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.

Abstract. Photoacoustic computed tomography (PACT) and ultrasound computed tomography (USCT) are emerging modalities for breast imaging. As in all emerging imaging technologies, computer-simulation studies play a critically important role in developing and optimizing the designs of hardware and image reconstruction methods for PACT and USCT. Using computer-simulations, the parameters of an imaging system can be systematically and comprehensively explored in a way that is generally not possible through experimentation. When conducting such studies, numerical phantoms are employed to represent the physical properties of the patient or object to-be-imaged that influence the measured image data. It is highly desirable to utilize numerical phantoms that are realistic, especially when task-based measures of image quality are to be utilized to guide system design. However, most reported computer-simulation studies of PACT and USCT breast imaging employ simple numerical phantoms that oversimplify the complex anatomical structures in the human female breast. We develop and implement a methodology for generating anatomically realistic numerical breast phantoms from clinical contrast-enhanced magnetic resonance imaging data. The phantoms will depict vascular structures and the volumetric distribution of different tissue types in the breast. By assigning optical and acoustic parameters to different tissue structures, both optical and acoustic breast phantoms will be established for use in PACT and USCT studies.

Abstract. Peripheral neuropathy is a common neurological problem that affects millions of people worldwide. Diagnosis and treatment of this condition are often hindered by the difficulties in making objective, noninvasive measurements of nerve fibers. Photoacoustic microscopy (PAM) has the ability to obtain high resolution, specific images of peripheral nerves without exogenous contrast. We demonstrated the first proof-of-concept imaging of peripheral nerves using PAM. As validated by both standard histology and photoacoustic spectroscopy, the origin of photoacoustic signals is myelin, the primary source of lipids in the nerves. An extracted sciatic nerve sandwiched between two layers of chicken tissue was imaged by PAM to mimic the in vivo case. Ordered fibrous structures inside the nerve, caused by the bundles of myelin-coated axons, could be observed clearly. With further technical improvements, PAM can potentially be applied to monitor and diagnose peripheral neuropathies.

In order to meet the potential need for emergency large-scale retrospective radiation biodosimetry following an accident or attack, we have developed instrumentation and methodology for in vivo electron paramagnetic resonance spectroscopy to quantify concentrations of radiation-induced radicals within intact teeth. This technique has several very desirable characteristics for triage, including independence from confounding biologic factors, a non-invasive measurement procedure, the capability to make measurements at any time after the event, suitability for use by non-expert operators at the site of an event, and the ability to provide immediate estimates of individual doses. Throughout development there has been a particular focus on the need for a deployable system, including instrumental requirements for transport and field use, the need for high throughput, and use by minimally trained operators. Numerous measurements have been performed using this system in clinical and other non-laboratory settings, including in vivo measurements with unexposed populations as well as patients undergoing radiation therapies. The collection and analyses of sets of three serially-acquired spectra with independent placements of the resonator, in a data collection process lasting approximately five minutes, provides dose estimates with standard errors of prediction of approximately 1 Gy. As an example, measurements were performed on incisor teeth of subjects who had either received no irradiation or 2 Gy total body irradiation for prior bone marrow transplantation; this exercise provided a direct and challenging test of our capability to identify subjects who would be in need of acute medical care.

Ultrasound computed tomography (USCT) holds great promise 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 to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This limitation can be overcome by replacing stochastic gradient descent (SGD) 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 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 method. 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.