18 research outputs found
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
In "extreme" computational imaging that collects extremely undersampled or
noisy measurements, obtaining an accurate image within a reasonable computing
time is challenging. Incorporating image mapping convolutional neural networks
(CNN) into iterative image recovery has great potential to resolve this issue.
This paper 1) incorporates image mapping CNN using identical convolutional
kernels in both encoders and decoders into a block coordinate descent (BCD)
signal recovery method and 2) applies alternating direction method of
multipliers to train the aforementioned image mapping CNN. We refer to the
proposed recurrent network as BCD-Net using identical encoding-decoding CNN
structures. Numerical experiments show that, for a) denoising low
signal-to-noise-ratio images and b) extremely undersampled magnetic resonance
imaging, the proposed BCD-Net achieves significantly more accurate image
recovery, compared to BCD-Net using distinct encoding-decoding structures
and/or the conventional image recovery model using both wavelets and total
variation.Comment: 5 pages, 3 figure
Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference
The photon-counting detector (PCD) based spectral computed tomography
attracts much more attentions since it has the capability to provide more
accurate identification and quantitative analysis for biomedical materials. The
limited number of photons within narrow energy-bin leads to low signal-noise
ratio data. The existing supervised deep reconstruction networks for CT
reconstruction are difficult to address these challenges. In this paper, we
propose an iterative deep reconstruction network to synergize model and data
priors into a unified framework, named as Spectral2Spectral. Our
Spectral2Spectral employs an unsupervised deep training strategy to obtain
high-quality images from noisy data with an end-to-end fashion. The structural
similarity prior within image-spectral domain is refined as a regularization
term to further constrain the network training. The weights of neural network
are automatically updated to capture image features and structures with
iterative process. Three large-scale preclinical datasets experiments
demonstrate that the Spectral2spectral reconstruct better image quality than
other state-of-the-art methods
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised
training of (hierarchical) convolutional sparsifying operators or autoencoders
from large datasets. One can use many training images for CAOL, but a precise
understanding of the impact of doing so has remained an open question. This
paper presents a series of results that lend insight into the impact of dataset
size on the filter update in CAOL. The first result is a general deterministic
bound on errors in the estimated filters, and is followed by a bound on the
expected errors as the number of training samples increases. The second result
provides a high probability analogue. The bounds depend on properties of the
training data, and we investigate their empirical values with real data. Taken
together, these results provide evidence for the potential benefit of using
more training data in CAOL.Comment: 5 pages, 2 figure
Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction
Although sparse-view computed tomography (CT) has significantly reduced
radiation dose, it also introduces severe artifacts which degrade the image
quality. In recent years, deep learning-based methods for inverse problems have
made remarkable progress and have become increasingly popular in CT
reconstruction. However, most of these methods suffer several limitations:
dependence on high-quality training data, weak interpretability, etc. In this
study, we propose a fully unsupervised framework called Deep Radon Prior (DRP),
inspired by Deep Image Prior (DIP), to address the aforementioned limitations.
DRP introduces a neural network as an implicit prior into the iterative method,
thereby realizing cross-domain gradient feedback. During the reconstruction
process, the neural network is progressively optimized in multiple stages to
narrow the solution space in radon domain for the under-constrained imaging
protocol, and the convergence of the proposed method has been discussed in this
work. Compared with the popular pre-trained method, the proposed framework
requires no dataset and exhibits superior interpretability and generalization
ability. The experimental results demonstrate that the proposed method can
generate detailed images while effectively suppressing image
artifacts.Meanwhile, DRP achieves comparable or better performance than the
supervised methods.Comment: 11 pages, 12 figures, Journal pape
Neural networks-based regularization for large-scale medical image reconstruction
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude