An accurate measurement of the breast glandular fraction, or glandularity, is important for many research and clinical applications, such as breast cancer risk assessment. We propose a method to estimate the loss of glandular tissue detail due to the limited voxel size in tomographic images of the breast. CT images of a breast tissue specimen were acquired using a CdTe single photon counting detector (nominal pixel size of 60 μm) and using a monochromatic synchrotron radiation x-ray beam. Images were reconstructed using a filtered backprojection algorithm at seven different voxel sizes (range 60-420 μm, with a 60 μm step) and twelve groups of Regions of Interest (ROIs) with different percentage and patterns of glandular tissue were extracted. All ROIs within each group contained the same portion of the image (and therefore the same glandular fraction) reconstructed at a different voxel size. The glandular tissue was segmented and the glandularity calculated for all ROIs. A machine learning algorithm was trained on the glandularity values as a function of reconstruction voxel size. After the training was completed, the algorithm could estimate, given a tomographic breast image reconstructed at a given voxel size with a certain glandularity, the increase (or decrease) of glandularity if the same image were reconstructed with a smaller (or larger) voxel dimension. The algorithm was tested on six additional groups of ROIs, resulting in an average relative standard error between the calculated and estimated glandularity of 0.02 ± 0.016.