Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associated with variations in breast density, and the biological plausibility of the associations with risk of breast cancer. We also discuss the potential for improved risk prediction that might be achieved by using alternative breast imaging methods, such as magnetic resonance or ultrasound. After adjustment for other risk factors, breast density is consistently associated with breast cancer risk, more strongly than most other risk factors for this disease, and extensive breast density may account for a substantial fraction of breast cancer. Breast density is associated with risk of all of the proliferative lesions that are thought to be precursors of breast cancer. Studies of twins have shown that breast density is a highly heritable quantitative trait. Associations between breast density and variations in breast histology, risk of proliferative breast lesions, and risk of breast cancer may be the result of exposures of breast tissue to both mitogens and mutagens. Characterization of breast density by mammography has several limitations, and the uses of breast density in risk prediction and breast cancer prevention may be improved by other methods of imaging, such as magnetic resonance or ultrasound tomography.

Although mammography is the gold standard for breast imaging, its limitations result in a high rate of biopsies of benign lesions and a significant false negative rate for women with dense breasts. In response to this imaging performance gap we have been developing a clinical breast imaging methodology based on the principles of ultrasound tomography. The Computed Ultrasound Risk Evaluation (CURE) system has been designed with the clinical goals of whole breast, operator-independent imaging, and differentiation of breast masses. This paper describes the first clinical prototype, summarizes our initial image reconstruction techniques, and presents phantom and preliminary in vivo results. In an initial assessment of its in vivo performance, we have examined 50 women with the CURE prototype and obtained the following results. (1) Tomographic imaging of breast architecture is demonstrated in both CURE modes of reflection and transmission imaging. (2) In-plane spatial resolution of 0.5 mm in reflection and 4 mm in transmission is achieved. (3) Masses > 15 mm in size are routinely detected. (4) Reflection, sound speed, and attenuation imaging of breast masses are demonstrated. These initial results indicate that operator-independent, whole-breast imaging and the detection of breast masses are feasible. Future studies will focus on improved detection and differentiation of masses in support of our long-term goal of increasing the specificity of breast exams, thereby reducing the number of biopsies of benign masses.

We discuss a bent-ray ultrasound tomography algorithm with total-variation (TV) regularization. We have applied this algorithm to 61 in vivo breast datasets collected with our in-house clinical prototype for imaging sound-speed distributions in the breast. Our analysis showed that TV regularization could preserve sharper lesion edges than the classic Tikhonov regularization. Furthermore, the image quality of our TV bent-ray sound-speed tomograms was superior to that of the straight-ray counterparts for all types of breasts within BI-RADS density categories 1 through 4. Our analysis showed that the improvements for average sharpness (in the unit of (m · s)−1) of lesion edges in our TV bent-ray tomograms are between 2.1 to 3.4-fold compared with the straight ray tomograms. Reconstructed sound-speed tomograms illustrated that our algorithm could successfully image fatty and glandular tissues within the breast. We calculated the mean sound-speed values for fatty tissue and breast parenchyma as 1422±9 m/s (mean±SD) and 1487±21 m/s, respectively. Based on 32 lesions in a cohort of 61 patients, we also found that the mean sound-speed for malignant breast lesions 1548±17 m/s was higher, on average, than that of benign ones (1513±27 m/s) (one-sided p < 0.001). These results suggest that, clinically, sound-speed tomograms can be used to assess breast density (and therefore, breast cancer risk), as well as detect and help differentiate breast lesions. Finally, our sound-speed tomograms may also be a useful tool to monitor the clinical response of breast cancer patients to neo-adjuvant chemotherapy.

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Ultrasound computed tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the sound speed distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Both computer-simulation and experimental phantom studies are conducted to demonstrate the use of the WISE method. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.

We present a wide-field, subarcminute-resolution VLA image of the Galactic center region at 330 MHz. With a resolution of $7 00 ; 12 00 and an rms noise of 1.6 mJy beam À1 , this image represents a significant increase in resolution and sensitivity over the previously published VLA image at this frequency. The improved sensitivity has more than tripled the census of small-diameter sources in the region, has resulted in the detection of two new nonthermal filaments (NTFs), 18 NTF candidates, and 30 pulsar candidates, reveals previously known extended sources in greater detail, and has resulted in the first detection of Sagittarius A* in this frequency range.

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Ultrasound imaging is widely used in medicine because of its benign characteristics and real-time capabilities. Physics theory suggests that the application of tomographic techniques may allow ultrasound imaging to reach its full potential as a diagnostic tool allowing it to compete with other tomographic modalities such as x-ray computer tomography, and MRI. This paper describes the construction and use of a prototype tomographic scanner and reports on the feasibility of implementing tomographic theory in practice and the potential of ultrasound (US) tomography in diagnostic imaging. Data were collected with the prototype by scanning two types of phantoms and a cadaveric breast. A specialized suite of algorithms was developed and utilized to construct images of reflectivity and sound speed from the phantom data. The basic results can be summarized as follows. (i) A fast, clinically relevant US tomography scanner can be built using existing technology. (ii) The spatial resolution, deduced from images of reflectivity, is 0.4 mm. The demonstrated 10 cm depth-of-field is superior to that of conventional ultrasound and the image contrast is improved through the reduction of speckle noise and overall lowering of the noise floor. (iii) Images of acoustic properties such as sound speed suggest that it is possible to measure variations in the sound speed of 5 m/s. An apparent correlation with x-ray attenuation suggests that the sound speed can be used to discriminate between various types of soft tissue. (iv) Ultrasound tomography has the potential to improve diagnostic imaging in relation to breast cancer detection.