11 research outputs found
An approach for radiation dose reduction in computerized tomography
Minimization of radiation dose plays an important role in human wellbeing. Excess of radiation dose leads to cancer. Radiation greatly affects young children less than 10 years of age as their life span is longer. Radiation can be reduced by hardware and/or by software techniques. Hardware methods deal with variation of parameters such as tube voltage, tube current, exposure time, focal distance and filter type. Software techniques include image processing methods. The originally acquired X-ray images may be contaminated with noise due to the fact of instability in the case of sensors, electrical power or X-ray source, that is responsible for the degradation of the image attributes. An enhanced image denoising algorithm has been proposed which decreases Gaussian noise combined with salt and pepper noise that retains most information particulars
Faster PET reconstruction with a stochastic primal-dual hybrid gradient method
Abstract:
Image reconstruction in positron emission tomography (PET) is computationally challenging due to Poisson noise, constraints and potentially non-smooth priors-let alone the sheer size of the problem. An algorithm that can cope well with the first three of the aforementioned challenges is the primal-dual hybrid gradient algorithm (PDHG) studied by Chambolle and Pock in 2011. However, PDHG updates all variables in parallel and is therefore computationally demanding on the large problem sizes encountered with modern PET scanners where the number of dual variables easily exceeds 100 million. In this work, we numerically study the usage of SPDHG-a stochastic extension of PDHG-but is still guaranteed to converge to a solution of the deterministic optimization problem with similar rates as PDHG. Numerical results on a clinical data set show that by introducing randomization into PDHG, similar results as the deterministic algorithm can be achieved using only around 10 % of operator evaluations. Thus, making significant progress towards the feasibility of sophisticated mathematical models in a clinical setting
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Traditional model-based image reconstruction (MBIR) methods combine forward
and noise models with simple object priors. Recent machine learning methods for
image reconstruction typically involve supervised learning or unsupervised
learning, both of which have their advantages and disadvantages. In this work,
we propose a unified supervised-unsupervised (SUPER) learning framework for
X-ray computed tomography (CT) image reconstruction. The proposed learning
formulation combines both unsupervised learning-based priors (or even simple
analytical priors) together with (supervised) deep network-based priors in a
unified MBIR framework based on a fixed point iteration analysis. The proposed
training algorithm is also an approximate scheme for a bilevel supervised
training optimization problem, wherein the network-based regularizer in the
lower-level MBIR problem is optimized using an upper-level reconstruction loss.
The training problem is optimized by alternating between updating the network
weights and iteratively updating the reconstructions based on those weights. We
demonstrate the learned SUPER models' efficacy for low-dose CT image
reconstruction, for which we use the NIH AAPM Mayo Clinic Low Dose CT Grand
Challenge dataset for training and testing. In our experiments, we studied
different combinations of supervised deep network priors and unsupervised
learning-based or analytical priors. Both numerical and visual results show the
superiority of the proposed unified SUPER methods over standalone supervised
learning-based methods, iterative MBIR methods, and variations of SUPER
obtained via ablation studies. We also show that the proposed algorithm
converges rapidly in practice.Comment: 15 pages, 16 figures, submitted journal pape
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solving
inverse problems in imaging, image processing, and computer vision. INNs
combine regression NNs and an iterative model-based image reconstruction (MBIR)
algorithm, often leading to both good generalization capability and
outperforming reconstruction quality over existing MBIR optimization models.
This paper proposes the first fast and convergent INN architecture,
Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum
and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum
terms in extrapolation modules, and noniterative MBIR modules at each iteration
by using majorizers, where each iteration of Momentum-Net consists of three
core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees
convergence to a fixed-point for general differentiable (non)convex MBIR
functions (or data-fit terms) and convex feasible sets, under two asymptomatic
conditions. To consider data-fit variations across training and testing
samples, we also propose a regularization parameter selection scheme based on
the "spectral spread" of majorization matrices. Numerical experiments for
light-field photography using a focal stack and sparse-view computational
tomography demonstrate that, given identical regression NN architectures,
Momentum-Net significantly improves MBIR speed and accuracy over several
existing INNs; it significantly improves reconstruction quality compared to a
state-of-the-art MBIR method in each application.Comment: 28 pages, 13 figures, 3 algorithms, 4 tables, submitted revision to
IEEE T-PAM