Deep Back Projection for Sparse-View Ct Reconstruction

Ye, Dong Hye; Buzzard, Gregery T.; Ruby, Max; Bouman, Charles A.

doi:10.1109/globalsip.2018.8646669

Cited by 21 publications

(12 citation statements)

References 23 publications

(20 reference statements)

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“…Toward reconstructing CT images from one or two planar x-rays, prior efforts have been made to train deep neural networks using CT images and their digital forward projections, namely, DRRs. [82][83][84] The majority of the validations of these networks used DRRs (over which the developers have precise control and knowledge during the forward-projection process) instead of real x-ray images, not to mention real scout images. To our knowledge, the only prior work that performed evaluations using real x-ray radiography images, but not actual scout images, is Ref.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of three‐dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning

Montoya

Zhang

et al. 2022

Medical Physics

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Background: A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation coefficients. These patient models do not account for the detailed anatomical variations of human subjects, and thus, may limit the accuracy of intraview or organ-specific dose modulations in emerging CT technologies. Purpose: The purpose of this work was to show that 3D tomographic patient models can be generated from two-view scout images using deep learning strategies, and the reconstructed 3D patient models indeed enable accurate prescriptions of fluence-field modulated or organ-specific dose delivery in the subsequent CT scans. Methods: CT images and the corresponding two-view scout images were retrospectively collected from 4214 individual CT exams. The collected data were curated for the training of a deep neural network architecture termed ScoutCT-NET to generate 3D tomographic attenuation models from two-view scout images. The trained network was validated using a cohort of 55 136 images from 212 individual patients. To evaluate the accuracy of the reconstructed 3D patient models, radiation delivery plans were generated using ScoutCT-NET 3D patient models and compared with plans prescribed based on true CT images (gold standard) for both fluence-field-modulated CT and organ-specific CT. Radiation dose distributions were estimated using Monte Carlo simulations and were quantitatively evaluated using the Gamma analysis method. Modulated dose profiles were compared against state-of -the-art tube current modulation schemes. Impacts of ScoutCT-NET patient model-based dose modulation schemes on universal-purpose CT acquisitions and organ-specific acquisitions were also compared in terms of overall image appearance, noise magnitude, and noise uniformity. Results:The results demonstrate that (1) The end-to-end trained ScoutCT-NET can be used to generate 3D patient attenuation models and demonstrate empirical generalizability. (2) The 3D patient models can be used to accurately estimate the spatial distribution of radiation dose delivered by standard helical CTs prior to the actual CT acquisition;compared to the gold-standard dose distribution, 95.0% of the voxels in the ScoutCT-NET based dose maps have acceptable gamma values for 5 mm distance-to-agreement and 10% dose difference.(3) The 3D patient models also enabled accurate prescription of fluence-field modulated CT to generate a more uniform noise distribution across the patient

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Section: Discussionmentioning

confidence: 99%

Reconstruction of three‐dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning

Montoya

Zhang

et al. 2022

Medical Physics

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“…In other word, U x and V x is related to wavelet decomposition and synthesis. Using sinogram measurements as network inputs was proposed by [72]. For different angles of view, corresponding sinogram was expand to a image by back projection and these images were stacked to form a tensor.…”

Section: Neural Network As Image Projectionsmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

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Compressed sensing (CS) and its medical applications are active areas of research. In this paper, we review recent works using deep learning method to solve CS problem for images or medical imaging reconstruction including computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission tomography (PET). We propose a novel framework to unify traditional iterative algorithms and deep learning approaches. In short, we define two projection operators toward image prior and data consistency, respectively, and any reconstruction algorithm can be decomposed to the two parts. Though deep learning methods can be divided into several categories, they all satisfies the framework. We built the relationship between different reconstruction methods of deep learning, and connect them to traditional methods through the proposed framework. It also indicates that the key to solve CS problem and its medical applications is how to depict the image prior. Based on the framework, we analyze the current deep learning methods and point out some important directions of research in the future.

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“…Previously, the CNN has been successfully applied to implement speckle elimination [19,20], target classification [21,22], and recognition [23] in the field of SAR imaging. Besides, CNN-based fast computed tomography (CT) image construction has also been proposed to address the sparseview problem [24]. In this paper, we propose and demonstrate a CNN-based FBP radar imaging method, which is noise-resistant when the radar is operated in low SNR conditions.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

Sun

Zhang

2020

IEEE Access

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We propose and demonstrate a convolutional neural network (CNN)-based fast back projection (FBP) imaging method, which has noise-resistant capability in strong noise conditions. In this method, the desired high-resolution image is constructed from a low-resolution back projection (BP) image using a pre-trained CNN. Compared to the high-resolution imaging with basic BP algorithm, the proposed CNN-based FBP imaging has significantly reduced complexity, enabling a fast imaging speed. Meanwhile, by training the CNN using noiseless images as the desired output, the CNN-based FBP imaging is noiseresistant, which helps to obtain high-quality images in strong noise scenarios. Performance of this CNNbased FBP imaging method is investigated and compared with basic BP imaging and other methods through extensive numerical simulations. The results show that, using a CNN with optimized structure, the proposed method can greatly improve the imaging speed. Meanwhile, high-quality images with improved peak signal to noise ratios (PSNRs) are obtained in low signal-to-noise-ratio (SNR) conditions. This CNN-based FBP imaging method is expected to find applications where high-quality and fast radar imaging is required. INDEX TERMS Synthetic aperture radar, back projection algorithm, fast back projection imaging, convolutional neural network, high-resolution imaging.

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Deep Back Projection for Sparse-View Ct Reconstruction

Cited by 21 publications

References 23 publications

Reconstruction of three‐dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning

Reconstruction of three‐dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

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