Title
The Use Of Super-Resolution Techniques To Reduce Slice Thickness In Functional Mri
Abstract
The problem of increasing the slice resolution of functional MRI (fMRI) images without a loss in signal-to-noise ratio is considered. In standard fMRI experiments, increasing the slice resolution by a certain factor decreases the signal-to-noise ratio of the images with the same factor. For this purpose an adapted EPI MRI acquisition protocol is proposed, allowing one to acquire slice-shifted images from which one can generate interpolated super-resolution images, with an increased resolution in the slice direction. To solve the problem of correctness and robustness of the created super-resolution images from these slice-shifted datasets, the use of discontinuity preserving regularization methods is proposed. Tests on real morphological, synthetic functional, and real functional MR datasets have been performed, by comparing the obtained super-resolution datasets with high-resolution reference clatasets. In the morphological experiments the image spatial resolution of the different types of images are compared. In the synthetic and real fMRI experiments, on the other hand, the quality of the different clatasets is studied as function of their resulting activation maps. From the results obtained in this study, we conclude that the proposed super-resolution techniques can both improve the signal-to-noise ratio and augment the detectability of small activated areas in fMRI image sets acquired with thicker slices. (C) 2004 Wiley Periodicals, Inc.
Year
DOI
Venue
2004
10.1002/ima.20016
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Keywords
Field
DocType
fMRI, super-resolution algorithm, SNR
Computer vision,Computer science,Discontinuity (linguistics),Correctness,Interpolation,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Slice thickness,Superresolution,Image resolution
Journal
Volume
Issue
ISSN
14
3
0899-9457
Citations 
PageRank 
References 
22
1.83
6
Authors
10
Name
Order
Citations
PageRank
Ronald R Peeters1546.94
Pierre Kornprobst239936.67
Mila Nikolova31792105.71
Stefan Sunaert425721.38
Thierry Vieville59816.35
Gr�goire Malandain612513.07
Rachid Deriche74903633.65
Olivier D. Faugeras893642568.69
Ng Michael94231311.70
Paul Van Hecke10689.17