A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart

Foomany, Farbod Hosseyndoust; Beheshti, Mohammad Taghi Hamidi; Magtibay, Karl; Massé, Stéphane; Lai, Patrick F.H.; Asta, John; Zamiri, Nima; Jaffray, David A.; Krishnan, Sridhar; Nanthakumar, Kumaraswamy; Umapathy, Karthikeyan

doi:10.1109/embc.2014.6943780

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…• 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%

“…SNR in magnitude MRI images has been assumed to have Gaussian distribution [79] and SNR measures have been developed by Firbank et al [127]. We performed noise analysis experiments on phantom and pig heart and studied the contribution of current injection to the level of noise [76]. In this work, a baseline analysis method was suggested, which adds the phase changes from two phase cycles instead of subtracting them.…”

Section: Quantifying Current Injection Effect On Noise Level In CDImentioning

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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“…• 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%

Section: Quantifying Current Injection Effect On Noise Level In CDImentioning

confidence: 99%

Simulation models of current density imaging in studying cardiac states

Beheshti¹

2021

Preprint

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“…We can verify the distribution of noise in the phantom by performing a baseline analysis. 21 In this case, we record real and imaginary images twice when there is no current in each orientation. The current is then calculated by considering repeated images in each orientation as two phase cycles of the CDI method.…”

Section: Distribution Of Noisementioning

confidence: 99%

“…The current is then calculated by considering repeated images in each orientation as two phase cycles of the CDI method. 21 The resulting current density map in A∕m 2 and its histogram are shown in Figs. 7(a) and 7(b).…”

Section: Distribution Of Noisementioning

confidence: 99%

Noise distribution and denoising of current density images

et al. 2015

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Abstract. Current density imaging (CDI) is a magnetic resonance (MR) imaging technique that could be used to study current pathways inside the tissue. The current distribution is measured indirectly as phase changes. The inherent noise in the MR imaging technique degrades the accuracy of phase measurements leading to imprecise current variations. The outcome can be affected significantly, especially at a low signal-to-noise ratio (SNR). We have shown the residual noise distribution of the phase to be Gaussian-like and the noise in CDI images approximated as a Gaussian. This finding matches experimental results. We further investigated this finding by performing comparative analysis with denoising techniques, using two CDI datasets with two different currents (20 and 45 mA). We found that the block-matching and three-dimensional (BM3D) technique outperforms other techniques when applied on current density (J). The minimum gain in noise power by BM3D applied to J compared with the next best technique in the analysis was found to be around 2 dB per pixel. We characterize the noise profile in CDI images and provide insights on the performance of different denoising techniques when applied at two different stages of current density reconstruction.

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A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart

Cited by 2 publications

References 15 publications

Simulation models of current density imaging in studying cardiac states

Simulation models of current density imaging in studying cardiac states

Noise distribution and denoising of current density images

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