Exponential apparent diffusion coefficient (EADC) is an indicator of diffusion-weighted imaging (DWI) and reflects the pathological changes of tissues quantitatively. However, no study has been investigated in the space-occupying kidney disease using EADC values. This study aims to evaluate the diagnostic role of EADC values at a high magnetic field strength (3.0 T) in kidney neoplastic lesions, compared with that of the ADC values. Ninety patients with suspected renal tumors (including 101 suspected renal lesions) and 20 healthy volunteers were performed MRI scanning. Diffusion-weighted imaging was performed with a single-shot spin-echo echo-planar imaging (SE-EPI) sequence at a diffusion gradient of b = 500 s/mm2. We found renal cell carcinoma (RCC) can be distinguished from angiomyolipoma, and clear cell carcinoma can be distinguished from non-clear cell carcinoma by EADC value. There was significant difference in overall EADC values between renal cell carcinoma (0.150 ± 0.059) and angiomyolipoma (0.270 ± 0.108) when b value was 500 s/mm2. When receiver operating characteristic (ROC) was higher than 0.192, the sensitivity and specificity of EADC value of renal cell carcinoma were 84.6 and 81.1 %, respectively. In conclusion, EADC map shows the internal structure of the kidney tumor more intuitively than the ADC map dose, and is also in line with the observation habits of the clinicians. EADC can be used as an effective imaging method for tumor diagnosis.
We present an open-source and MATLAB-based tool with an easy-to-use graphical user interface (GUI) consisting of four polarization analysis approaches: the particle-motion trajectory (a hodogram in a 3D plane), eigenvalue decomposition (EVD) based on the covariance matrix (including two calculation methods), singular value decomposition using principal component analysis, and EVD based on a constructed analytic signal matrix (EVD-ASM). We review the calculation processes and features of the four cited methods. The eigenvalue and eigenvector are applied to obtain the polarization attributes of the three-component (3C) seismic data. Using rose graphs and histograms, the corresponding azimuth and incidence angle are calculated to determine the propagation direction of the seismic wave. Statistical distribution curves of the corresponding rectilinearity and planarity of the waves are also plotted. The polarization analysis GUI can simultaneously analyze two selected data sections in a seismic recording corresponding to P and S waves. We evaluate the performance of these algorithms using real 3C earthquake datasets. Comparison tests indicate that the aforementioned four methods have different time consumption, and the differences between the results of the EVD-ASM and those of the other methods are very small.
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