Human Brain Imaging by Electromagnetic Tomography: a mobile brain scanner for clinical settings

Henriksson, Tommy; Sahebdivan, Sahar; Planas, Ramón; Brunner, Cornelia; Kellermair, Lukas; Guger, Michael; Struhal, Walter; Fuchs, Peter; Reichl, Jakob; Stelzer, Andreas; Семенов, С. М.; Pearce, Stephen; Voško, Milan R.

doi:10.23919/eucap53622.2022.9769049

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…Equation ( 30) can be divided into two integrals: one including the second-order spatial-derivative of the Ẽz , and the other one including the second-order time-derivative of the Ẽz . By following the steps given in (11) and (12), the first integral can be rearranged as follows…”

Section: The Direct Methodsmentioning

confidence: 99%

“…In microwave imaging, an imaging domain is illuminated by transmit antennas with low‐power, non‐ionizing electromagnetic waves, and the scattered data collected by the receiving antennas are used to infer information about the target 1–3 . This technique, being safe, non‐invasive, and cost‐effective, is widely used in biomedical applications especially in breast cancer 4–10 and stroke detection 11–16 …”

Section: Introductionmentioning

confidence: 99%

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On the utilization of the adjoint method in microwave tomography

Soydan,

Top,

Gençer

2024

Numer Methods Biomed Eng

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In microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm. Because of the problem‐specific nature of the adjoint formulations, the dissimilarities between the adjoint calculations may be overlooked. Here, we have classified the adjoint method formulations into two groups: the direct and indirect methods. The direct method involves calculating the derivative of the cost function, whereas, in the indirect method, the derivative of the predicted data is calculated. In this review, the direct and indirect adjoint methods are presented, compared, and discussed. The formulations are explicitly derived using the two‐dimensional wave equation in frequency and time domains. Finite‐difference time‐domain simulations are conducted to show the different uses of the adjoint methods for both single source‐multiple receiver, and multiple transceiver scenarios. This study demonstrated that an appropriate adjoint method selection is significant to achieve improved computational efficiency for the applied optimization algorithm.

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Section: The Direct Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the utilization of the adjoint method in microwave tomography

Soydan,

Top,

Gençer

2024

Numer Methods Biomed Eng

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“…This research is the first technique to employ MWI for imaging bone health. Henriksson et al 81 proposed a portable electromagnetic tomography brain scanner prototype for human clinical studies. The scanner was reliable and safe enough for use in a clinical investigation.…”

Section: State Of Art Mwi Techniques For Disease Detectionmentioning

confidence: 99%

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Khalid,

Zubair,

Mehmood

et al. 2024

npj Imaging

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In recent years, microwave imaging (MWI) has emerged as a non-ionizing and cost-effective modality in healthcare, specifically within medical imaging. Concurrently, advances in artificial intelligence (AI) have significantly augmented the capabilities of medical imaging tools. This paper explores the intersection of these two domains, focusing on the integration of AI algorithms into MWI techniques to elevate accuracy and overall performance. Within the scope of existing literature, representative prior works are compared concerning the application of AI in both the “MWI for Healthcare Applications" and “Artificial Intelligence Assistance In MWI" sections. This comparative analysis sheds light on the diverse approaches employed to enhance the synergy between AI and MWI. While highlighting the state-of-the-art technology in MWI and its historical context, this paper delves into the historical taxonomy of AI-assisted MWI, elucidating the evolution of intelligent systems within this domain. Moreover, it critically examines prominent works, providing a nuanced understanding of the advancements and challenges encountered. Addressing the limitations and challenges inherent in developing AI-assisted MWI systems like Generalization to different conditions, Generalization to different conditions, etc the paper offers a brief synopsis of these obstacles, emphasizing the importance of overcoming them for robust and reliable results in actual clinical environments. Finally, the paper not only underscores the current advancements but also anticipates future innovations and developments in utilizing AI for MWI applications in healthcare.

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Automated, Reproducible, and Reconfigurable Human Head Phantom for Experimental Testing of Microwave Systems for Stroke Classification

Pokorny,

Drizdal,

Novak

et al. 2024

Int J Imaging Syst Tech

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Microwave systems for prehospital stroke classification are currently being developed. In the future, these systems should enable rapid recognition of the type of stroke, shorten the time to start treatment, and thus significantly improve the prognosis of patients. In this study, we realized a realistic and reconfigurable 3D human head phantom for the development, testing, and validation of these newly developed diagnostic methods. The phantom enables automated and reproducible measurements for different positions of the stroke model. The stroke model itself is also interchangeable, so measurements can be made for different types, sizes, and shapes of strokes. Furthermore, an extensive series of measurements was performed at a frequency of 1 GHz, and an SVM classification algorithm was deployed, which successfully identified ischemic stroke in 80% of the corresponding measured data. If similar classification accuracy could be achieved in patients, it would lead to a dramatic reduction in the consequences of strokes.

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Human Brain Imaging by Electromagnetic Tomography: a mobile brain scanner for clinical settings

Cited by 11 publications

References 23 publications

On the utilization of the adjoint method in microwave tomography

On the utilization of the adjoint method in microwave tomography

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Automated, Reproducible, and Reconfigurable Human Head Phantom for Experimental Testing of Microwave Systems for Stroke Classification

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