2,347 research outputs found
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
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
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
Ultrasound Signal Processing: From Models to Deep Learning
Medical ultrasound imaging relies heavily on high-quality signal processing
algorithms to provide reliable and interpretable image reconstructions.
Hand-crafted reconstruction methods, often based on approximations of the
underlying measurement model, are useful in practice, but notoriously fall
behind in terms of image quality. More sophisticated solutions, based on
statistical modelling, careful parameter tuning, or through increased model
complexity, can be sensitive to different environments. Recently, deep learning
based methods have gained popularity, which are optimized in a data-driven
fashion. These model-agnostic methods often rely on generic model structures,
and require vast training data to converge to a robust solution. A relatively
new paradigm combines the power of the two: leveraging data-driven deep
learning, as well as exploiting domain knowledge. These model-based solutions
yield high robustness, and require less trainable parameters and training data
than conventional neural networks. In this work we provide an overview of these
methods from the recent literature, and discuss a wide variety of ultrasound
applications. We aim to inspire the reader to further research in this area,
and to address the opportunities within the field of ultrasound signal
processing. We conclude with a future perspective on these model-based deep
learning techniques for medical ultrasound applications
DeepMB: Deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound
Multispectral optoacoustic tomography (MSOT) is a high-resolution functional
imaging modality that can non-invasively access a broad range of
pathophysiological phenomena by quantifying the contrast of endogenous
chromophores in tissue. Real-time imaging is imperative to translate MSOT into
clinical imaging, visualize dynamic pathophysiological changes associated with
disease progression, and enable in situ diagnoses. Model-based reconstruction
affords state-of-the-art optoacoustic images; however, the image quality
provided by model-based reconstruction remains inaccessible during real-time
imaging because the algorithm is iterative and computationally demanding. Deep
learning affords faster reconstruction, but the lack of ground truth training
data can lead to reduced image quality for in vivo data. We introduce a
framework, termed DeepMB, that achieves accurate optoacoustic image
reconstruction for arbitrary input data in 31 ms per image by expressing
model-based reconstruction with a deep neural network. DeepMB facilitates
accurate generalization to experimental test data through training on signals
synthesized from real-world images and ground truth images generated by
model-based reconstruction. The framework affords in-focus images for a broad
range of anatomical locations because it supports dynamic adjustment of the
reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible
with the data rates and image sizes of modern multispectral optoacoustic
tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images
and demonstrate that the framework reconstructs images 1000 times faster than
the iterative model-based reference method while affording near-identical image
qualities. Accurate and real-time image reconstructions with DeepMB can enable
full access to the high-resolution and multispectral contrast of handheld
optoacoustic tomography
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