Photon-counting computed tomography thermometry via material decomposition and machine learning

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorWang, Nathanen_US
dc.contributor.authorLi, Mengzhouen_US
dc.contributor.authorHaverinen, Petterien_US
dc.contributor.departmentDesign Factoryen
dc.contributor.organizationJohns Hopkins Universityen_US
dc.contributor.organizationRensselaer Polytechnic Instituteen_US
dc.contributor.organizationDesign Factoryen_US
dc.date.accessioned2023-04-26T08:39:04Z
dc.date.available2023-04-26T08:39:04Z
dc.date.issued2023-12en_US
dc.descriptionFunding Information: This work is supported by the Johns Hopkins University Leong Research Award for Undergraduates. Publisher Copyright: © 2023, The Author(s).
dc.description.abstractThermal ablation procedures, such as high intensity focused ultrasound and radiofrequency ablation, are often used to eliminate tumors by minimally invasively heating a focal region. For this task, real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings. Current computed tomography (CT) thermometry is based on energy-integrated CT, tissue-specific experimental data, and linear relationships between attenuation and temperature. In this paper, we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest. In our feasibility study, distilled water, 50 mmol/L CaCl2, and 600 mmol/L CaCl2 are chosen as the base materials. Their attenuations are measured in four discrete energy bins at various temperatures. The neural network trained on the experimental data achieves a mean absolute error of 3.97 °C and 1.80 °C on 300 mmol/L CaCl2 and a milk-based protein shake respectively. These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dissimilar to our base materials.en
dc.description.versionPeer revieweden
dc.format.extent6
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationWang, N, Li, M & Haverinen, P 2023, ' Photon-counting computed tomography thermometry via material decomposition and machine learning ', Visual Computing for Industry, Biomedicine, and Art, vol. 6, no. 1, 2 . https://doi.org/10.1186/s42492-022-00129-wen
dc.identifier.doi10.1186/s42492-022-00129-wen_US
dc.identifier.issn2096-496X
dc.identifier.issn2524-4442
dc.identifier.otherPURE UUID: 33f447b9-37b0-4222-8c1b-e282b7a37e53en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/33f447b9-37b0-4222-8c1b-e282b7a37e53en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85146299375&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/106792949/s42492_022_00129_w.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/120515
dc.identifier.urnURN:NBN:fi:aalto-202304262837
dc.language.isoenen
dc.publisherSPRINGER
dc.relation.ispartofseriesVisual Computing for Industry, Biomedicine, and Arten
dc.relation.ispartofseriesVolume 6, issue 1en
dc.rightsopenAccessen
dc.subject.keywordArtificial intelligenceen_US
dc.subject.keywordComputed tomography thermometryen_US
dc.subject.keywordDeep learningen_US
dc.subject.keywordMaterial decompositionen_US
dc.subject.keywordNeural networken_US
dc.subject.keywordPhoton-counting computed tomographyen_US
dc.subject.keywordRadiotherapyen_US
dc.subject.keywordThermotherapyen_US
dc.titlePhoton-counting computed tomography thermometry via material decomposition and machine learningen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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