0000000000732508
AUTHOR
H. Fitzke
Maximum likelihood ADC parameter estimates improve selection of metastatic cervical nodes for patients with head and neck squamous cell cancer
The aim of this work was to determine whether classification of benign and metastatic cervical nodes based on diffusion weighted imaging (DWI) could be improved by use of a maximum likelihood algorithm for derivation of ADC parameters. A non linear least squares (LSQ) algorithm is usually used to fit parameters to the measured MR signal intensities as a function of b-value. LSQ assumes that the noise in high b-values is normally distributed whereas in reality it follows a Rice distribution. To account for the Rician noise, maximum likelihood (ML) algorithms have been proposed that provide unbiased ADC estimates. In this work the monoexponential, stretched exponential and biexponential model…
Multi-scale analysis of apparent diffusion coefficient (ADC) predicts cervical nodal status in patients with head and neck squamous cell carcinoma
The study assess multi-scale diffusion parameters (median volumetric nodal region of interest values, inter-voxel histogram distributions, and intra-voxel diffusion heterogeneity as assessed by the stretched exponential model) as classifiers of nodal status in patients with head and neck squamous cell carcinoma (SCC). Low b value (0, 50, 100) derived nodal ADC (perfusion sensitive) was the key parameter facilitating discrimination of metastatic from benign nodes in patients with head and neck SCC. The stretched exponential derived α value together with histogram features of ADC provide an accurate decision tree model for classification of nodal disease.