Restoration of MRI data for intensity non-uniformities using local high order intensity statistics
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- 作者：Stathis Hadjidemetriou ; Colin Studholme ; Susanne Mueller ; Michael Weiner ; Norbert Schuff
- 关键词：Image intensity restoration ; MRI bias field correction ; Co-occurrence statistics ; High order image statistics ; Non-stationary Wiener filtering
- 刊名：Medical Image Analysis
- 出版年：2009
- 出版时间：February 2009
- 年：2009
- 卷：13
- 期：1
- 页码：36-48
- 全文大小：929 K
文摘
MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the “bias field”. These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images.