## Abstract ## Purpose To investigate inconsistencies between common performance measures for bias field correction reported in several recent studies and propose a solution. ## Materials and Methods A set of synthetic images of a normal brain from the Montréal Simulated Brain Database (SBD) was
The fast automatic algorithm for correction of MR bias field
✍ Scribed by Mikhail V. Milchenko; Oleg S. Pianykh; John M. Tyler
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 609 KB
- Volume
- 24
- Category
- Article
- ISSN
- 1053-1807
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
Purpose
To develop a method for efficient automatic correction of slow‐varying nonuniformity in MR images.
Materials and Methods
The original MR image is represented by a piecewise constant function, and the bias (nonuniformity) field of an MR image is modeled as multiplicative and slow varying, which permits to approximate it with a low‐order polynomial basis in a “log‐domain.” The basis coefficients are determined by comparing partial derivatives of the modeled bias field with the original image.
Results
We tested the resulting algorithm named derivative surface fitting (dsf) on simulated images and phantom and real data. A single iteration was sufficient in most cases to produce a significant improvement to the MR image's visual quality. dsf does not require prior knowledge of intensity distribution and was successfully used on brain and chest images. Due to its design, dsf can be applied to images of any modality that can be approximated as piecewise constant with a multiplicative bias field.
Conclusion
The resulting algorithm appears to be an efficient method for fast correction of slow varying nonuniformity in MR images. J. Magn. Reson. Imaging 2006;. © 2006 Wiley‐Liss, Inc.
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