Some Investigations on Robustness of Deep Learning in Limited Angle Tomography

Huang, Yixing; Würfl, Tobias; Breininger, Katharina; Liu, Ling; Lauritsch, Guenter; Maier, Andreas

doi:10.1007/978-3-030-00928-1_17

Cited by 110 publications

(74 citation statements)

References 9 publications

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“…Yet, they may suffer from a "new kind" of deep learning artifacts. In particular, the work by Huang et al [108] show these effects in great detail. Both precision learning as well as Bayesian approaches seem well suited to tackle the problem in the future.…”

Section: Discussionmentioning

confidence: 93%

“…As noted earlier, all networks are prone to adversarial attacks. Huang et al demonstrate this [108] in their work, showing that already incorrect noise modelling may distort the entire image. Yet, the networks reconstruct visually pleasing results and artifacts cannot be as easily identified as in classical methods.…”

Section: Image Reconstructionmentioning

confidence: 84%

“…Learning operators completely data-driven carries the risk that undesired effects may occur [108], as is shown in Fig. 8.…”

Section: Image Reconstructionmentioning

confidence: 99%

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A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

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395

224

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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

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Section: Discussionmentioning

confidence: 93%

Section: Image Reconstructionmentioning

confidence: 84%

See 1 more Smart Citation

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

Self Cite

395

224

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show abstract

“…In this work, we choose the third category for limited angle tomography. In [19], the U-Net learns artifacts from streaky images in the image domain only. Reconstruction images obtained by such image-to-image prediction are very likely not consitent to measured data as the prediction does not have any direct connection to measured data.…”

Section: Introductionmentioning

confidence: 99%

“…As displayed in Fig. 1, the same U-Net architecture as that in [19] is used for artifact reduction in limited angle tomography, which is modified from [22] and [18]. In this work, the input images are Ram-Lak-kernel-based FBP reconstructions from limited angle data, while the output images are artifact images.…”

Section: Introductionmentioning

confidence: 99%

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

Huang

Preuhs

Lauritsch

et al. 2019

Machine Learning for Medical Image Reconstruction

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Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120 • cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.

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A neural network with encoded visible edge prior for limited‐angle computed tomography reconstruction

Zhang

Zhao

et al. 2021

Medical Physics

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Purpose Limited‐angle computed tomography is a challenging but important task in certain medical and industrial applications for nondestructive testing. The limited‐angle reconstruction problem is highly ill‐posed and conventional reconstruction algorithms would introduce heavy artifacts. Various models and methods have been proposed to improve the quality of reconstructions by introducing different priors regarding to the projection data or ideal images. However, the assumed priors might not be practically applicable to all limited‐angle reconstruction problems. Convolutional neural network (CNN) exhibits great promise in the modeling of data coupling and has recently become an important technique in medical imaging applications. Although existing CNN methods have demonstrated promising results, their robustness is still a concern. In this paper, in light of the theory of visible and invisible boundaries, we propose an alternating edge‐preserving diffusion and smoothing neural network (AEDSNN) for limited‐angle reconstruction that builds the visible boundaries as priors into its structure. The proposed method generalizes the alternating edge‐preserving diffusion and smoothing (AEDS) method for limited‐angle reconstruction developed in the literature by replacing its regularization terms by CNNs, by which the piecewise constant assumption assumed by AEDS is effectively relaxed. Methods The AEDSNN is derived by unrolling the AEDS algorithm. AEDSNN consists of several blocks, and each block corresponds to one iteration of the AEDS algorithm. In each iteration of the AEDS algorithm, three subproblems are sequentially solved. So, each block of AEDSNN possesses three main layers: data matching layer, x‐direction regularization layer for visible edges diffusion, and y‐direction regularization layer for artifacts suppressing. The data matching layer is implemented by conventional ordered‐subset simultaneous algebraic reconstruction technique (OS‐SART) reconstruction algorithm, while the two regularization layers are modeled by CNNs for more intelligent and better encoding of priors regarding to the reconstructed images. To further strength the visible edge prior, the attention mechanism and the pooling layers are incorporated into AEDSNN to facilitate the procedure of edge‐preserving diffusion from visible edges. Results We have evaluated the performance of AEDSNN by comparing it with popular algorithms for limited‐angle reconstruction. Experiments on the medical dataset show that the proposed AEDSNN effectively breaks through the piecewise constant assumption usually assumed by conventional reconstruction algorithms, and works much better for piecewise smooth images with nonsharp edges. Experiments on the printed circuit board (PCB) dataset show that AEDSNN can better encode and utilize the visible edge prior, and its reconstructions are consistently better compared to the competing algorithms. Conclusions A deep‐learning approach for limited‐angle reconstruction is proposed in this paper, which significantly ...

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Some Investigations on Robustness of Deep Learning in Limited Angle Tomography

Cited by 110 publications

References 9 publications

A gentle introduction to deep learning in medical image processing

A gentle introduction to deep learning in medical image processing

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

A neural network with encoded visible edge prior for limited‐angle computed tomography reconstruction

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