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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
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
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