Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network

Yang, Honggang; Liang, Kaichao; Kang, Kejun; Xing, Yuxiang

doi:10.1007/s41365-019-0581-7

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…The use of DL techniques for CT reconstruction has predominantly involved sparse‐view and low‐dose acquisitions as there is no ideal tomographic ground truth for learning strategies 105–110 . Iterative reconstruction (IR) applications utilizing DL can be categorized into “plug‐and‐play” and “unrolling” methods 109 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

et al. 2022

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The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone‐beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings—with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state‐of‐the‐art methods utilized in radiation oncology.

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Section: Discussionmentioning

confidence: 99%

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

et al. 2022

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“…For noise suppression, algorithms of the following classes are used: based on statistical analysis, nonlinear filters, iterative optimization algorithms, and neural networks. With noisy data, neural networks can be as a noise reduction operation after reconstruction [3,4] (post-processing), as a noise reduction operation on a set of projections (pre-processing) [5], and as a full-reconstruction operator [6,7]. To solve these problems various neural network architectures are used, for example, convolutional neural networks [8], neural networks operating in wavelet space [9] (post-processing noise suppression), networks operating both in the reconstruction space and measured data space [7], generative neural networks (post-processing noise reduction) [10].…”

Section: Introductionmentioning

confidence: 99%

Neural network regularization in the problem of few-view computed tomography

et al. 2022

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The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.

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“…And it demonstrated that two CNN in both projection domain and image domain perform better than image domain CNN only. For 3D problem, [17] used U-Net in dual-domain to solve the low-dose problem in cone-beam CT. It proposed a slice-wise reconstruction method which outperformed analytical reconstruction methods.…”

Section: Introductionmentioning

confidence: 99%

“…For this kind of problem, analytical reconstruction methods naturally result in severe artifacts and model-based iterative reconstruction could be used to cope with the ill-condition of this problem, but a huge computational cost is unavoidable. Based on the former research [17] of our group, we propose a reconstruction method using cascaded CNN learning both data transform in projection domain and error reduction in image domain. Our method not only shows an encouraging result in test datasets but also performs robustly 3 in generalization.…”

Section: Introductionmentioning

confidence: 99%

A deep learning reconstruction method for sparsely spaced multidetector helical CT using cascaded dual-domain CNN

Gao

Zhang

et al. 2020

Medical Imaging 2020: Physics of Medical Imaging

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Helical CT has been widely used in clinical diagnosis. Sparsely spaced multidetector in z direction can increase the coverage of the detector provided limited detector rows. It can speed up volumetric CT scan, lower the radiation dose and reduce motion artifacts. However, it leads to insufficient data for reconstruction. That means reconstructions from general analytical methods will have severe artifacts. Iterative reconstruction methods might be able to deal with this situation but with the cost of huge computational load. In this work, we propose a cascaded dual-domain deep learning method that completes both data transformation in projection domain and error reduction in image domain. First, a convolutional neural network (CNN) in projection domain is constructed to estimate missing helical projection data and converting helical projection data to 2D fanbeam projection data. This step is to suppress helical artifacts and reduce the following computational cost. Then, an analytical linear operator is followed to transfer the data from projection domain to image domain. Finally, an image domain CNN is added to improve image quality further. These three steps work as an entirety and can be trained end to end. The overall network is trained using a simulated lung CT dataset with Poisson noise from 25 patients. We evaluate the trained network on another three patients and obtain very encouraging results with both visual examination and quantitative comparison. The resulting RRMSE is 6.56% and the SSIM is 99.60%. In addition, we test the trained network on the lung CT dataset with different noise level and a new dental CT dataset to demonstrate the generalization and robustness of our method.

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Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network

Cited by 19 publications

References 27 publications

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

Neural network regularization in the problem of few-view computed tomography

A deep learning reconstruction method for sparsely spaced multidetector helical CT using cascaded dual-domain CNN

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