Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction

Liang, Kaichao; Yang, Hongkai; Xing, Yuxiang

doi:10.48550/arxiv.1804.04289

Cited by 10 publications

(12 citation statements)

References 17 publications

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“…The benefits of including the projections within the network were demonstrated in Ref. [6], especially for sparse-view computed tomography (CT) data. One can instead compute the FBP from the low-dose projections to feed a network that removes noise and artifacts, which remain in the obtained image: Different structures can be chosen to generate the desired reconstruction, such as U-NET 7 in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Impact of the training loss in deep learning–based CT reconstruction of bone microarchitecture

et al. 2022

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Purpose Computed tomography (CT) is a technique of choice to image bone structure at different scales. Methods to enhance the quality of degraded reconstructions obtained from low‐dose CT data have shown impressive results recently, especially in the realm of supervised deep learning. As the choice of the loss function affects the reconstruction quality, it is necessary to focus on the way neural networks evaluate the correspondence between predicted and target images during the training stage. This is even more true in the case of bone microarchitecture imaging at high spatial resolution where both the quantitative analysis of bone mineral density (BMD) and bone microstructure is essential for assessing diseases such as osteoporosis. Our aim is thus to evaluate the quality of reconstruction on key metrics for diagnosis depending on the loss function that has been used for training the neural network. Methods We compare and analyze volumes that are reconstructed with neural networks trained with pixelwise, structural, and adversarial loss functions or with a combination of them. We perform realistic simulations of various low‐dose acquisitions of bone microarchitecture. Our comparative study is performed with metrics that have an interest regarding the diagnosis of bone diseases. We therefore focus on bone‐specific metrics such as bone volume and the total volume (BV and TV), resolution, connectivity assessed with the Euler number, and quantitative analysis of BMD to evaluate the quality of reconstruction obtained with networks trained with the different loss functions. Results We find that using L1$L_1$ norm as the pixelwise loss is the best choice compared to L2$L_2$ or no pixelwise loss since it improves resolution without deteriorating other metrics. Visual Geometry Group (VGG) perceptual loss, especially when combined with an adversarial loss, allows to better retrieve topological and morphological parameters of bone microarchitecture compared to Structural SIMilarity (SSIM) index. This however leads to a decreased resolution performance. The adversarial loss enhances the reconstruction performance in terms of BMD distribution accuracy. Conclusions In order to retrieve the quantitative and structural characteristics of bone microarchitecture that are essential for postreconstruction diagnosis, our results suggest to use L1$L_1$ norm as part of the loss function. Then, trade‐offs should be made depending on the application: VGG perceptual loss improves accuracy in terms of connectivity at the cost of a deteriorated resolution, and adversarial losses help better retrieve BMD distribution while significantly increasing the training time.

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Section: Introductionmentioning

confidence: 99%

“…6 trainable parameters, and VGG loss implies 20 × 10 6 extra TA B L E 2 Optimal hyperparameters (HPs) for each method. These HPs have been optimized on a validation set consisting of 20% of the slices obtained from the 10 training volumes.…”

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confidence: 99%

Impact of the training loss in deep learning–based CT reconstruction of bone microarchitecture

et al. 2022

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“…Fewer projection data are acquired with uniform spacing over complete angular range. Similar approaches have been explored for this sparse-view CT: image post-processing [33][34][35][36], projection data completion [37][38][39], combination of projection data completion and image post-processing [40], and end-to-end learning [41,42].…”

Section: Related Workmentioning

confidence: 99%

Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium

Ghani¹,

Karl²

2019

Preprint

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Computed Tomography (CT) is a non-invasive imaging modality with applications ranging from healthcare to security. It reconstructs cross-sectional images of an object using a collection of projection data collected at different angles. Conventional methods, such as FBP, require that the projection data be uniformly acquired over the complete angular range. In some applications, it is not possible to acquire such data. Security is one such domain where non-rotational scanning configurations are being developed which violate the complete data assumption. Conventional methods produce images from such data that are filled with artifacts. The recent success of deep learning (DL) methods has inspired researchers to post-process these artifact laden images using deep neural networks (DNNs). This approach has seen limited success on real CT problems. Another approach has been to pre-process the incomplete data using DNNs aiming to avoid the creation of artifacts altogether. Due to imperfections in the learning process, this approach can still leave perceptible residual artifacts. In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework. Specifically, we use conditional generative adversarial networks (cGANs) in both the data and the image domain for enhanced performance and efficient computation and combine them through a consensus process. We demonstrate the effectiveness of our approach on a real security CT dataset for a challenging 90 0 limited-angle problem. The same framework can be applied to other limited data problems arising in applications such as electron microscopy, nondestructive evaluation, and medical imaging.

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“…Since neural networks are capable of predicting unknown data in the Radon and image domains, a natural idea is to combine these two domains [48,49,34,50,51,52] to acquire better restoration results. Specifically, it first complements the Radon data, and then remove the residual artifacts and noises on images converted from the full-view Radon data.…”

Section: Introductionmentioning

confidence: 99%

“…In 2018, Zhao et al proposed SVGAN [34], an artifacts reduction method for low-dose and sparse-view CT via a single model trained by GAN. In 2019, Liang et al [49] proposed a comprehensive network combining projection and image domains. The projection estimation network is based on Res-CNN structure, and the image domain network takes the advantage of U-Net.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

Liu¹,

Deng²,

Sun³

et al. 2021

Preprint

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Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations. However, its severe imaging noise and streaking artifacts turn out to be a major issue in the low dose protocol. In this paper, we propose a dual-domain deep learning-based method that breaks through the limitations of currently prevailing algorithms that merely process single image slices. Since the scanned object usually contains a high degree of spatial continuity, the obtained consecutive imaging slices embody rich information that is largely unexplored. Therefore, we establish a cascade model named LS-AAE which aims to tackle the above problem. In addition, in order to adapt to the social trend of lightweight medical care, our model adopts the inverted residual with linear bottleneck in the module design to make it mobile and lightweight (reduce model parameters to one-eighth of its original) without sacrificing its performance. In our experiments, sparse sampling is conducted at intervals of 4°, 8°and 16°, which appears to be a challenging sparsity that few scholars have attempted. Nevertheless, our method still exhibits its robustness and achieves the state-of-the-art performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while ensuring high model mobility. Particularly, it still exceeds other current methods when the sampling rate is one-fourth of them, thereby demonstrating its remarkable superiority.

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Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction

Cited by 10 publications

References 17 publications

Impact of the training loss in deep learning–based CT reconstruction of bone microarchitecture

Impact of the training loss in deep learning–based CT reconstruction of bone microarchitecture

Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium

A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

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