Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations.

PURPOSE: To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm2 in benign and malignant breast lesions. METHODS: Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm2 ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated. RESULTS: The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at bmax = 600 s/mm2 already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction. CONCLUSION: Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

Non-gaussian diffusion evaluation of the human kidney by Padé exponent model.

PURPOSE: To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects. MATERIALS AND METHODS: Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory-triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b-values [0-750 s/mm2 ]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R2 -test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion-weighted imaging signal decay. RESULTS: The mathematical accuracy of the PEM was significantly higher than for the other three-parameter and the monoexponential model (P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar Rbi2 0.989 ± 0.008, AICcbi 113.3 ± 6.6; cortical Rbi2 0.992 ± 0.006, AICcbi 113.3 ± 5.2) than the three-parameter models (medullar RPadé2 0.965 ± 0.016, AICcPadé 122.6 ± 6.4, RK2 0.954 ± 0.019, AICcK 128.5 ± 6.0; cortical RPadé2 0.989 ± 0.005, AICcPadé 116.3 ± 4.4, RK2 0.985 ± 0.007, AICcK 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar Rmono2 0.898 ± 0.039, AICcmono 141.4 ± 5.6; cortical Rmono2 0.961 ± 0.013, AICcmono 135.4 ± 4.8). CONCLUSION: The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:160-167.