Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: A feasibility study. Jansen, J. F., Stambuk, H. E., Koutcher, J. A., & Shukla-Dave, A. AJNR Am J Neuroradiol, 31(4):741-8, 2010.
Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: A feasibility study [link]Paper  doi  abstract   bibtex   
BACKGROUND AND PURPOSE: Water in biological structures often displays non-Gaussian diffusion behavior. The objective of this study was to test the feasibility of non-Gaussian fitting by using the kurtosis model of the signal intensity decay curves obtained from DWI by using an extended range of b-values in studies of phantoms and HNSCC. MATERIALS AND METHODS: Seventeen patients with HNSCC underwent DWI by using 6 b-factors (0, 50-1500 s/mm(2)) at 1.5T. Monoexponential (yielding ADC(mono)) and non-Gaussian kurtosis (yielding apparent diffusion coefficient D(app) and apparent kurtosis coefficient K(app)) fits were performed on a voxel-by-voxel basis in selected regions of interest (primary tumors, metastatic lymph nodes, and spinal cord). DWI studies were also performed on phantoms containing either water or homogenized asparagus. To determine whether the kurtosis model provided a significantly better fit than did the monoexponential model, an F test was performed. Spearman correlation coefficients were calculated to assess correlations between K(app) and D(app). RESULTS: The kurtosis model fit the experimental data points significantly better than did the monoexponential model (P < .05). D(app) was approximately twice the value of ADC(mono) (eg, in neck nodal metastases D(app) was 1.54 and ADC(mono) was 0.84). K(app) showed a weak Spearman correlation with D(app) in a homogenized asparagus phantom and for 44% of tumor lesions. CONCLUSIONS: The use of kurtosis modeling to fit DWI data acquired by using an extended b-value range in HNSCC is feasible and yields a significantly better fit of the data than does monoexponential modeling. It also provides an additional parameter, K(app), potentially with added value.
@article{RN40,
   author = {Jansen, J. F. and Stambuk, H. E. and Koutcher, J. A. and Shukla-Dave, A.},
   title = {Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: A feasibility study},
   journal = {AJNR Am J Neuroradiol},
   volume = {31},
   number = {4},
   pages = {741-8},
   ISSN = {1936-959X (Electronic)
0195-6108 (Linking)},
   Abstract = {BACKGROUND AND PURPOSE: Water in biological structures often displays non-Gaussian diffusion behavior. The objective of this study was to test the feasibility of non-Gaussian fitting by using the kurtosis model of the signal intensity decay curves obtained from DWI by using an extended range of b-values in studies of phantoms and HNSCC. MATERIALS AND METHODS: Seventeen patients with HNSCC underwent DWI by using 6 b-factors (0, 50-1500 s/mm(2)) at 1.5T. Monoexponential (yielding ADC(mono)) and non-Gaussian kurtosis (yielding apparent diffusion coefficient D(app) and apparent kurtosis coefficient K(app)) fits were performed on a voxel-by-voxel basis in selected regions of interest (primary tumors, metastatic lymph nodes, and spinal cord). DWI studies were also performed on phantoms containing either water or homogenized asparagus. To determine whether the kurtosis model provided a significantly better fit than did the monoexponential model, an F test was performed. Spearman correlation coefficients were calculated to assess correlations between K(app) and D(app). RESULTS: The kurtosis model fit the experimental data points significantly better than did the monoexponential model (P < .05). D(app) was approximately twice the value of ADC(mono) (eg, in neck nodal metastases D(app) was 1.54 and ADC(mono) was 0.84). K(app) showed a weak Spearman correlation with D(app) in a homogenized asparagus phantom and for 44% of tumor lesions. CONCLUSIONS: The use of kurtosis modeling to fit DWI data acquired by using an extended b-value range in HNSCC is feasible and yields a significantly better fit of the data than does monoexponential modeling. It also provides an additional parameter, K(app), potentially with added value.},
   DOI = {10.3174/ajnr.A1919},
   url = {http://www.ncbi.nlm.nih.gov/pubmed/20037133},
   year = {2010},
   keywords = {f) Advanced Diffusion Models},
   type = {Journal Article}
}
Downloads: 0