99m-Tc MDP Bone Scan Image Enhancement using Pipeline Application of Dynamic Stochastic Resonance Algorithm and Block-Matching 3D Filter

Pandey, AnilKumar; Kaur, Gagandeep; Chaudhary, Jagrati; Hemrom, Angel; Jaleel, Jasim; Sharma, ParamDev; Patel, Chetan; Kumar, Rakesh

doi:10.4103/ijnm.ijnm_78_22

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Although not directly related to our study; studies have been conducted for reducing noise levels in planar [2,3,35–37], SPECT [4,38–42] and PET images [43,44]. These studies do not involve neural networks for noise reduction, however.…”

Section: Discussionmentioning

confidence: 96%

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Chaudhary

Phulia

Pandey

et al. 2023

Nuclear Medicine Communications

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Introduction A DnCNN for image denoising trained with natural images is available in MATLAB. For Tc-99m DMSA images, any loss of clinical details during the denoising process will have serious consequences since denoised image is to be used for diagnosis. The objective of the study was to find whether this pre-trained DnCNN can be used for denoising Tc-99m DMSA images and compare its performance with block matching 3D (BM3D) filter. Materials and methods Two hundred forty-two Tc-99m DMSA images were denoised using BM3D filter (at sigma = 5, 10, 15, 20, and 25) and DnCNN. The original and denoised images were reviewed by two nuclear medicine physicians and also assessed objectively using the image quality metrics: SSIM, FSIM, MultiSSIM, PIQE, Blur, GCF, and Brightness. Wilcoxon signed-rank test was applied to find the statistically significant difference between the value of image quality metrics of the denoised images and the corresponding original images. Results Nuclear medicine physicians observed no loss of clinical information in DnCNN denoised image and superior image quality compared to its original and BM3D denoised images. Edges/boundaries of the scar were found to be well preserved, and doubtful scar became obvious in the denoised image. Objective assessment also showed that the quality of DnCNN denoised images was significantly better than that of original images at P-value <0.0001. Conclusion The pre-trained DnCNN available with MATLAB Deep Learning Toolbox can be used for denoising Tc-99m DMSA images, and the performance of DnCNN was found to be superior in comparison with BM3D filter.

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Section: Discussionmentioning

confidence: 96%

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Chaudhary

Phulia

Pandey

et al. 2023

Nuclear Medicine Communications

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“…Benzi raised the concept of stochastic resonance (SR) in 1981 when studying the changes of the Earth’s ice ages [ 7 ]. After 40 years of development, SR theory has been widely used in fault diagnosis [ 8 ], optics [ 9 ], medicine [ 10 ], image denoising [ 11 ], and other fields, and has achieved many remarkable results. The SR algorithm makes use of the synergy generated by the joint excitation of nonlinear systems, input signals, and noise to make Brownian particles oscillate, improve the output signal-to-noise ratio, and effectively detect the measured signal, which is a typical method to enhance the measured signal.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

Huang

Zhang

2023

Sensors

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In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy–Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected.

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99m-Tc MDP Bone Scan Image Enhancement using Pipeline Application of Dynamic Stochastic Resonance Algorithm and Block-Matching 3D Filter

Cited by 2 publications

References 17 publications

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

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