Accelerated Stimulated Raman Projection Tomography by Sparse Reconstruction From Sparse-View Data

Chen, Xueli; Zhu, Sencun; Wang, Huiyuan; Bao, Cuiping; Yang, Defu; Zhang, Chi; Lin, Peng; Cheng, Ji‐Xin; Zhan, Yonghua; Liang, Jimin; Tian, Jie

doi:10.1109/tbme.2019.2935301

Cited by 7 publications

(2 citation statements)

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“…A further 1,818 images from 1,202 patients were selected from the remaining 2,601 images, according to image quality, and used to test our training results. The experimental dataset, collected by our homebuilt OPT system, had been acquired and published in a previous study (14). Full-angle projection images of Drosophilia and Arabidopsis silique samples were collected and downsampled to obtain sparsely sampled sinusoidal signals.…”

Section: Dataset and Pre-processingmentioning

confidence: 99%

“…For example, using analytic reconstruction algorithms such as the filtered back-projection (FBP) algorithm, no fewer than 360 projection images within a full-angle, or 180 projection images within a semicircleangle need to be collected to obtain reasonable imaging results (11)(12)(13). Furthermore, the higher the number of projection images used, the higher the quality and accuracy of the reconstructed images will be (14). However, this processing has serious drawbacks as it involves long dataacquisition time, leading to increased sample damage from the accumulation of light dose and a high degree of sample fixation (15).…”

Section: Introductionmentioning

confidence: 99%

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Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography

Wang¹,

Wang²,

Xie³

et al. 2022

Quant Imaging Med Surg

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Background: Projection tomography (PT) is a very important and valuable method for fast volumetric imaging with isotropic spatial resolution. Sparse-view or limited-angle reconstruction-based PT can greatly reduce data acquisition time, lower radiation doses, and simplify sample fixation modes. However, few techniques can currently achieve image reconstruction based on few-view projection data, which is especially important for in vivo PT in living organisms.Methods: A 2-stage deep learning network (TSDLN)-based framework was proposed for parallel-beam PT reconstructions using few-view projections. The framework is composed of a reconstruction network (R-net) and a correction network (C-net). The R-net is a generative adversarial network (GAN) used to complete image information with direct back-projection (BP) of a sparse signal, bringing the reconstructed image close to reconstruction results obtained from fully projected data. The C-net is a U-net array that denoises the compensation result to obtain a high-quality reconstructed image. Results:The accuracy and feasibility of the proposed TSDLN-based framework in few-view projectionbased reconstruction were first evaluated with simulations, using images from the DeepLesion public dataset. The framework exhibited better reconstruction performance than traditional analytic reconstruction algorithms and iterative algorithms, especially in cases using sparse-view projection images. For example, with as few as two projections, the TSDLN-based framework reconstructed high-quality images very close to the original image, with structural similarities greater than 0.8. By using previously acquired optical PT (OPT) data in the TSDLN-based framework trained on computed tomography (CT) data, we further exemplified the migration capabilities of the TSDLN-based framework. The results showed that when the number of projections was reduced to 5, the contours and distribution information of the samples in question could still be seen in the reconstructed images. Conclusions:The simulations and experimental results showed that the TSDLN-based framework has strong reconstruction abilities using few-view projection images, and has great potential in the application of in vivo PT.

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Section: Dataset and Pre-processingmentioning

confidence: 99%