Noise distribution and denoising of current density images

Beheshti, Mohammadali; Foomany, Farbod Hosseyndoust; Magtibay, Karl; Jaffray, David A.; Krishnan, Sridhar; Nanthakumar, Kumaraswamy; Umapathy, Karthikeyan

doi:10.1117/1.jmi.2.2.024005

Cited by 3 publications

(5 citation statements)

References 21 publications

(26 reference statements)

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“…e BM3D algorithm is a denoising method based on image blocks. e specific operation was to divide the images with noises into many image domains, each image was denoised separately, and the denoised image domains were reaggregated, so as to form the final image with higher signal-tonoise ratio (SNR) [21]. Figure 1 illustrates the specific process.…”

Section: Image Denoising Methods Based On Bm3d Algorithmmentioning

confidence: 99%

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Zhang

Yin

Han

et al. 2021

Scientific Programming

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This work was aimed to explore the application of the L2-block-matching and 3-dimentional filtering (BM3D) (L2-BM3D) denoising algorithm in the treatment of lumbar degeneration with long- and short-segment fixation of posterior decompression. 120 patients with degenerative lumbar scoliosis were randomly divided into group A (MRI images were not processed), group B (MRI images were processed by the BM3D denoising algorithm), and group C (MRI images were processed by the BM3D denoising algorithm based on weighted norm L2). This denoising algorithm was comprehensively evaluated in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and running time. Besides, the results of surgeries based on different denoising methods were assessed through the surgical time, intraoperative blood loss, postoperative drainage, and postoperative follow-up. The results showed the following: (1) PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) of the L2-BM3D algorithm are better than those of the BM3D algorithm (31.21 dB versus 29.33 dB, 0.83 versus 0.72), while mean square error (MSE) was less than that of the BM3D algorithm ( P < 0.05 ). (2) The operation time, intraoperative bleeding, and postoperative drainage volume in group C were lower than those in group B and group A ( P < 0.05 ). The postoperative follow-up results showed that, in group C, the postoperative VAS (visual analysis scale) score (1.03 ± 0.29) and ODI (Oswestry disability index) (9.29 ± 0.32) were lower, indicating that the postoperative recovery effect of patients was better. Therefore, the patient’s postoperative recovery effect was better. In conclusion, the L2-BM3D algorithm had an ideal denoising effect on MRI images of lumbar degeneration and was worthy of clinical promotion.

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Section: Image Denoising Methods Based On Bm3d Algorithmmentioning

confidence: 99%

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Zhang

Yin

Han

et al. 2021

Scientific Programming

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“…The closed form of noise distribution for the phase-wrapped images was proved in [75], the proof of which is provided in Appendix A. The phase distribution for a point inside the subject is proved to be [75]:…”

Section: Noise Distribution In CDI Images and Their Denoisingmentioning

confidence: 97%

“…• Correlation of structural data obtained by DTI imaging and dynamic CDI data for the first time [73,74] • Improved post processing of the CDI imaging [75][76][77], and…”

Section: Proposed Approaches and Contributionsmentioning

confidence: 99%

“…In order to counter the noise in CDI images more efficiently, we need to characterize the CDI noise distribution. In another study by our group, the distribution of noise in the phase images was calculated and the distribution of noise in the final CDI images was approximated [75].…”

Section: Noise Distribution In CDI Images and Their Denoisingmentioning

confidence: 99%

“…These phase distributions can be safely approximated as Gaussian [75]. The distribution of current density at each point can also be approximated as Gaussian [75].…”

Section: Noise Distribution In CDI Images and Their Denoisingmentioning

confidence: 99%

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Simulation models of current density imaging in studying cardiac states

Beheshti¹

2021

Preprint

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Electro-mechanical disorders in cardiac function result in arrhythmias. Due to the non-stationary nature of these arrhythmias and, owing to lethality associated with certain type of arrhythmias, they are challenging to study. Most of the existing studies are limited in that they extract electrical activity from surface intracardiac electrical activity, either through the use of electrical or optical mapping. One way of studying current pathways inside and through biological tissues is by using Magnetic Resonance Imaging (MRI) based Low Frequency Current Density Imaging (LFCDI). For the first time CDI was used to study ex-vivo beating hearts in different cardiac states. It should be said that; this approach involves heavy logistical and procedural complexity, hence, it would be beneficial to adapt existing electrophysiological computer models to investigate and simulate current density maps specific to studying cardiac function. In achieving this, the proposed work presents an approach to model the current density maps in 3D and study the current distributions in different electrophysiological states of the heart. The structural and fiber orientation of the heart used in this study were extracted using MRI-based Diffusion Tensor Imaging. The monodomain and bidomain Aliev-Panfilov electrophysiological models were used for CDI modeling, and the results indicate that different states were distinguishable using range and correlation of simulated current density maps. The obtained results through modeling were corroborated with actual experimental CDI data from porcine hearts. Individually and comparatively, the experimental and simulation results for various states have the same trend in terms of variations (trend correlation coefficients ≥ 0.98) and state correlations (trend correlation coefficients ≥ 0.89). The results also show that the root mean square (RMS) error in average range ratios between bidomain CDI model results and real CDI data is 0.1972 and the RMS error in state correlations between bidomain CDI model results and real CDI data is 0.2833. These results indicate, as expected, the proposed bidomain model simulation of CDI corroborates well with experimental data and can serve as a valuable tool for studying lethal cardiac arrhythmias under different simulation conditions that are otherwise not possible or difficult in a real-world experimental setup.

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Modeling Current Density Maps Using Aliev–Panfilov Electrophysiological Heart Model

Beheshti

Foomany

Magtibay

et al. 2016

Cardiovasc Eng Tech

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Most existing studies of cardiac arrhythmia rely on surface measurements through optical or electrical mapping techniques. Current density imaging (CDI) is a method which enables us to study current pathways inside the tissue. However, this method entails implementation complexities for beating ex vivo hearts. Hence, this work presents an approach to simulate and study the current distributions in different cardiac electrophysiological states. The results are corroborated by experimental data, and they indicate that different states were distinguishable. The CDI simulations can be used for studying cardiac arrhythmias under simulation conditions which are otherwise impossible or difficult to be implemented experimentally.

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Noise distribution and denoising of current density images

Cited by 3 publications

References 21 publications

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Simulation models of current density imaging in studying cardiac states

Modeling Current Density Maps Using Aliev–Panfilov Electrophysiological Heart Model

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