Modified Newton Raphson Algorithm for Electrical Impedance Image Reconstruction

Chang, Tiantian; Huo, Xuyang

doi:10.1007/978-3-030-89698-0_95

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…This process enables the visualization of dynamic physiological events such as pulmonary ventilation and perfusion, providing valuable real-time feedback for respiratory care management [4,7]. The Newton-Raphson method in EIT is used to update the conductivity estimate iteratively by minimizing the difference between the measured boundary voltages and the boundary voltages predicted by a forward model based on the current conductivity estimate [8]. Graphical representation of this process involves:…”

Section: Eit Image Reconstructionmentioning

confidence: 99%

Electrical Impedance Tomography: A Narrative Review on Technology Principles and Its Current and Future Fields of Application

Cappellini,

Campagnola,

Consales

2024

Preprint

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Background: Electrical Impedance Tomography (EIT), combined with variable ventilation strate-gies and Artificial Intelligence (AI), is poised to revolutionize critical care by transitioning from reactive to predictive approaches. This integration aims to enhance patient outcomes through personalized interventions and real-time monitoring. Methods: This narrative review explores the principles and applications of EIT, variable ventilation, and AI in critical care. EIT’s impedance sensing creates dynamic images of internal physiology, aiding in the management of conditions like Acute Respiratory Distress Syndrome (ARDS). Variable ventilation mimics natural breathing variability to improve lung function and minimize ventilator-induced lung injury. AI enhances EIT through advanced image reconstruction techniques, neural networks, and digital twin technology, offering more accurate diagnostics and tailored therapeutic interventions. Conclu-sions: The confluence of EIT, variable ventilation, and AI represents a significant advancement in critical care, enabling a predictive, personalized approach. EIT provides real-time insights into lung function, guiding precise ventilation adjustments and therapeutic interventions. AI inte-gration enhances EIT's diagnostic capabilities, facilitating the development of personalized treatment plans. This synergy fosters interdisciplinary collaborations and sets the stage for in-novative research, ultimately improving patient outcomes and advancing the future of critical care.

show abstract

Section: Eit Image Reconstructionmentioning

confidence: 99%

Electrical Impedance Tomography: A Narrative Review on Technology Principles and Its Current and Future Fields of Application

Cappellini,

Campagnola,

Consales

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This process enables the visualization of dynamic physiological events such as pulmonary ventilation and perfusion, providing valuable real-time feedback for respiratory care management [4,7]. The Newton-Raphson method in EIT is used to update the conductivity estimate iteratively by minimizing the difference between the measured boundary voltages and the boundary voltages predicted by a forward model based on the current conductivity estimate [8]. A graphical representation of this process involves:…”

Section: Eit Image Reconstructionmentioning

confidence: 99%

“…This model assigns voltages based on proximity to simulate a forward problem. Subsequently, an 'inverse problem step recalibrates the The Newton-Raphson method in EIT is used to update the conductivity estimate iteratively by minimizing the difference between the measured boundary voltages and the boundary voltages predicted by a forward model based on the current conductivity estimate [8]. A graphical representation of this process involves:…”

Section: Eit Image Reconstructionmentioning

confidence: 99%

Electrical Impedance Tomography, Artificial Intelligence, and Variable Ventilation: Transforming Respiratory Monitoring and Treatment in Critical Care

Cappellini,

Campagnola,

Consales

2024

JPM

View full text Add to dashboard Cite

Background: Electrical Impedance Tomography (EIT), combined with variable ventilation strategies and Artificial Intelligence (AI), is poised to revolutionize critical care by transitioning from reactive to predictive approaches. This integration aims to enhance patient outcomes through personalized interventions and real-time monitoring. Methods: this narrative review explores the principles and applications of EIT, variable ventilation, and AI in critical care. EIT impedance sensing creates dynamic images of internal physiology, aiding the management of conditions like Acute Respiratory Distress Syndrome (ARDS). Variable ventilation mimics natural breathing variability to improve lung function and minimize ventilator-induced lung injury. AI enhances EIT through advanced image reconstruction techniques, neural networks, and digital twin technology, offering more accurate diagnostics and tailored therapeutic interventions. Conclusions: the confluence of EIT, variable ventilation, and AI represents a significant advancement in critical care, enabling a predictive, personalized approach. EIT provides real-time insights into lung function, guiding precise ventilation adjustments and therapeutic interventions. AI integration enhances EIT diagnostic capabilities, facilitating the development of personalized treatment plans. This synergy fosters interdisciplinary collaborations and sets the stage for innovative research, ultimately improving patient outcomes and advancing the future of critical care.

show abstract

Modified Newton Raphson Algorithm for Electrical Impedance Image Reconstruction

Cited by 2 publications

References 10 publications

Electrical Impedance Tomography: A Narrative Review on Technology Principles and Its Current and Future Fields of Application

Electrical Impedance Tomography: A Narrative Review on Technology Principles and Its Current and Future Fields of Application

Electrical Impedance Tomography, Artificial Intelligence, and Variable Ventilation: Transforming Respiratory Monitoring and Treatment in Critical Care

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