We compared three mammographic techniques to an MRI technique for quantifying volumetric breast density. TBV, as measured by MRI and mammography techniques were well correlated with regression slopes ranging from 0.92 to 1.06 times that of MRI. One may expect that the MRI TBV to be higher because MRI has access to delineate around the pectorals muscle. However, we found that average TBV was higher for SXA and Volpara but lower for Quantra when compared to MRI. We found most cases of disagreement were driven by differences in TBV measured for MRI versus mammography. shows comparative images selected from the results in . From , the RMSE between SXA and MRI TBV was 108 ml but 65 ml for FGV (not shown), indicating that the observed lack of agreement between mammographic density and MRI density is most likely driven by differences in the total volume. is an example where the difference in TBV caused a large difference in density due to glandular density that extended to the chest wall (i.e. a lack of retroglandular adipose). However, differences seen in seem to be related to the breast having higher attenuation throughout, including the adipose that impacted the mammographic measures. The mammographic images in were acquired from the same woman on the same day (left, MR images on right are the same) and are examples of how measured TBV can vary substantially due to breast positioning. The mammogram in was a reimage of that in 4d to ensure a good nipple profile in the image. Only that in was used in our study results since we chose the last mammogram acquired for each visit. and its calculated measures plotted in were not actually part of the study's quantitative analyses, but only included to illustrate one reason for discrepancy. Comparing the MR images of , there were substantial differences in how the TBV was delineated from truncal subcutaneous in the retroglandular region. In the TBV is substantially less than the mammographically-defined measure.
Six comparisons of the LCC mammograms to their respective left central breast axial-slice MR images on five different women (c and d are the same woman).
As evidenced in , the differences in TBV were mainly due the difficulties in delineating the breast from truncal subcutaneous adipose (for MRI) and variations in breast positioning (for mammography). TBV errors, either by incomplete breast imaging in craniocaudal views or ambiguity in delineating between breast and truncal adipose, seem to be the limiting factor on both accuracy and precision for volumetric breast density. It is unclear if screening mammography mediolateral oblique views would be any better in this regard as this was not tested.
The primary differences between the three mammography techniques and MRI were in the type of references used for defining fibroglandular and adipose tissue. The mammographic techniques do not segregate fibroglandular from adipose tissue while MRI does ultimately label each voxel as one or the other. There were differences in the best fit RMSE values to MRI especially for %FGV and FGV. It was not possible to directly test for explanations. Kallenberg et al. 
found that paddle tilt correction improved the agreement between both percent and absolute FGV of their mammography measures to MRI. The SXA method also attempts to accurately assess breast thickness variations due to compression paddle tilt and warp, and uses a mammogram-specific phantom for a tissue density reference. The Volpara and Quantra methods are proprietary with respect to corrections they may make regarding paddle tilt. Because the SXA model references to fat and fibroglandular tissue, we expected SXA to have higher %FGV and FGV than MRI, Volpara, or Quantra since the water volume of adipose is included in the SXA FGV compartment. We found that eliminating the adipose water volume from the FGV slightly improved the MRI and SXA agreement.
There are several previous comparisons of MRI to mammographic measures of volumetric breast density. The most methodologically similar study to the present study was smaller (n
32) and compared Quantra to MRI density, Kontos et al. 
. The MRI were analyzed using a similar fuzzy C-means segmentation algorithm. Like the present study, TBV and density were found to be highly associated (R2
0.71 and 0.80 respectively), with the average MRI TBV being higher and density being lower than Quantra. However, Kontos et al. found lower overall association of FGV than in our study (R2
0.15). These finding are similar but not identical to our findings and we attribute the differences to the small size of their study and only three subjects with a density greater than 30%. In our study, 48 subjects had breast density higher than 30%. Van Engeland et al. 
compared a proprietary mammographic volumetric breast density to MRI on 22 women. MRI breast density was measured using a manual segmentation technique and the authors reported a high correlation of R2
0.94. In 26 young women, Highnam et al.
compared mammographic volumetric density using Volpara version 1.2.1 and found a correlation of R
0.94 but no further analysis was offered on how the MRI density was measured or on further statistical description. Thus, including our own study, there are at least four studies with four different mammographic volumetric breast density measures that show a high correlation to volumetric breast density by MRI.
It appears that not all volumetric measures of breast density, either by MRI or by mammography, are equivalent. This lack of equivalency may or may not impact their association with breast cancer risk. For example, in fully-adjusted models of 275 breast cancer cases and 825 controls, the SXA method has been shown to have a greater association to breast cancer risk than percentage mammographic breast density 
where the fifth to first quintile odds ratios were 4.1 for SXA breast density and 2.5 for two-dimensional breast density. To date, there have not been reports of breast cancer associations for MRI, Volpara, or Quantra measures of volumetric breast density techniques.
Our study had the following limitations: First, most MR and mammography images were not acquired at the same visit. Images acquired on the same visit could have potentially eliminated some of the observed differences. Second, we did not compare the associations to breast cancer risk across techniques. This will be done in a larger dataset now being collected.
We conclude that volumetric breast density measures of total breast volume, fibroglandular volume, and percent fibroglandular volume from screening digital mammograms calculated from the techniques used in this study are in moderate to substantial agreement with the volume measures derived from MRI. The SXA measure of density showed a higher association to MRI than Volpara or Quantra density measures. However, classification of women by volumetric density by any of the three mammographic techniques is comparable to classifications by MRI density.