A method for delineation of bone surfaces in photoacoustic computed tomography of the finger

Photoacoustic imaging of interphalangeal peripheral joints is of interest in the context of using the synovial membrane as a surrogate marker of rheumatoid arthritis. Previous work has shown that ultrasound produced by absorption of light at the epidermis reflects on the bone surfaces within the finger. When the reflected signals are backprojected in the region of interest, artifacts are produced, confounding interpretation of the images. In this work, we present an approach where the photoacoustic signals known to originate from the epidermis, are treated as virtual ultrasound transmitters, and a separate reconstruction is performed as in ultrasound reflection imaging. This allows us to identify the bone surfaces. Further, the identification of the joint space is important as this provides a landmark to localize a region-of-interest in seeking the inflamed synovial membrane. The ability to delineate bone surfaces allows us not only to identify the artifacts, but also to identify the interphalangeal joint space without recourse to new US hardware or a new measurement. We test the approach on phantoms and on a healthy human finger

Attenuation Imaging with Pulse-Echo Ultrasound based on an Acoustic Reflector

Ultrasound attenuation is caused by absorption and scattering in tissue and is thus a function of tissue composition, hence its imaging offers great potential for screening and differential diagnosis. In this paper we propose a novel method that allows to reconstruct spatial attenuation distribution in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode, thus it can be implemented on conventional ultrasound systems with minor modifications. We use calibration with water measurements in order to normalize measurements for quantitative imaging of attenuation. In contrast to earlier techniques, we herein show that attenuation reconstructions are possible without any geometric prior on the inclusion location or shape. We present a quantitative evaluation of reconstructions based on simulations, gelatin phantoms, and ex-vivo bovine skeletal muscle tissue, achieving contrast-to-noise ratio of up to 2.3 for an inclusion in ex-vivo tissue.Comment: Accepted at MICCAI 2019 (International Conference on Medical Image Computing and Computer Assisted Intervention

Toward Speed-of-Sound Anisotropy Quantification in Muscle With Pulse-Echo Ultrasound

The velocity of ultrasound longitudinal waves (speed of sound) is emerging as a valuable biomarker for a wide range of diseases, including musculoskeletal disorders. Muscles are fiber-rich tissues that exhibit anisotropic behavior, meaning that velocities vary with the wave-propagation direction. Therefore, quantifying anisotropy is essential to improve velocity estimates while providing a new metric related to muscle composition and architecture. For the first time, this work presents a method to estimate speed-of-sound anisotropy in transversely isotropic tissues using pulse-echo ultrasound. We assume elliptical anisotropy and consider an experimental setup with a flat reflector parallel to the linear probe, with the muscle in between. This setup allows us to measure first-arrival reflection traveltimes using multistatic operation. Unknown muscle parameters are the orientation angle of the anisotropy symmetry axis and the velocities along and across this axis. We derive analytical expressions for the nonlinear relationship between traveltimes and anisotropy parameters, including reflector inclinations. These equations are exact for homogeneous media and are useful to estimate the effective average anisotropy in muscles. To analyze the structure of this forward problem, we formulate the inversion statistically using the Bayesian framework. We demonstrate that anisotropy parameters can be uniquely constrained by combining traveltimes from different reflector inclinations. Numerical results from wide-ranging acquisition and anisotropy properties show that uncertainties in velocity estimates are substantially lower than expected velocity differences in the muscle. Thus, our approach could provide meaningful muscle anisotropy estimates in future clinical applications

Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequency-dependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04dB/cm at 1MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, from images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as help attenuation compensation other ultrasound-based imaging techniques

Ultrasound Aberration Correction based on Local Speed-of-Sound Map Estimation

For beamforming ultrasound (US) signals, typically a spatially constant speed-of-sound (SoS) is assumed to calculate delays. As SoS in tissue may vary relatively largely, this approximation may cause wavefront aberrations, thus degrading effective imaging resolution. In the literature, corrections have been proposed based on unidirectional SoS estimation or computationally-expensive a posteriori phase rectification. In this paper we demonstrate a direct delay correction approach for US beamforming, by leveraging 2D spatial SoS distribution estimates from plane-wave imaging. We show both in simulations and with ex vivo measurements that resolutions close to the wavelength limit can be achieved using our proposed local SoS-adaptive beamforming, yielding a lateral resolution improvement of 22% to 29% on tissue samples with up to 3% SoS-contrast (45m/s). We verify that our method accurately images absolute positions of tissue structures down to sub-pixel resolution of a tenth of a wavelength, whereas a global SoS assumption leads to artifactual localizations.Comment: will be published in the proceedings of the IEEE International Ultrasonics Symposium (IUS) 201

Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation

Speed-of-sound (SoS) is an emerging ultrasound contrast modality, where pulse-echo techniques using conventional transducers offer multiple benefits. For estimating tissue SoS distributions, spatial domain reconstruction from relative speckle shifts between different beamforming sequences is a promising approach. This operates based on a forward model that relates the sought local values of SoS to observed speckle shifts, for which the associated image reconstruction inverse problem is solved. The reconstruction accuracy thus highly depends on the hand-crafted forward imaging model. In this work, we propose to learn the SoS imaging model based on data. We introduce a convolutional formulation of the pulse-echo SoS imaging problem such that the entire field-of-view requires a single unified kernel, the learning of which is then tractable and robust. We present least-squares estimation of such convolutional kernel, which can further be constrained and regularized for numerical stability. In experiments, we show that a forward model learned from k-Wave simulations improves the median contrast of SoS reconstructions by 63%, compared to a conventional hand-crafted line-based wave-path model. This simulation-learned model generalizes successfully to acquired phantom data, nearly doubling the SoS contrast compared to the conventional hand-crafted alternative. We demonstrate equipment-specific and small-data regime feasibility by learning a forward model from a single phantom image, where our learned model quadruples the SoS contrast compared to the conventional hand-crafted model. On in-vivo data, the simulation- and phantom-learned models respectively exhibit impressive 7 and 10 folds contrast improvements over the conventional model