title: Computed tomography perfusion imaging denoising using gaussian process regression.
abstract: 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.
authors.affiliation: Data-Intensive Research Group, School of Informatics, University of Edinburgh, Edinburgh, UK. F.Zhu@ed.ac.uk
authors.forename: Fan
authors.initials: F
authors.lastname: Zhu
authors.forename: Trevor
authors.initials: T
authors.lastname: Carpenter
authors.forename: David
authors.initials: D
authors.lastname: Rodriguez Gonzalez
authors.forename: Malcolm
authors.initials: M
authors.lastname: Atkinson
authors.forename: Joanna
authors.initials: J
authors.lastname: Wardlaw
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000098:blood supply / Q000000981:diagnostic imaging; D005260:Female; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006439:Hemodynamics; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D016011:Normal Distribution; D055420:Perfusion Imaging / Q000379:methods*; D012044:Regression Analysis; D059629:Signal-To-Noise Ratio; D013997:Time Factors