Computed tomography perfusion imaging denoising using Gaussian process regression

Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to rad...

Full description

Saved in:
Bibliographic Details
Published inPhysics in medicine & biology Vol. 57; no. 12; pp. N183 - N198
Main Authors Zhu, Fan, Carpenter, Trevor, Gonzalez, David Rodriguez, Atkinson, Malcolm, Wardlaw, Joanna
Format Journal Article
LanguageEnglish
Published England IOP Publishing 21.06.2012
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0031-9155
1361-6560
DOI:10.1088/0031-9155/57/12/N183