Non-Invasive Differential Temperature Monitoring Using Sensor Array for Microwave Hyperthermia Applications: A Subspace-Based Approach Article Swipe
Ji Wu
,
Fan Yang
,
Jinchuan Zheng
,
Hung T. Nguyen
,
Rifai Chai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jsan14010019
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jsan14010019
Non-invasive temperature monitoring is highly valuable in applications such as microwave hyperthermia treatment, where overheating may damage healthy tissue. This paper presents a subspace-based method for real-time temperature monitoring using a sensor array configuration. The proposed method improves upon the conventional Born approximation (BA) approach by accurately estimating the total field through primary induced currents. The temperature-dependent dielectric properties of breast tissues are modeled using data from porcine tissues, and a sigmoid function is employed to create realistic temperature transition zones in the numerical breast phantom. The method is validated through extensive simulations under noise-free and noisy conditions (SNR = 30 dB and 20 dB). The results demonstrate that our method maintains consistent performance across different temperature levels (38–45 °C), achieving reconstruction accuracy within ±0.2 °C at SNR = 30 dB and ±0.5 °C at SNR = 20 dB. While the computational overhead of calculating primary induced currents slightly increases the overall processing time, it leads to a faster convergence in the cost function minimization. These findings suggest that the proposed method offers a promising solution for real-time temperature monitoring in microwave hyperthermia applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jsan14010019
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4407364031Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jsan14010019Digital Object Identifier
- Title
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Non-Invasive Differential Temperature Monitoring Using Sensor Array for Microwave Hyperthermia Applications: A Subspace-Based ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-11Full publication date if available
- Authors
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Ji Wu, Fan Yang, Jinchuan Zheng, Hung T. Nguyen, Rifai ChaiList of authors in order
- Landing page
-
https://doi.org/10.3390/jsan14010019Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/jsan14010019Direct OA link when available
- Concepts
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Subspace topology, Computer science, Microwave, Differential (mechanical device), Microwave heating, Artificial intelligence, Telecommunications, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
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-
0Total citation count in OpenAlex
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.upon | 38 |
| abstract_inverted_index.These | 165 |
| abstract_inverted_index.While | 139 |
| abstract_inverted_index.array | 32 |
| abstract_inverted_index.field | 50 |
| abstract_inverted_index.leads | 155 |
| abstract_inverted_index.noisy | 96 |
| abstract_inverted_index.paper | 20 |
| abstract_inverted_index.time, | 153 |
| abstract_inverted_index.total | 49 |
| abstract_inverted_index.under | 93 |
| abstract_inverted_index.using | 29, 64 |
| abstract_inverted_index.where | 13 |
| abstract_inverted_index.zones | 80 |
| abstract_inverted_index.°C), | 119 |
| abstract_inverted_index.±0.2 | 124 |
| abstract_inverted_index.±0.5 | 132 |
| abstract_inverted_index.across | 114 |
| abstract_inverted_index.breast | 60, 84 |
| abstract_inverted_index.create | 76 |
| abstract_inverted_index.damage | 16 |
| abstract_inverted_index.faster | 158 |
| abstract_inverted_index.highly | 4 |
| abstract_inverted_index.levels | 117 |
| abstract_inverted_index.method | 24, 36, 87, 110, 171 |
| abstract_inverted_index.offers | 172 |
| abstract_inverted_index.sensor | 31 |
| abstract_inverted_index.within | 123 |
| abstract_inverted_index.healthy | 17 |
| abstract_inverted_index.induced | 53, 146 |
| abstract_inverted_index.modeled | 63 |
| abstract_inverted_index.overall | 151 |
| abstract_inverted_index.porcine | 67 |
| abstract_inverted_index.primary | 52, 145 |
| abstract_inverted_index.results | 106 |
| abstract_inverted_index.sigmoid | 71 |
| abstract_inverted_index.suggest | 167 |
| abstract_inverted_index.through | 51, 90 |
| abstract_inverted_index.tissue. | 18 |
| abstract_inverted_index.tissues | 61 |
| abstract_inverted_index.(38–45 | 118 |
| abstract_inverted_index.accuracy | 122 |
| abstract_inverted_index.approach | 44 |
| abstract_inverted_index.currents | 147 |
| abstract_inverted_index.employed | 74 |
| abstract_inverted_index.findings | 166 |
| abstract_inverted_index.function | 72, 163 |
| abstract_inverted_index.improves | 37 |
| abstract_inverted_index.overhead | 142 |
| abstract_inverted_index.phantom. | 85 |
| abstract_inverted_index.presents | 21 |
| abstract_inverted_index.proposed | 35, 170 |
| abstract_inverted_index.slightly | 148 |
| abstract_inverted_index.solution | 175 |
| abstract_inverted_index.tissues, | 68 |
| abstract_inverted_index.valuable | 5 |
| abstract_inverted_index.achieving | 120 |
| abstract_inverted_index.currents. | 54 |
| abstract_inverted_index.different | 115 |
| abstract_inverted_index.extensive | 91 |
| abstract_inverted_index.increases | 149 |
| abstract_inverted_index.maintains | 111 |
| abstract_inverted_index.microwave | 10, 181 |
| abstract_inverted_index.numerical | 83 |
| abstract_inverted_index.promising | 174 |
| abstract_inverted_index.real-time | 26, 177 |
| abstract_inverted_index.realistic | 77 |
| abstract_inverted_index.validated | 89 |
| abstract_inverted_index.accurately | 46 |
| abstract_inverted_index.conditions | 97 |
| abstract_inverted_index.consistent | 112 |
| abstract_inverted_index.dielectric | 57 |
| abstract_inverted_index.estimating | 47 |
| abstract_inverted_index.monitoring | 2, 28, 179 |
| abstract_inverted_index.noise-free | 94 |
| abstract_inverted_index.processing | 152 |
| abstract_inverted_index.properties | 58 |
| abstract_inverted_index.transition | 79 |
| abstract_inverted_index.treatment, | 12 |
| abstract_inverted_index.calculating | 144 |
| abstract_inverted_index.convergence | 159 |
| abstract_inverted_index.demonstrate | 107 |
| abstract_inverted_index.overheating | 14 |
| abstract_inverted_index.performance | 113 |
| abstract_inverted_index.simulations | 92 |
| abstract_inverted_index.temperature | 1, 27, 78, 116, 178 |
| abstract_inverted_index.Non-invasive | 0 |
| abstract_inverted_index.applications | 7 |
| abstract_inverted_index.conventional | 40 |
| abstract_inverted_index.hyperthermia | 11, 182 |
| abstract_inverted_index.applications. | 183 |
| abstract_inverted_index.approximation | 42 |
| abstract_inverted_index.computational | 141 |
| abstract_inverted_index.minimization. | 164 |
| abstract_inverted_index.configuration. | 33 |
| abstract_inverted_index.reconstruction | 121 |
| abstract_inverted_index.subspace-based | 23 |
| abstract_inverted_index.temperature-dependent | 56 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.04089105 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |