Characterization of the diffusion signal of breast tissues using multi-exponential models

Purpose Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. Methods The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. Results A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D-1,D-3 = 0, D-2,D-3 = 1.5 x 10(-3), and D-3,D-3 = 10.8 x 10(-3) mm(2)/s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. Conclusion Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Automated summary

RSI decomposes the diffusion-weighted MRI signal into components with known apparent diffusion coefficients, with optimal number and values determined empirically. A triexponential model was found to best describe breast tissue, with larger signal contributions of slower diffusion components in tumors compared to fibroglandular tissue. The tumor conspicuity in breast RSI model was comparable to that of DCE imaging, suggesting potential use as differential features between healthy and malignant breast tissues.