379 research outputs found
Sparsity-driven sparse-aperture ultrasound imaging
We propose an image formation algorithm for ultrasound imaging based on sparsity-driven regularization functionals. We consider data collected by synthetic transducer arrays, with the primary motivating application being nondestructive evaluation. Our framework involves the use of a physical optics-based forward model of the observation process; the formulation of an optimization problem for image formation; and the solution of that problem through efficient numerical algorithms. Our sparsity-driven, model-based approach achieves the preservation of physical features while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse observation apertures. We demonstrate the effectiveness of our imaging strategy on real ultrasound data
Sparsity driven ultrasound imaging
An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data
Deep Learning for Accelerated Ultrasound Imaging
In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an
increasing demand to reconstruct high quality images from limited number of
data. However, the existing solutions require either hardware changes or
computationally expansive algorithms. To overcome these limitations, here we
propose a novel deep learning approach that interpolates the missing RF data by
utilizing the sparsity of the RF data in the Fourier domain. Extensive
experimental results from sub-sampled RF data from a real US system confirmed
that the proposed method can effectively reduce the data rate without
sacrificing the image quality.Comment: Invited paper for ICASSP 2018 Special Session for "Machine Learning
in Medical Imaging: from Measurement to Diagnosis
Four-Dimensional Computational Ultrasound Imaging of Brain Haemodynamics
Four-dimensional ultrasound imaging of complex biological systems such as the
brain is technically challenging because of the spatiotemporal sampling
requirements. We present computational ultrasound imaging (cUSi), a new imaging
method that uses complex ultrasound fields that can be generated with simple
hardware and a physical wave prediction model to alleviate the sampling
constraints. cUSi allows for high-resolution four-dimensional imaging of brain
haemodynamics in awake and anesthetized mice
Super-resolution photoacoustic and ultrasound imaging with sparse arrays
It has previously been demonstrated that model-based reconstruction methods
relying on a priori knowledge of the imaging point spread function (PSF)
coupled to sparsity priors on the object to image can provide super-resolution
in photoacoustic (PA) or in ultrasound (US) imaging. Here, we experimentally
show that such reconstruction also leads to super-resolution in both PA and US
imaging with arrays having much less elements than used conventionally (sparse
arrays). As a proof of concept, we obtained super-resolution PA and US
cross-sectional images of microfluidic channels with only 8 elements of a
128-elements linear array using a reconstruction approach based on a linear
propagation forward model and assuming sparsity of the imaged structure.
Although the microchannels appear indistinguishable in the conventional
delay-and-sum images obtained with all the 128 transducer elements, the applied
sparsity-constrained model-based reconstruction provides super-resolution with
down to only 8 elements. We also report simulation results showing that the
minimal number of transducer elements required to obtain a correct
reconstruction is fundamentally limited by the signal-to-noise ratio. The
proposed method can be straigthforwardly applied to any transducer geometry,
including 2D sparse arrays for 3D super-resolution PA and US imaging
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
Four-dimensional computational ultrasound imaging of brain hemodynamics
Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain hemodynamics in awake and anesthetized mice.</p
- …