4.6 Article

Motion Correction of Multi-b-value Diffusion-weighted Imaging in the Liver

Journal

ACADEMIC RADIOLOGY
Volume 19, Issue 12, Pages 1573-1580

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.acra.2012.07.005

Keywords

Retrospective motion correction of diffusion-weighted imaging data; bulk motion artifacts; intravoxel incoherent motion (IVIM); abdomen

Funding

  1. NCI NIH HHS [P30 CA008748] Funding Source: Medline

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Rationale and Objectives: Motion artifacts are a significant source of error in the acquisition and quantification of parameters from multi-b-value diffusion-weighted imaging (DWI). The objective of this article is to present a reliable method to reduce motion-related artifacts during free-breathing at higher b-values when signal levels are low. Materials and Methods: Twelve patients referred for magnetic resonance imaging of the liver underwent a clinical magnetic resonance imaging examination of the abdominal region that included DWI. Conventional single-shot spin-echo echo planar imaging acquisitions of the liver during free breathing were repeated in a time-resolved manner during a single acquisition to obtain data for multi-b-value analysis, alternating between low and high b-values. Image registration using a normalized mutual information similarity measure was used to correct for spatial misalignment of diffusion-weighted volumes caused by motion. Registration error was estimated indirectly by comparing the normalized root-mean-square error (NRMSE) values of data fitted to the biexponential intra-voxel incoherent motion model before and after motion correction. Regions of interest (ROIs) were selected in the liver close to the surface of the liver and close to internal structures such as large bile ducts and blood vessels. Results: For the 12 patient datasets, the mean NRMSE value for the motion-corrected ROIs (0.38 +/- 0.16) was significantly lower than the mean NRMSE values for the non-motion-corrected ROIs (0.41 +/- 0.13) (P<.05). In cases where there was substantial respiratory motion during the acquisition, visual inspection verified that the algorithm markedly improved alignment of the liver contours between frames. Conclusions: The proposed method addresses motion-related artifacts to increase robustness in multi-b-value acquisitions.

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