Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

Wang, Jiaxi; Liang, Jun; Cheng, Jingye; Guo, Yumeng; Zeng, Li

doi:10.1371/journal.pone.0226963

Cited by 53 publications

(32 citation statements)

References 37 publications

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“…Comprehensive sequence analysis with relative synonymous codon usage bias demonstrated that SARS-CoV-2 may be derived from recombination between a bat coronavirus and another coronavirus from snakes (17). Additionally, mink have been suggested as a potential host of SARS-CoV-2 (18). Interspecies transmissions of viruses between animals and humans may result in unpredictable pathogenic potential and transmissible COVID-19 disease (19).…”

Section: Virologymentioning

confidence: 99%

Confronting the threat of SARS‑CoV‑2: Realities, challenges and therapeutic strategies (Review)

2020

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Section: Virologymentioning

confidence: 99%

Confronting the threat of SARS‑CoV‑2: Realities, challenges and therapeutic strategies (Review)

2020

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“…A disadvantage of iterative techniques is that the noise properties are non-linear and unpredictable across the field of view (FOV) as compared to linear noise in analytical methods. Several deep learning-based reconstructions have recently been introduced as supervised training using paired images and unsupervised training using unpaired images (28)(29)(30)(31)(32)(33)(34). Achieving satisfactory results requires sufficient training data.…”

Section: Introductionmentioning

confidence: 99%

Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography

et al. 2021

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Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.

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“…Image reconstruction has wide application in medicine [1,2], biology [3], X-ray crystallography [4] and low-light vision, among other technologies. These reconstructions generally involve solving an inverse problem and retrieving the phase from phase-less intensity measurements.…”

Section: Introductionmentioning

confidence: 99%

Toward simple, generalizable neural networks with universal training for low-SWaP hybrid vision

et al. 2021

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Speed, generalizability, and robustness are fundamental issues for building lightweight computational cameras. Here we demonstrate generalizable image reconstruction with the simplest of hybrid machine vision systems: linear optical preprocessors combined with nohidden-layer, "small-brain" neural networks. Surprisingly, such simple neural networks are capable of learning the image reconstruction from a range of coded diffraction patterns using two masks. We investigate the possibility of generalized or "universal training" with these small brains. Neural networks trained with sinusoidal or random patterns uniformly distribute errors around a reconstructed image, whereas models trained with a combination of sharp and curved shapes (the phase pattern of optical vortices) reconstruct edges more boldly. We illustrate variable convergence of these simple neural networks and relate learnability of an image to its singular value decomposition entropy (SVD-entropy) of the image. We also provide heuristic experimental results. With thresholding, we achieve robust reconstruction of various disjoint datasets. Our work is favorable for future real-time low-SWaP hybrid vision: we reconstruct images on a 15W laptop CPU with 15k fps: faster by a factor of 3 than previously reported results and 3 orders of magnitude faster than convolutional neural networks.

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Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

Cited by 53 publications

References 37 publications

Confronting the threat of SARS‑CoV‑2: Realities, challenges and therapeutic strategies (Review)

Confronting the threat of SARS‑CoV‑2: Realities, challenges and therapeutic strategies (Review)

Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography

Toward simple, generalizable neural networks with universal training for low-SWaP hybrid vision

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