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

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

Mrgprd Enhances Excitability in Specific Populations of Cutaneous Murine Polymodal Nociceptors

The Mas-related G protein-coupled receptor D (Mrgprd) is selectively expressed in nonpeptidergic nociceptors that innervate the outer layers of mammalian skin. The function of Mrgprd in nociceptive neurons and the physiologically relevant somatosensory stimuli that activate Mrgprd^-expressing (Mrgprd^+) neurons are currently unknown. To address these issues, we studied three Mrgprd knock-in mouse lines using an ex vivo somatosensory preparation to examine the role of the Mrgprd receptor and Mrgprd+ afferents in cutaneous somatosensation. In mouse hairy skin, Mrgprd, as marked by expression of green fluorescent protein reporters, was expressed predominantly in the population of nonpeptidergic, TRPV1-negative, C-polymodal nociceptors. In mice lacking Mrgprd, this population of nociceptors exhibited decreased sensitivity to cold, heat, and mechanical stimuli. Additionally, in vitro patch-clamp studies were performed on cultured dorsal root ganglion neurons from Mrgprd^(–/–) and Mrgprd^(+/–) mice. These studies revealed a higher rheobase in neurons from Mrgprd^(–/–) mice than from Mrgprd^(+/–) mice. Furthermore, the application of the Mrgprd ligand β-alanine significantly reduced the rheobase and increased the firing rate in neurons from Mrgprd^(+/–) mice but was without effect in neurons from Mrgprd^(–/–) mice. Our results demonstrate that Mrgprd influences the excitability of polymodal nonpeptidergic nociceptors to mechanical and thermal stimuli

Genetic identification of C fibres that detect massage-like stroking of hairy skin in vivo

Stroking of the skin produces pleasant sensations that can occur during social interactions with conspecifics, such as grooming. Despite numerous physiological studies (reviewed in ref. 2), molecularly defined sensory neurons that detect pleasant stroking of hairy skin in vivo have not been reported. Previously, we identified a rare population of unmyelinated sensory neurons in mice that express the G-protein-coupled receptor MRGPRB4. These neurons exclusively innervate hairy skin with large terminal arborizations that resemble the receptive fields of C-tactile (CT) afferents in humans. Unlike other molecularly defined mechanosensory C-fibre subtypes, MRGPRB4^+ neurons could not be detectably activated by sensory stimulation of the skin ex vivo. Therefore, we developed a preparation for calcium imaging in the spinal projections of these neurons during stimulation of the periphery in intact mice. Here we show that MRGPRB4^+ neurons are activated by massage-like stroking of hairy skin, but not by noxious punctate mechanical stimulation. By contrast, a different population of C fibres expressing MRGPRD was activated by pinching but not by stroking, consistent with previous physiological and behavioural data. Pharmacogenetic activation of Mrgprb4-expressing neurons in freely behaving mice promoted conditioned place preference, indicating that such activation is positively reinforcing and/or anxiolytic. These data open the way to understanding the function of MRGPRB4 neurons during natural behaviours, and provide a general approach to the functional characterization of genetically identified subsets of somatosensory neurons in vivo