Can Breast Compression Be Reduced in Digital Mammography and Breast Tomosynthesis?

OBJECTIVE: The objective of this study was to investigate the impact of decreasing breast compression during digital mammography and breast tomosynthesis (DBT) on perceived pain and image quality. MATERIALS AND METHODS: In this two-part study, two groups of women with prior mammograms were recruited. In part 1, subjects were positioned for craniocaudal (CC) and mediolateral oblique (MLO) views, and four levels of compression force were applied to evaluate changes in breast thickness, perceived pain, and relative tissue coverage. No imaging was performed. In part 2, two MLO DBT images of one breast of each patient were acquired at standard and reduced compression. Blurring artifacts and tissue coverage were judged by three breast imaging radiologists, and compression force, breast thickness, relative tissue coverage, and perceived pain were recorded. RESULTS: Only the first reduction in force was feasible because further reduction resulted in inadequate breast immobilization. Mean force reductions of 48% and 47% for the CC and MLO views, respectively, resulted in a significantly reduced perceived pain level, whereas the thickness of the compressed breast increased by 0.02 cm (CC view) and 0.09 (MLO view, part 1 of the study) and 0.38 cm (MLO view, part 2 of the study), respectively, with no change in tissue coverage or increase in motion blurring. CONCLUSION: Mammography and DBT acquisitions may be possible using half of the compression force used currently, with a significant and substantial reduction in perceived pain with no clinically significant change in breast thickness and tissue coverage.

The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

To characterize and develop a patient-based 3D model of the compressed breast undergoing mammography and breast tomosynthesis. During this IRB-approved, HIPAA-compliant study, 50 women were recruited to undergo 3D breast surface imaging with structured light (SL) during breast compression, along with simultaneous acquisition of a tomosynthesis image. A pair of SL systems were used to acquire 3D surface images by projecting 24 different patterns onto the compressed breast and capturing their reflection off the breast surface in approximately 12-16 s. The 3D surface was characterized and modeled via principal component analysis. The resulting surface model was combined with a previously developed 2D model of projected compressed breast shapes to generate a full 3D model. Data from ten patients were discarded due to technical problems during image acquisition. The maximum breast thickness (found at the chest-wall) had an average value of 56 mm, and decreased 13% towards the nipple (breast tilt angle of 5.2°). The portion of the breast not in contact with the compression paddle or the support table extended on average 17 mm, 18% of the chest-wall to nipple distance. The outermost point along the breast surface lies below the midline of the total thickness. A complete 3D model of compressed breast shapes was created and implemented as a software application available for download, capable of generating new random realistic 3D shapes of breasts undergoing compression. Accurate characterization and modeling of the breast curvature and shape was achieved and will be used for various image processing and clinical tasks.

Simulations of breast cancer imaging using gamma-ray stimulated emission computed tomography.

Here, we present an innovative imaging technology for breast cancer using gamma-ray stimulated spectroscopy based on the nuclear resonance fluorescence (NRF) technique. In NRF, a nucleus of a given isotope selectively absorbs gamma rays with energy exactly equal to one of its quantized energy states, emitting an outgoing gamma ray with energy nearly identical to that of the incident gamma ray. Due to its application of NRF, gamma-ray stimulated spectroscopy is sensitive to trace element concentration changes, which are suspected to occur at early stages of breast cancer, and therefore can be potentially used to noninvasively detect and diagnose cancer in its early stages. Using Monte-Carlo simulations, we have designed and demonstrated an imaging system that uses gamma-ray stimulated spectroscopy for visualizing breast cancer. We show that gamma-ray stimulated spectroscopy is able to visualize breast cancer lesions based primarily on the differences in the concentrations of trace elements between diseased and healthy tissue, rather than differences in density that are crucial for X-ray mammography. The technique shows potential for early breast cancer detection; however, improvements are needed in gamma-ray laser technology for the technique to become a clinically feasible method of detecting and diagnosing cancer at early stages.