Full-Spectrum-Knowledge-Aware Tensor Model for Energy-Resolved CT Iterative Reconstruction

Yao, Lisha; Ge, Yongshuai; Sui, Li; Xie, Qi; Zhang, Hao; Bian, Zhaoying; Zhao, Qian; Li, Yuanqing; Zhao, Xu; Meng, Deyu; Ma, Jianhua

doi:10.1109/tmi.2020.2976692

Cited by 17 publications

(8 citation statements)

References 43 publications

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“…The observation that most of the image pixels tend to be spanned by a small number of basis materials, whereas k-edge elements are often confined to localized regions, can be exploited for noise reduction as shown by Gao et al (2011). There are also algorithms exploiting the low dimensionality of spectral CT images in combination with SPICCS (Zeng et al 2020) or patch-based denoising (Niu et al 2018a). Finally, a new and promising research direction is the application of convolutional neural networks for bin image denoising (Clark and Badea 2020).…”

Section: Energy Bin Imagesmentioning

confidence: 99%

Photon-counting x-ray detectors for CT

2021

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The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

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Section: Energy Bin Imagesmentioning

confidence: 99%

Photon-counting x-ray detectors for CT

2021

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“…This kind of method is difficult to be implemented straightforwardly in this study due to the spatial mismatch between the dual-energy projections. The second one is the image-domain decomposition method [18][19][20] , in which the CT images reconstructed using the filtered back-projection (FBP) or iterative algorithm are decomposed into different bases. Despite that the spatial mismatch of projections can be compensated after reconstruction, however, the image-domain method is not able to generate CT bases with the desired high spatial resolution.…”

Section: B Materials Decomposition Algorithmmentioning

confidence: 99%

Super resolution dual-energy cone-beam CT imaging with dual-layer flat-panel detector

Su¹,

Zhu²,

Zhang³

et al. 2022

Preprint

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For medical cone-beam computed tomography (CBCT) imaging, the native receptor array of the flat-panel detector (FPD) is usually binned into a reduced matrix size. By doing so, the signal readout speed can be increased by over 4-9 times at the expense of sacrificing the spatial resolution by at least 50%-67%. Clearly, such tradition poses a main bottleneck in generating high spatial resolution and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, an 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied to replace the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high signal readout speed and high spatial resolution CBCT imaging with FPD. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from the dual-energy CBCT projections containing spatial sub-pixel shifts. Experiments based on the single-layer FPD and DL-FPD are performed with physical phantoms and biological specimen to validate this newly developed suRi method.The synthesized monochromatic CT imaging results demonstrate that suRi can significantly improve the spatial image resolution by 46.15%. We believe the developed suRi method would be capable to greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

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“…8 Recently, Zeng et al proposed a full-spectrum-knowledge-aware tensor by imposing the global correlation, piecewise smooth and latent full-spectrum properties of PCCT images. 9 These methods have been shown great potential in preserving image details and suppressing noise. However, there remain some challenges in practice.…”

Section: Introductionmentioning

confidence: 99%

Full-spectrum-knowledge-aware unsupervised network for photon-counting CT imaging

Duan²,

Bian³

et al. 2022

7th International Conference on Image Formation in X-Ray Computed Tomography

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Deep learning (DL) based methods have been widely adopted in computed tomography (CT) field. And they also show a great potential in photon-counting CT (PCCT) imaging field. They usually require a large quantity of paired data to train networks. However, it is time-consuming and expensive to collect such large-scale PCCT dataset. In addition, lots of energy-integrating detector (EID) data are not yet included in the DL-based PCCT reconstruction network training. In this work, to address the issue of limited PCCT data and take advantage of labeled EID data, we propose a novel unsupervised full-spectrum-knowledge-aware DL-based network (FSANet), which contains supervised and unsupervised networks, to produce high-quality PCCT images. Specifically, the supervised network is trained based on paired EID dataset and serves as the prior knowledge to regularize the unsupervised PCCT network training. Moreover, a data-fidelity term for characterizing the PCCT image characteristics is constructed as a self-supervised term. Finally, we train the PCCT network with the prior knowledge and self-supervised terms following an unsupervised learning strategy. Numerical studies on synthesized clinical data are conducted to validate and evaluate the performance of the presented FSANet method, qualitatively and quantitatively. The experimental results demonstrate that presented FSANet method significantly improves the PCCT image quality in the case of limited photon counts.

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Full-Spectrum-Knowledge-Aware Tensor Model for Energy-Resolved CT Iterative Reconstruction

Cited by 17 publications

References 43 publications

Photon-counting x-ray detectors for CT

Photon-counting x-ray detectors for CT

Super resolution dual-energy cone-beam CT imaging with dual-layer flat-panel detector

Full-spectrum-knowledge-aware unsupervised network for photon-counting CT imaging

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