TU-E-217BCD-03: Characterization of Anatomical Noise in Mammography, Tomosynthesis and Breast CT

PURPOSE

The role of breast density in cancer detection has been well characterized, and newer modalities such as breast tomosynthesis and breast CT (bCT) were developed to improve cancer detection in the dense breast by reducing anatomical complexity. Anatomical noise was characterized on a small cohort of patient images and compared across digital mammography, tomosynthesis, and bCT images.

METHODS AND MATERIALS

An IRB-approved and HIPPA-compliant clinical study was performed on women undergoing breast biopsy, and mammography, tomosynthesis, and bCT were performed on both breasts immediately prior to biopsy. A total of 23 women participated in this study, and the unaffected breast (no lesion) was evaluated. A total of 1000 regions of interest were sampled on each image data set, and the 2D noise power spectrum (NPS) was evaluated. This was radially averaged to produce a 1D NPS, and the NPS was fit to a power law: ln{NPS(f)} = alpha+betaxln(f), over an anatomically-relevant range of spatial frequencies. The slope, beta, was averaged across patients and compared between modalities and projections.

RESULTS

The value of beta was determined for bCT data sets, and they were 1.75 (0.424), 1.83 (0.352), and 1.79 (0.397), for the coronal, sagittal and axial views, respectively. For tomosynthesis, beta was 3.06 (0.361) and 3.10 (0.315) for the CC and MLO views, respectively. For mammography, these values were 3.17 (0.226) and 3.30 (0.236), for the CC and MLO views, respectively. The values of beta for breast CT were significantly different than those for tomosynthesis and mammography (p<0.001, all 12 comparisons).

CONCLUSIONS

The results of this investigation demonstrate that the anatomical complexity of the breast, as characterized by the parameter beta, is statistically similar between mammography and tomosynthesis, a somewhat surprising finding. The breast CT image data, however, demonstrate a statistically-significant reduction in beta across all projections. Funded in part by Hologic Corporation and by a grant from the National Institute of Biomedical Imaging and Bioengineering, EB002138.

Statistical texture synthesis of mammographic images with super-blob lumpy backgrounds

Realistic anatomical images are useful for assessment and improvement of medical image quality. The use of synthesized images has the advantage of providing the user with a large number of independent samples, in a controlled environment. We propose a method to generate medical textures that are fully defined by a set of adjustable parameters and a random number generator, and which statistical properties are analytically tractable. This method, called the "clustered lumpy background", is a generalization of the original lumpy background described by Rolland and Barrett (1992). A detailed application of the method in the case of mammography is presented. It is shown that the synthesized images are visually very similar and that their first and second order statistics can be considered as being equivalent.

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