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Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer. J Magn Reson Imaging 2012 Nov;36(5):1088-96

Date

07/25/2012

Pubmed ID

22826198

Pubmed Central ID

PMC3482143

DOI

10.1002/jmri.23770

Scopus ID

2-s2.0-84867818423 (requires institutional sign-in at Scopus site)   78 Citations

Abstract

PURPOSE: To extend the intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) model to restricted diffusion and to simultaneously quantify the perfusion and restricted diffusion parameters in neck nodal metastases.

MATERIALS AND METHODS: The non-gaussian (NG)-IVIM model was developed and tested on diffusion-weighted MRI data collected on a 1.5-Tesla MRI scanner from eight patients with head and neck cancer. Voxel-wise parameter quantification was performed by using a noise-rectified least-square fitting method. The NG-IVIM, IVIM, Kurtosis, and ADC (apparent diffusion coefficient) models were used for comparison. For each voxel, within the metastatic node, the optimal model was determined using the Bayesian Information Criterion. The voxel percentage preferred by each model was calculated and the optimal model map was generated. Monte Carlo simulations were performed to evaluate the accuracy and precision dependency of the new model.

RESULTS: For the eight neck nodes, the range of voxel percentage preferred by the NG-IVIM model was 2.3-79.3%. The optimal modal maps showed heterogeneities within the tumors. The Monte Carlo simulations demonstrated that the accuracy and precision of the NG-IVIM model improved by increasing signal-to-noise ratio and b value.

CONCLUSION: The NG-IVIM model characterizes perfusion and restricted diffusion simultaneously in neck nodal metastases.

Author List

Lu Y, Jansen JF, Mazaheri Y, Stambuk HE, Koutcher JA, Shukla-Dave A

Author

Yonggang Lu PhD Assistant Professor in the Radiology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Algorithms
Computer Simulation
Data Interpretation, Statistical
Female
Head and Neck Neoplasms
Humans
Imaging, Three-Dimensional
Least-Squares Analysis
Lymph Nodes
Lymphatic Metastasis
Male
Middle Aged
Models, Biological
Models, Statistical
Motion
Normal Distribution
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity