79 research outputs found
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
A Review on Deep Learning in Medical Image Reconstruction
Medical imaging is crucial in modern clinics to guide the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most
fundamental and important components of medical imaging, whose major objective
is to acquire high-quality medical images for clinical usage at the minimal
cost and risk to the patients. Mathematical models in medical image
reconstruction or, more generally, image restoration in computer vision, have
been playing a prominent role. Earlier mathematical models are mostly designed
by human knowledge or hypothesis on the image to be reconstructed, and we shall
call these models handcrafted models. Later, handcrafted plus data-driven
modeling started to emerge which still mostly relies on human designs, while
part of the model is learned from the observed data. More recently, as more
data and computation resources are made available, deep learning based models
(or deep models) pushed the data-driven modeling to the extreme where the
models are mostly based on learning with minimal human designs. Both
handcrafted and data-driven modeling have their own advantages and
disadvantages. One of the major research trends in medical imaging is to
combine handcrafted modeling with deep modeling so that we can enjoy benefits
from both approaches. The major part of this article is to provide a conceptual
review of some recent works on deep modeling from the unrolling dynamics
viewpoint. This viewpoint stimulates new designs of neural network
architectures with inspirations from optimization algorithms and numerical
differential equations. Given the popularity of deep modeling, there are still
vast remaining challenges in the field, as well as opportunities which we shall
discuss at the end of this article.Comment: 31 pages, 6 figures. Survey pape
A green prospective for learned post-processing in sparse-view tomographic reconstruction
Deep Learning is developing interesting tools that are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green AI literature, we propose a shallow neural network to perform efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results show that the proposed inexpensive network computes images of comparable (or even higher) quality in about one-fourth of time and is more robust than the widely used and very deep ResUNet for tomographic reconstructions from sparse-view protocols
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