Robust estimation of the cerebral blood flow in arterial spin labelling

Camille Maumet 1 Pierre Maurel 2 Jean-Christophe Ferré 3 Christian Barillot 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : The introduction of Arterial Spin Labelling (ASL) techniques in Magnetic Resonance Imaging (MRI) has made feasible a non-invasive measurement of the Cerebral Blood Flow (CBF). However, to date, the low signal-to-noise ratio of ASL gives us no option but to repeat the acquisition to accumulate enough data in order to get a reliable signal. The perfusion signal is then usually extracted by averaging across the repetitions. But the sample mean is very sensitive to outliers. A single incorrect observation can therefore be the source of strong detrimental effects on the perfusion-weighted image estimated with the sample mean. We propose to estimate robust ASL CBF maps with M-estimators to overcome the deleterious effects of outliers. The behaviour of this method is compared to z-score thresholding as recommended in [1]. Validation on simulated and real data is provided. Quantitative validation is undertaken by measuring the correlation with the most widespread technique to measure perfusion with MRI: Dynamic Susceptibility weighted Contrast imaging.
Document type :
Journal articles
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00942814
Contributor : Camille Maumet <>
Submitted on : Wednesday, March 19, 2014 - 9:43:32 AM
Last modification on : Tuesday, May 14, 2019 - 9:44:01 PM
Long-term archiving on : Thursday, June 19, 2014 - 11:04:00 AM

File

Maumet_MRI_2014_ASL-M-estimato...
Files produced by the author(s)

Identifiers

Citation

Camille Maumet, Pierre Maurel, Jean-Christophe Ferré, Christian Barillot. Robust estimation of the cerebral blood flow in arterial spin labelling. Magnetic Resonance Imaging, Elsevier, 2014, 32 (5), pp.497 - 504. ⟨10.1016/j.mri.2014.01.016⟩. ⟨inserm-00942814⟩

Share

Metrics

Record views

1675

Files downloads

735