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Hyperthermia-enhanced chemo- and radiation therapies have been used in the treatment of various types of cancers such as breast, and soft-tissue sarcoma. Localized hyperthermia can increase the effectiveness of treatment and reduce the dosage requirements. Simultaneous temperature monitoring has generally required the use of additional modalities such as magnetic resonance imaging (MRI). Ultrasound (US) offers the potential for an all-in-one solution for heat induction and temperature monitoring through speed of sound (SOS) measurements. Such a system would ease the utility of hyperthermia-enhanced therapies and minimize the required hardware/costs, thereby presenting an attractive alternative. In this study, we present the results for 1) all-acoustic, in-silico, localized heating of a numerical breast phantom with embedded tumor, and 2) ex-vivo thermometry results, which look at monitoring the changes in water sound speed at 25, 35, and 45 °C in a heterogeneous lamb tissue phantom. The measurements from the thermometry experiment were then compared against the known values from the literature. The results from this study show successful all-acoustic induced localized heating and good agreement between the measured water sound speed values and their literature counterparts, which represent a significant step toward the development of a real-time US hyperthermia system.

Clinical diagnostic imaging modalities of breast cancer are still having certain limitations such as low sensitivity in dense breast in mammography and low specificity of the B-mode ultrasound [1, 2]. Photoacoustic (PA) imaging has been introduced as an enabling tool to acquire important functional information of the pathologic tissues as well as molecular imaging of cancer when it is augmented with nano-sized contrast agents [3]. US tomography (UST) has shown superior utility imaging of the breast and is growing as reliable tool for characterizing breast lesions [4, 5]. In this study, we demonstrate the development of an US and PA tomography (USPAT) system, consists of a full-ring US acquisition and an all-reflective, aberration-independent, and easy to implement light delivery to achieve an omni-directional co-planar ring illumination.

Ultrasound computed tomography (USCT) is a promising medical imaging modality that offers several novel tissue contrasts and holds great potential for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique was combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This non-optimal approach can be overcome by replacing stochastic gradient descent with a structured optimization method, such as the regularized dual averaging (RDA) method, that exploits knowledge of the composition of the cost function. In this work, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that, for a particular choice of regularization function, each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing non-smooth regularization penalties to be naturally incorporated. The wave equation is solved by use of a time-domain k-space pseudo-spectral method, implemented on general-purpose graphics processing units. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by use of stochastic gradient descent.

The purpose of this paper is to describe the technical and clinical performance of SoftVue, a breast imaging device based on the principles of ultrasound tomography. We report on initial results from data acquired from 30 participants in a recently undertaken clinical study. Initial results suggest that SoftVue can accurately image the full range of breast anatomy, including both benign lesions and cancer. Ongoing research is focused on assessing SoftVue's ability to differentiate benign masses from cancer.

Ultrasound tomography (UST) is a breast imaging modality that is radiation free, operator independent, and does not involve breast compression. In the UST system under consideration, the breast is surrounded by a transducer ring that moves along the coronal axis from the chest wall to the nipple region. The deployment of UST in a clinical setting is technically challenging from three major standpoints: acquisition speed, storage capability, and computational requirements. Data acquisition must be fast to maximize patient throughput and minimize image registration artifacts. Unlike traditional ultrasound, hundreds of gigabytes of data must be acquired, buffered, and processed to image various characteristics of breast tissues such as sound speed, attenuation, and reflectivity. The tomographic image reconstruction methods are non-linear, iterative algorithms with significant computational complexity. Moreover, the scanner hosting the acquisition and reconstruction components must satisfy stringent cost, power, and size requirements. For decades, the above constraints have hindered the practicality of UST in a clinical scenario. We describe the design of a UST system that addresses the relevant clinical requirements as a means to demonstrate the feasability of UST deployment in a clinical setting.

Ultrasound Computer Tomography (USCT) is a promising 3D modality for early breast cancer detection, which is expected to provide quantitative imaging. The aim of this paper is to evaluate the quantitative diagnostic value of the USCT images, i.e. sound speed and attenuation images, using X-ray mammograms as ground truth. For this purpose we applied our 2D/3D registration method, which is based on biomechanical modeling of the breast. Mammograms were segmented into fatty, glandular and tumorous tissue. For each tissue, the average sound speed and attenuation in the corresponding USCT images was calculated. Tumorous tissue could be separated from fatty and glandular tissue using a fixed absolute sound speed threshold in all regarded datasets. By combining sound speed and attenuation, the separation between fatty and glandular tissue could be improved. By overlaying sound speed and attenuation information on the mammogram, quantitative and morphological information can be combined for multimodal diagnosis. This may benefit early breast cancer detection in future.

Ultrasound Computer Tomography (USCT) is an upcoming modality for early breast cancer diagnosis, which provides morphological as well as quantitative imaging. In order to compare USCT images to the standard modality X-ray mammography, a 2D/3D registration has to be applied. To analyze the relevance of sound speed images as a quantitative imaging method for tissue characterization, the aim of this paper is to quantify sound speed values of different types of tissue using mammograms as ground truth. Mammograms are segmented and classified into fat, glandular and tumorous tissue. For each tissue class the average sound speed in the registered sound speed image is calculated. The mean absolute sound speed was 1457 m/s for regions segmented as fatty tissue, 1470 m/s for glandular and 1509 m/s for tumorous tissue. For all ten datasets, the sound speed in tumorous tissue was significantly higher than in glandular and fatty tissue. Also glandular and fatty tissue could be separated easily by the absolute sound speed values. By color-coding sound speed, quantitative information from USCT and morphological information from X-ray mammography are fused for combined diagnosis. We believe this method will help radiologists in gaining experience in the reading of USCT images. The combination of diagnostic information is likely to be beneficial to early breast cancer detection.

We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scan- ners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted to the properties of UST images. This dictionary is learned from high reso- lution MRI breast scans using an unsupervised maximum likelihood dictionary learning method. The proposed dictionary-based regular- ization method significantly improves the quality of reconstructed breast UST images. It outperforms the wavelet-based reconstruction and the least squares minimization with lowpass constraints, on both numerical and in vivo data. Our results demonstrate that the use of the learned dictionary improves the image accuracy for up to 4 dB with the exact measurement matrix and for 3.5 dB with the estimated measurement matrix over the wavelet-based reconstruction under the same conditions.

Multiple tissue parameters are useful for early breast cancer detection and diagnosis. Ultrasound tomography is a new, important imaging modality for our computerized ultrasound risk evaluation (CURE) prototype, which employs a ring transducer array to scan the whole breast in a water tank. We use our bent-ray time-of-light ultrasound tomography to reconstruct sound speeds of breasts with different densities. In addition, we develop an ultrasound attenuation reconstruction method based on complex- signal energy ratios. We apply our reconstruction techniques to in vitro and in vivo breast data acquired using the CURE device. Our reconstruction results demonstrate that CURE has great potential to reliably measure multiple mass characteristics by combining sound-speed, attenuation and reflection images.

Commercial medical ultrasound scanners produce images of human tissue by interpreting the information carried by echoes reflected from structures contained inside it. In this paper, a new generation of toroidal ultrasound arrays is used to measure both the signals reflected and transmitted through the tissue. It is shown that transmission measurements encode complete information about the gross structure of the tissue and lead to images that are superior to those obtained form reflection measurements alone. Experimental results are provided for a gel phantom and a human breast in vivo.