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The authors investigate the performance of diffuse optical tomography to image highly heterogeneous media, such as breast tissue, as a function of background heterogeneity. To model the background heterogeneity, they have employed the functional information derived from Gadolinium-enhanced magnetic resonance images of the breast. The authors demonstrate that overall image quality and quantification accuracy worsens as the background heterogeneity increases. Furthermore they confirm the appearance of characteristic artifacts at the boundaries that scale with background heterogeneity. These artifacts are very similar to the ones seen in clinical examinations and can be misinterpreted as actual objects if not accounted for. To eliminate the artifacts and improve the overall image reconstruction, the authors apply a data-correction algorithm that yields superior reconstruction results and is virtually independent of the degree of the background heterogeneity.