79 research outputs found
CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization
This paper proposes a spatial-Radon domain CT image reconstruction model
based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model
combines the idea of joint image and Radon domain inpainting model of
\cite{Dong2013X} and that of the data-driven tight frames for image denoising
\cite{cai2014data}. It is different from existing models in that both CT image
and its corresponding high quality projection image are reconstructed
simultaneously using sparsity priors by tight frames that are adaptively
learned from the data to provide optimal sparse approximations. An alternative
minimization algorithm is designed to solve the proposed model which is
nonsmooth and nonconvex. Convergence analysis of the algorithm is provided.
Numerical experiments showed that the SRD-DDTF model is superior to the model
by \cite{Dong2013X} especially in recovering some subtle structures in the
images
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
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