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Abbosh A, Bialkowski K, Guo L, Al-Saffar A, Zamani A, Trakic A, Brankovic A, Bialkowski A, Zhu G, Cook D, Crozier S. Clinical electromagnetic brain scanner. Sci Rep 2024; 14:5760. [PMID: 38459073 PMCID: PMC10923816 DOI: 10.1038/s41598-024-55360-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/22/2024] [Indexed: 03/10/2024] Open
Abstract
Stroke is a leading cause of death and disability worldwide, and early diagnosis and prompt medical intervention are thus crucial. Frequent monitoring of stroke patients is also essential to assess treatment efficacy and detect complications earlier. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they cannot be easily used onsite, nor for frequent monitoring purposes. To meet those requirements, an electromagnetic imaging (EMI) device, which is portable, non-invasive, and non-ionizing, has been developed. It uses a headset with an antenna array that irradiates the head with a safe low-frequency EM field and captures scattered fields to map the brain using a complementary set of physics-based and data-driven algorithms, enabling quasi-real-time detection, two-dimensional localization, and classification of strokes. This study reports clinical findings from the first time the device was used on stroke patients. The clinical results on 50 patients indicate achieving an overall accuracy of 98% in classification and 80% in two-dimensional quadrant localization. With its lightweight design and potential for use by a single para-medical staff at the point of care, the device can be used in intensive care units, emergency departments, and by paramedics for onsite diagnosis.
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Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
- CRC-P60941 Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants
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Affiliation(s)
- Amin Abbosh
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
| | - Konstanty Bialkowski
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Lei Guo
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Ahmed Al-Saffar
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Ali Zamani
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Adnan Trakic
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Aida Brankovic
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Alina Bialkowski
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - Guohun Zhu
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
| | - David Cook
- Faculty of Medicine, The University of Queensland, St Lucia, QLD4072, Australia
| | - Stuart Crozier
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia
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Xue F, Guo L, Bialkowski A, Abbosh A. Training Universal Deep-Learning Networks for Electromagnetic Medical Imaging Using a Large Database of Randomized Objects. SENSORS (BASEL, SWITZERLAND) 2023; 24:8. [PMID: 38202870 PMCID: PMC10780526 DOI: 10.3390/s24010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based approaches are susceptible to inaccuracies stemming from inadequate training datasets, primarily consisting of signals generated from simplified and homogeneous imaging scenarios. This paper introduces a novel methodology to construct an expansive and diverse database encompassing domains featuring randomly shaped structures with electrical properties representative of healthy and abnormal tissues. The core objective of this database is to enable the training of universal deep-learning techniques for permittivity profile reconstruction in complex electromagnetic medical imaging domains. The constructed database contains 25,000 unique objects created by superimposing from 6 to 24 randomly sized ellipses and polygons with varying electrical attributes. Introducing randomness in the database enhances training, allowing the neural network to achieve universality while reducing the risk of overfitting. The representative signals in the database are generated using an array of antennas that irradiate the imaging domain and capture scattered signals. A custom-designed U-net is trained by using those signals to generate the permittivity profile of the defined imaging domain. To assess the database and confirm the universality of the trained network, three distinct testing datasets with diverse objects are imaged using the designed U-net. Quantitative assessments of the generated images show promising results, with structural similarity scores consistently exceeding 0.84, normalized root mean square errors remaining below 14%, and peak signal-to-noise ratios exceeding 33 dB. These results demonstrate the practicality of the constructed database for training deep learning networks that have generalization capabilities in solving inverse problems in medical imaging without the need for additional physical assistant algorithms.
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Affiliation(s)
- Fei Xue
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane 4072, Australia; (L.G.); (A.B.); (A.A.)
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Ding Y, Gong Z, Chen Y, Hu J, Chen Y. Quantitative Analysis of Super Resolution in Electromagnetic Inverse Scattering for Microwave Medical Sensing and Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:7404. [PMID: 37687861 PMCID: PMC10490563 DOI: 10.3390/s23177404] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
Microwave medical sensing and imaging (MMSI) has been a research hotspot in the past years. Imaging algorithms based on electromagnetic inverse scattering (EIS) play a key role in MMSI due to the super-resolution phenomenon. EIS problems generally employ far-field scattered data to reconstruct images. However, the far-field data do not include information outside the Ewald's sphere, so theoretically it is impossible to achieve super resolution. The reason for super resolution has not been clarified. The majority of the current research focuses on how nonlinearity affects the super-resolution phenomena in EIS. However, the mechanism of super-resolution in the absence of nonlinearity is routinely ignored. In this research, we address a prevalent yet overlooked problem where the image resolution due to scatterers of extended structures is incorrectly analyzed using the model of point scatterers. Specifically, the classical resolution of EIS is defined by the Rayleigh criterion which is only suitable for point-like scatterers. However, the super-resolution in EIS is often observed for general scatterers like cylinders, squares or Austria shapes. Subsequently, we provide theoretical results for the Born approximation framework in EIS, and employ the Sparrow criteria to quantify the resolution for symmetric objects of extended structures. Furthermore, the modified Sparrow criterion is proposed to calculate the resolution of asymmetric scatterers. Numerical examples show that the proposed approach can better explain the super-resolution phenomenon in EIS.
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Affiliation(s)
- Yahui Ding
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (Y.D.); (J.H.); (Y.C.)
| | - Zheng Gong
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China;
| | - Yifan Chen
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jun Hu
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (Y.D.); (J.H.); (Y.C.)
| | - Yongpin Chen
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (Y.D.); (J.H.); (Y.C.)
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