Imaging phantom,Image noise,Regularization (mathematics),Artificial intelligence,Root mean square,Pressure gradient | Conference |
Volume | ISSN | ISBN |
2011 | 1557-170X | 978-1-4244-4122-8 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Delles | 1 | 22 | 3.93 |
| Rengier Fabian | 2 | 0 | 0.68 |
| Sebastian Ley | 3 | 2 | 0.77 |
| von Tengg-Kobligk Hendrik | 4 | 4 | 1.50 |
| Kauczor Hans-Ulrich | 5 | 0 | 1.01 |
| Dillmann Rüdiger | 6 | 0 | 0.34 |
| Unterhinninghofen Roland | 7 | 0 | 0.34 |