Estimation of mutual information objective function based on Fourier shift theorem: an application to eddy current distortion correction in diffusion tensor imaging
| Autor: | Udomchai Techavipoo, Xin Guan, Song Lai, Jianrong Shi, John Lackey |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Mathematical optimization
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Biophysics Bilinear interpolation Linear interpolation Multivariate interpolation Diffusion Nearest-neighbor interpolation Distortion Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Retrospective Studies Mathematics Models Statistical Fourier Analysis Brain Neoplasms Phantoms Imaging Trilinear interpolation Brain Reproducibility of Results Stairstep interpolation Diffusion Magnetic Resonance Imaging Artifacts Algorithms Interpolation |
| Zdroj: | Magnetic Resonance Imaging. 27:1281-1292 |
| ISSN: | 0730-725X |
| Popis: | Diffusion tensor imaging requires correction of eddy current distortion in diffusion-weighted images. An effective retrospective correction approach is to transform a diffusion-weighted image to maximize the mutual information (MI) between the transformed diffusion-weighted image and the corresponding T2-weighted image. In the literature, either linear interpolation or partial volume interpolation is applied to estimate the MI objective function. However, these interpolation methods induce artifacts to the MI objective function, thus compromising correction results. In this work, the MI objective function is estimated based on interpolation using Fourier shift theorem. This method eliminates the artifacts incurred with the aforementioned interpolation methods. The algorithm is further improved by approximating pixel values using their nearest neighbors in the up-sampled spatial domain, resulting in dramatically increased computational efficiency without compromising the correction results. The effects of varying the number of quantization levels and using Parzen window filtering to smooth the MI objective function are also investigated to obtain optimized algorithm parameters. The diffusion tensor image quality after applying the proposed distortion correction method is significantly improved visually. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect