137 research outputs found
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Limitations on bandwidth and power consumption impose strict bounds on data
rates of diagnostic imaging systems. Consequently, the design of suitable (i.e.
task- and data-aware) compression and reconstruction techniques has attracted
considerable attention in recent years. Compressed sensing emerged as a popular
framework for sparse signal reconstruction from a small set of compressed
measurements. However, typical compressed sensing designs measure a
(non)linearly weighted combination of all input signal elements, which poses
practical challenges. These designs are also not necessarily task-optimal. In
addition, real-time recovery is hampered by the iterative and time-consuming
nature of sparse recovery algorithms. Recently, deep learning methods have
shown promise for fast recovery from compressed measurements, but the design of
adequate and practical sensing strategies remains a challenge. Here, we propose
a deep learning solution termed Deep Probabilistic Sub-sampling (DPS), that
learns a task-driven sub-sampling pattern, while jointly training a subsequent
task model. Once learned, the task-based sub-sampling patterns are fixed and
straightforwardly implementable, e.g. by non-uniform analog-to-digital
conversion, sparse array design, or slow-time ultrasound pulsing schemes. The
effectiveness of our framework is demonstrated in-silico for sparse signal
recovery from partial Fourier measurements, and in-vivo for both anatomical
image and tissue-motion (Doppler) reconstruction from sub-sampled medical
ultrasound imaging data
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
Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques
Beamforming is a signal processing technique to steer, shape, and focus an
electromagnetic wave using an array of sensors toward a desired direction. It
has been used in several engineering applications such as radar, sonar,
acoustics, astronomy, seismology, medical imaging, and communications. With the
advances in multi-antenna technologies largely for radar and communications,
there has been a great interest on beamformer design mostly relying on
convex/nonconvex optimization. Recently, machine learning is being leveraged
for obtaining attractive solutions to more complex beamforming problems. This
article captures the evolution of beamforming in the last twenty-five years
from convex-to-nonconvex optimization and optimization-to-learning approaches.
It provides a glimpse of this important signal processing technique into a
variety of transmit-receive architectures, propagation zones, paths, and
conventional/emerging applications
Roadmap on signal processing for next generation measurement systems
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System
Biomedical Imaging and Analysis In the Age of Big Data and Deep Learning
International audienc
Machine Learning for Beamforming in Audio, Ultrasound, and Radar
Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar.
Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers many of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more.
In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech.
Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data.
Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar
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