A New Method of Image Reconstruction for PET Using a Combined Regularization Algorithm

Boudjelal, Abdelwahhab; Moataz, Abderrahim El; Messali, Zoubeida

doi:10.1007/978-3-030-51935-3_19

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Based on the above understanding of past works [20][21][22][23][24], this paper proposes a novel scheme based on iterative filtering for computing the MLEM algorithm for ECT image reconstruction from noisy projections, namely a filtered MLEM. More precisely, we include an additional Beltrami [25][26][27] filtering step at each iteration of the MLEM to reduce noise and unwanted artifacts while preserving the edge information.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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The implementation of emission-computed tomography (ECT), including positron emission tomography and single-photon emission-computed tomography, has been an important research topic in recent years and is of significant and practical importance. However, the slow rate of convergence and the computational complexity have severely impeded the efficient implementation of iterative reconstruction. By combining the maximum-likelihood expectation maximization (MLEM) iteratively along with the Beltrami filter, this paper proposes a new approach to reformulate the MLEM algorithm. Beltrami filtering is applied to an image obtained using the MLEM algorithm for each iteration. The role of Beltrami filtering is to remove mainly out-of-focus slice blurs, which are artifacts present in most existing images. To improve the quality of an image reconstructed using MLEM, the Beltrami filter employs similar structures, which in turn reduce the number of errors in the reconstructed image. Numerical image reconstruction tomography experiments have demonstrated the performance capability of the proposed algorithm in terms of an increase in signal-to-noise ratio (SNR) and the recovery of fine details that can be hidden in the data. The SNR and visual inspections of the reconstructed images are significantly improved compared to those of a standard MLEM. We conclude that the proposed algorithm provides an edge-preserving image reconstruction and substantially suppress noise and edge artifacts.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Various physical model-based algorithms have been proposed in the last two decades to provide accurate tomographic reconstructions from low-count PET, and SPECT sinograms [6][7][8][9][10][11]. These techniques use a forward imaging model linking the tomographic image to the sinogram and incorporate prior knowledge on the solution to penalize the image variance or, equivalently, increase the image CNR.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Reconstruction Technique based on Newton's Method for Emission Tomography

Koutsantonis¹,

Carneiro²,

Kieffer³

et al. 2021

Preprint

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In this work, we present the Deep Newton Reconstruction Network (DNR-Net), a hybrid data-driven reconstruction technique for emission tomography inspired by Newton's method, a well-known iterative optimization algorithm. The DNR-Net employs prior information about the tomographic problem provided by the projection operator while utilizing deep learning approaches to a) imitate Newton's method by approximating the Newton descent direction and b) provide data-driven regularisation. We demonstrate that DNR-Net is capable of providing high-quality image reconstructions using data from SPECT phantom simulations by applying it to reconstruct images from noisy sinograms, each one containing 24 projections. The Structural Similarity Index (SSIM) and the Contrast-to-Noise ratio (CNR) were used to quantify the image quality. We also compare our results to those obtained by the OSEM method. According to the quantitative results, the DNR-Net produces reconstructions comparable to the ones produced by OSEM while featuring higher contrast and less noise.

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A Data-Driven Reconstruction Technique based on Newton’s Method for Emission Tomography

Koutsantonis

Carneiro

Kieffer

et al. 2021

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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A New Method of Image Reconstruction for PET Using a Combined Regularization Algorithm

Cited by 4 publications

References 21 publications

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

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

A Data-Driven Reconstruction Technique based on Newton's Method for Emission Tomography

A Data-Driven Reconstruction Technique based on Newton’s Method for Emission Tomography

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