We report and discuss clinical breast imaging results obtained with operator independent ultrasound tomography. A
series of breast exams are carried out using a recently upgraded clinical prototype designed and built on the principles of
ultrasound tomography. The in-vivo performance of the prototype is assessed by imaging patients at the Karmanos
Cancer Institute. Our techniques successfully demonstrate in-vivo tomographic imaging of breast architecture in both
reflection and transmission imaging modes. These initial results indicate that operator-independent whole-breast imaging
and the detection of cancerous breast masses are feasible using ultrasound tomography techniques. This approach has
the potential to provide a low cost, non-invasive, and non-ionizing means of evaluating breast masses. Future work will
concentrate on extending these results to larger trials.
Women with high mammographic breast density are at 4- to 6-fold increased risk of developing breast cancer compared
to women with fatty breasts. However, current breast density estimations rely on mammography, which cannot provide
accurate volumetric breast representation. Therefore, we explored two techniques of breast density evaluation via
ultrasound tomography. A sample of 93 patients was imaged with our clinical prototype; each dataset contained 45-75
tomograms ranging from near the chest wall through the nipple. Whole breast acoustic velocity was determined by
creating image stacks and evaluating the sound speed frequency distribution. Ultrasound percent density (USPD) was
determined by segmenting high sound speed areas from each tomogram using k-means clustering, integrating over the
entire breast, and dividing by total breast area. Both techniques were independently evaluated using two mammographic
density measures: (1) qualitative, determined by a radiologist's visual assessment using BI-RADS Categories, and (2)
quantitative, via semi-automatic segmentation to calculate mammographic percent density (MPD) for craniocaudal and
medio-lateral oblique mammograms. ~140 m/s difference in acoustic velocity was observed between fatty and dense BI-RADS
Categories. Increased sound speed was found with increased BI-RADS Category and quantitative MPD.
Furthermore, strong positive associations between USPD, BI-RADS Category, and calculated MPD were observed.
These results confirm that utilizing sound speed, both for whole-breast evaluation and segmenting locally, can be
implemented to evaluate breast density.
A novel clinical prototype, CURE (Computed Ultrasound Risk Evaluation), is used to collect breast tissue image data of
patients with either benign or malignant masses. Three types of images, reflection, sound speed and attenuation, are
generated from the raw data using tomographic reconstruction algorithms. Each type of image, usually presented as a
gray scale image, maps different characteristics of the breast tissue. This study is focused on fusing all three types of
images to create true color (RGB) images by assigning a different primary color to each type of image. The resulting
fused images display multiple tissue characteristics that can be viewed simultaneously. Preliminary results indicate that
it may be possible to characterize breast masses on the basis of viewing the superimposed information. Such a
methodology has the potential to dramatically reduce the time required to view all the acquired data and to make a
clinical assessment. Since the color scale can be quantified, it may also be possible to segment such images in order to
isolate the regions of interest and to ultimately allow automated methods for mass detection and characterization.