To compare image quality and breast density of two reconstruction methods, the widely-used filtered-back projection (FBP) reconstruction and the iterative heuristic bayesian inference reconstruction (bayesian inference reconstruction plus the method of total variation applied, HBI).Thirty-two clinical DBT data sets with malignant ( = 27) and benign findings ( = 17) were reconstructed using FBP and HBI. Three experienced radiologists evaluated the images independently using a five-point visual grading scale and classified breast density according to the ACR BI-RADS Atlas fifth edition. Image quality metrics included lesion conspicuity, clarity of lesion borders and spicules, noise level, artifacts surrounding the lesion, visibility of parenchyma and breast density.For masses, HBI-image quality was superior to FBP in terms of conspicuity and clarity of lesion borders and spicules ( < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions ( < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method ( < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B.HBI significantly improves lesion visibility compared to FBP. HBI-visibility of of breast parenchyma increased, leading to a lower breast density rating. Applying the HBIR algorithm may improve the diagnostic performance of DBT and decrease the need for additional imaging in patients with dense breasts.Iterative image reconstruction (HBI) substantially improves the image quality of breast tomosynthesis leading to a better visibility of breast carcinomas and reduction of the perceived breast density compared to the widely-used filtered-back projection reconstruction (FPB). Applying HBI may improve the accuracy of breast tomosynthesis and reduce the number of unnecessary breast biopsies. It may also reduce the radiation dose for the patients, which is especially important in the screening context.