A Novel Iterative MLEM Image Reconstruction Algorithm Based on Beltrami Filter: Application to ECT Images

Boudjelal, Abdelwahhab; Elmoataz, Abderrahim; Attallah, Bilal; Messali, Zoubeida

doi:10.3390/tomography7030026

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…Traditional reconstruction algorithms include filtered back projection (FBP) and the iterative reconstruction algorithm. [5][6][7][8][9][10] Although the FBP algorithm is fast, it is based on an ideal reconstruction process that ignores the noise in the original data, resulting in dynamically reconstructed images that contain a large number of radiometric artifacts. [11,12] In contrast, the iterative algorithm uses a system model and regularization, which makes the quality of reconstructed images better than the FBP algorithm, but the computational complexity of each iteration process in the iterative algorithm is equivalent to one FBP algorithm, so the reconstruction speed of the iterative algorithm is usually much slower than that of the FBP algorithm.…”

Section: Introductionmentioning

confidence: 99%

FBP‐CNN: A Direct PET Image Reconstruction Network for Flow Visualization

Fang

Guo

Zhao

et al. 2023

Advcd Theory and Sims

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Dynamic positron emission tomography (PET) imaging has the potential to address technical challenges that persist in the visualization of optically inaccessible flow fields in integrated systems. However, traditional reconstruction algorithms are unable to reconstruct high-quality images from dynamic scan data. In this paper, a neural network structure that can reconstruct high-quality images directly using sinograms as input by combining the filtered back-projection (FBP) algorithm and denoising convolutional neural network (CNN) is proposed, which is named FBP-CNN. Computational fluid dynamics (CFD) software and the Monte Carlo simulation platform are used jointly to generate a dataset for the flow around the bluff body problem. The dataset is then used to train, validate, and test the FBP-CNN network, and the network after completing training is used to reconstruct the real projection data. The results show that FBP-CNN can reconstruct high-quality images from both simulated datasets and real projection data.

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Section: Introductionmentioning

confidence: 99%

FBP‐CNN: A Direct PET Image Reconstruction Network for Flow Visualization

Fang

Guo

Zhao

et al. 2023

Advcd Theory and Sims

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“…CT reconstruction methods can be broadly classified into three categories, i.e., sinogramdomain reconstruction, iterative reconstruction (IR) and image-domain reconstruction. Sinogram-domain methods perform denoising, the removal of artifacts and interpolation in sinogram data by utilizing traditional filtering algorithms [3][4][5], dictionary-based approaches [6] and deep learning-based methods [7]. Filtering algorithms have the advantages of their computation cost and reconstruction speed but fail to achieve satisfying performance when the raw data are severely lacking.…”

Section: Introductionmentioning

confidence: 99%

Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction

2021

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Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable performances compared to iterative reconstruction algorithms. However, the performance of these methods may severely deteriorate when the degradation strength of the test image is not consistent with that of the training dataset. In addition, these methods do not pay enough attention to the characteristics of different degradation levels, so solely extending the training dataset with multiple degraded images is also not effective. Although training plentiful models in terms of each degradation level can mitigate this problem, extensive parameter storage is involved. Accordingly, in this paper, we focused on sparse-view CT reconstruction for multiple degradation levels. We propose a single degradation-aware deep learning framework to predict clear CT images by understanding the disparity of degradation in both the frequency domain and image domain. The dual-domain procedure can perform particular operations at different degradation levels in frequency component recovery and spatial details reconstruction. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and visual results demonstrate that our method outperformed the classical deep learning-based reconstruction methods in terms of effectiveness and scalability.

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