Technical Note: Emission expectation maximization look‐alike algorithms for x‐ray<scp>CT</scp>and other applications

Zeng, G.L.

doi:10.1002/mp.13077

Cited by 7 publications

(12 citation statements)

References 19 publications

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“…The derivation of the EM-lookalike algorithms in ref. [10] was based on the noise variance model, unlike the conventional approach based on a random variable distribution function. Our derivation only considered two items: (1) the noise variance in the projections and (2) the non-negativity constraint for the image.…”

Section: Modification Of Iterative Green's Osl Algorithmmentioning

confidence: 99%

“…This study builds on ref. [10], by considering a new energy function V and forcing its gradient U to zero. This point can be intuitively appreciated from the additive form algorithm (4).…”

Section: Modification Of Iterative Green's Osl Algorithmmentioning

confidence: 99%

“…In this case, noise weighting should not be utilized in the image reconstruction algorithm. The ML-EM lookalike algorithm for this hypothetical case is given as [10].…”

Section: Modified Algorithm For No Weightingmentioning

confidence: 99%

“…Green's algorithm is a maximum a posterior (MAP) algorithm, using image-domain constraints for emission tomography. Other MAP algorithms exist [10][11][12][13][14][15]. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…Most recently, we developed a family of emission-EMlookalike algorithms [10]. These were iterative algorithms in the form of multiplicative image updating, which intrinsically enforced image non-negativity.…”

Section: Introductionmentioning

confidence: 99%

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Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

Zeng

2019

Vis. Comput. Ind. Biomed. Art

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We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization (ML-EM) algorithm. In this study, we extend these algorithms to Bayesian algorithms. The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme. The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor, which contains the Bayesian information. One of the extended algorithms can be applied to emission tomography and another to transmission tomography. Computer simulations are performed and compared with the corresponding un-extended algorithms. The total-variation norm is employed as the Bayesian constraint in the computer simulations. The newly developed algorithms demonstrate a stable performance. A simple Bayesian algorithm can be derived for any noise variance function. The proposed algorithms have properties such as multiplicative updating, non-negativity, faster convergence rates for bright objects, and ease of implementation. Our algorithms are inspired by Green's one-steplate algorithm. If written in additive-update form, Green's algorithm has a step size determined by the future image value, which is an undesirable feature that our algorithms do not have.

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Section: Modification Of Iterative Green's Osl Algorithmmentioning

confidence: 99%

“…This study builds on ref. [10], by considering a new energy function V and forcing its gradient U to zero. This point can be intuitively appreciated from the additive form algorithm (4).…”

Section: Modification Of Iterative Green's Osl Algorithmmentioning

confidence: 99%

“…In this case, noise weighting should not be utilized in the image reconstruction algorithm. The ML-EM lookalike algorithm for this hypothetical case is given as [10].…”

Section: Modified Algorithm For No Weightingmentioning

confidence: 99%

“…Green's algorithm is a maximum a posterior (MAP) algorithm, using image-domain constraints for emission tomography. Other MAP algorithms exist [10][11][12][13][14][15]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

Zeng

2019

Vis. Comput. Ind. Biomed. Art

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Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia

Liu

Tang

2022

Computational and Mathematical Methods in Medicine

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The objective of this study was to explore the accuracy of low-dosage computed tomography (CT) images based on the expectation maximization algorithm denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia. 120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group. The low-dosage CT images of patients in the control group were not processed (nonalgorithm group), and the low-dosage CT images of patients in the observation group were denoised using the EM algorithm (algorithm group). In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis. The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients. The results showed that the peak signal-to-noise ratio (PSNR) (15.9 dB) of the EM algorithm was higher than the regularized adaptive matching pursuit (RAMP) algorithm (1.69 dB) and the mean filter (4.3 dB) ( P < 0.05 ). The time consumption of EM algorithm (21 s) was shorter than that of PWLS algorithm (34 s) and MS-PWLS algorithm (39 s) ( P < 0.05 ). The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group ( P < 0.05 ), which were 66.67% vs. 90%, 60% vs. 88.89%, 71.42% vs. 100%, and 60% vs. 88.89%, respectively. The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (ADPKD) (56.77%) was much higher than that of the other diseases ( P < 0.05 ). After denoising by the EM algorithm, low-dosage CT image could improve the diagnostic accuracy of several types of renal dysplasia except ADPKD, showing certain clinical application value. In addition, ADPKD was easy to cause hypertension.

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Image Features of Dynamic Enhanced Computed Tomography Scanning Combined with Digestive Endoscopy in the Treatment of Gastroesophageal Varices and Nursing of Esophagogastric Gastric Varices Bleeding

Ding¹

2022

Computational and Mathematical Methods in Medicine

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The objective of this study was to investigate the application of dynamic contrast-enhanced CT images in the nursing of patients with gastroesophageal varices (GOV) treated by digestive endoscopy and its role in relieving bleeding symptoms. A total of 60 patients with liver cirrhosis and GOV were selected as the research objects. According to whether CT was used to evaluate the position of tissue adhesion embolism, the patients were divided into the control group (24 cases) and the observation group (36 cases). The treatment effect and bleeding situation of patients in the two groups were analyzed and compared. The results showed that the main portal vein pressure ( 17.24 ± 1.02 cmH2O), liver function recovery effect ( 2.84 ± 0.45 points), and total effective rate (100%) in observation group were better than those in control group ( 9.70 ± 1.22 cmH2O, 0.95 ± 0.72 points, and 79.17%, respectively) ( P < 0.05 ). In addition, the bleeding rate in observation group (0%) was significantly lower than that in control group (16.67%) ( P < 0.05 ). In conclusion, dynamically enhanced CT scan images combined with digestive endoscopy can help improve the therapeutic effect of GOV and reduce postoperative bleeding, which was worthy of clinical application and promotion.

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Technical Note: Emission expectation maximization look‐alike algorithms for x‐rayCTand other applications

Abstract: For any noise variance function, an emission-EM-look-alike algorithm can be derived. This algorithm preserves many properties of the emission EM algorithm such as multiplicative update, non-negativity, faster convergence rate for the bright objects, and ease of implementation.

Cited by 7 publications

References 19 publications

Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia

Image Features of Dynamic Enhanced Computed Tomography Scanning Combined with Digestive Endoscopy in the Treatment of Gastroesophageal Varices and Nursing of Esophagogastric Gastric Varices Bleeding

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