456 research outputs found
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
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