Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging

Preston, Chet; Kasoff, Willard S.; Witte, Russell S.

doi:10.1016/j.ultrasmedbio.2018.06.021

Cited by 17 publications

(13 citation statements)

References 47 publications

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“…In addition, Preston constructed monopolar and dipolar models in saline with and without a human skullcap. AEI can accurately position DBS leads to within 0.70 mm, with a detection threshold of 1.75 mA at 1 MPa and a sensitivity of 0.52 ± 0.07 μV/(mA * MPa) (Preston et al, 2018 ). On this basis, he inserted 8-channel directional DBS leads into three adult human skulls immersed in 0.9% NaCl and used a 2.5 MHz linear array to focus near the contacts on the leads.…”

Section: Research Progressmentioning

confidence: 99%

Biological current source imaging method based on acoustoelectric effect: A systematic review

Zhang

Liu

et al. 2022

Front. Neurosci.

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Neuroimaging can help reveal the spatial and temporal diversity of neural activity, which is of utmost importance for understanding the brain. However, conventional non-invasive neuroimaging methods do not have the advantage of high temporal and spatial resolution, which greatly hinders clinical and basic research. The acoustoelectric (AE) effect is a fundamental physical phenomenon based on the change of dielectric conductivity that has recently received much attention in the field of biomedical imaging. Based on the AE effect, a new imaging method for the biological current source has been proposed, combining the advantages of high temporal resolution of electrical measurements and high spatial resolution of focused ultrasound. This paper first describes the mechanism of the AE effect and the principle of the current source imaging method based on the AE effect. The second part summarizes the research progress of this current source imaging method in brain neurons, guided brain therapy, and heart. Finally, we discuss the problems and future directions of this biological current source imaging method. This review explores the relevant research literature and provides an informative reference for this potential non-invasive neuroimaging method.

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Section: Research Progressmentioning

confidence: 99%

Biological current source imaging method based on acoustoelectric effect: A systematic review

Zhang

Liu

et al. 2022

Front. Neurosci.

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“…In the research of ABI, the Decoding Algorithm with Envelope is frequently utilized (Qin et al, 2012;Qin et al, 2015;Berthon et al, 2017;Preston et al, 2018;Berthon et al, 2019;Zhou et al, 2020;Zhou et al, 2021). The theory (Zhou et al, 2019) stipulates that the AE signal is first converted into an analytical signal.…”

Section: Decoding Algorithm With Envelopementioning

confidence: 99%

“…And, a highly sensitive ultrafast AE imaging system is included ( Berthon et al, 2017 ). A potential application for AE imaging, subsequently, is the selective mapping of lead currents using DBS device ( Preston et al, 2018 ). In 2017, the concept of Acoustoelectric brain imaging (ABI) was introduced, which expressly refers to the measurement of brain electrical signal via ABI.…”

Section: Introductionmentioning

confidence: 99%

An adaptive acoustoelectric signal decoding algorithm based on Fourier fitting for brain function imaging

Song

Wang

et al. 2022

Front. Physiol.

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Acousticelectric brain imaging (ABI), which is based on the acoustoelectric (AE) effect, is a potential brain function imaging method for mapping brain electrical activity with high temporal and spatial resolution. To further enhance the quality of the decoded signal and the resolution of the ABI, the decoding accuracy of the AE signal is essential. An adaptive decoding algorithm based on Fourier fitting (aDAF) is suggested to increase the AE signal decoding precision. The envelope of the AE signal is first split into a number of harmonics by Fourier fitting in the suggested aDAF. The least square method is then utilized to adaptively select the greatest harmonic component. Several phantom experiments are implemented to assess the performance of the aDAF, including 1-source with various frequencies, multiple-source with various frequencies and amplitudes, and multiple-source with various distributions. Imaging resolution and decoded signal quality are quantitatively evaluated. According to the results of the decoding experiments, the decoded signal amplitude accuracy has risen by 11.39% when compared to the decoding algorithm with envelope (DAE). The correlation coefficient between the source signal and the decoded timing signal of aDAF is, on average, 34.76% better than it was for DAE. Finally, the results of the imaging experiment show that aDAF has superior imaging quality than DAE, with signal-to noise ratio (SNR) improved by 23.32% and spatial resolution increased by 50%. According to the experiments, the proposed aDAF increased AE signal decoding accuracy, which is vital for future research and applications related to ABI.

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“…t US and t phys indicate the US propagation time (in microseconds) and physiologic time (in milliseconds), respectively. Detailed derivations of the general AE equation have been previously described [12][13][14].…”

Section: B Acoustoelectric Cardiac Imagingmentioning

confidence: 99%

In vivo acoustoelectric imaging for high-resolution visualization of cardiac electric spatiotemporal dynamics

et al. 2020

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Acoustoelectric cardiac imaging (ACI) is a hybrid modality that exploits the interaction of an ultrasonic pressure wave and the resistivity of tissue to map current densities in the heart. This study demonstrates for the first time in vivo ACI in a swine model. ACI measured beat-to-beat variability (n = 20) of the peak of the cardiac activation wave at one location of the left ventricle as 5.32 ± 0.74 ~V, 3.26 ± 0.54 mm below the epicardial surface, and 2.67 ± 0.56 ms before the peak of the local electrogram. Cross-sectional ACI images exhibited propagation velocities of 0.192 ± 0.061 m/s along the epicardial-endocardial axis with an SNR of 24.9 dB. This study demonstrates beat-to-beat and multidimensional ACI, which might reveal important information to help guide electroanatomic mapping procedures during ablation therapy.

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Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging

Cited by 17 publications

References 47 publications

Biological current source imaging method based on acoustoelectric effect: A systematic review

Biological current source imaging method based on acoustoelectric effect: A systematic review

An adaptive acoustoelectric signal decoding algorithm based on Fourier fitting for brain function imaging

In vivo acoustoelectric imaging for high-resolution visualization of cardiac electric spatiotemporal dynamics

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