Share Email Print

Proceedings Paper

Improving arterial spin labeling data by temporal filtering
Author(s): Jan Petr; Jean-Christophe Ferre; Jean-Yves Gauvrit; Christian Barillot
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Arterial spin labeling (ASL) is an MRI method for imaging brain perfusion by magnetically labeling blood in brain feeding arteries. The perfusion is obtained from the difference between images with and without prior labeling. Image noise is one of the main problems of ASL as the difference is around 0.5-2% of the image magnitude. Usually, 20-40 pairs of images need to be acquired and averaged to reach a satisfactory quality. The images are acquired shortly after the labeling to allow the labeled blood to reach the imaged slice. A sequence of images with multiple delays is more suitable for quantification of the cerebral blood flow as it gives more information about the blood arrival and relaxation. Although the quantification methods are sensitive to noise, no filtering or only Gaussian filtering is used to denoise the data in the temporal domain prior to quantification. In this article, we propose an efficient way to use the redundancy of information in the time sequence of each pixel to suppress noise. For this purpose, the vectorial NL-means method is adapted to work in the temporal domain. The proposed method is tested on simulated and real 3T MRI data. We demonstrate a clear improvement of the image quality as well as a better performance compared to Gaussian and normal spatial NL-means filtering.

Paper Details

Date Published: 12 March 2010
PDF: 9 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233B (12 March 2010); doi: 10.1117/12.843960
Show Author Affiliations
Jan Petr, INRIA (France)
Univ. of Rennes I, CNRS, UMR 6074 (France)
INSERM (France)
Jean-Christophe Ferre, INRIA (France)
Univ. Hospital of Rennes (France)
Univ. of Rennnes I, CNRS, UMR 6074 (France)
Jean-Yves Gauvrit, INRIA (France)
Univ. Hospital of Rennes (France)
Univ. of Rennnes I, CNRS, UMR 6074 (France)
Christian Barillot, INRIA (France)
Univ. of Rennnes I, CNRS, UMR 6074 (France)
INSERM (France)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

© SPIE. Terms of Use
Back to Top