Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images.
Medical imaging provides information valuable in diagnosis, planning, and control of therapies. In this paper, we develop a method that uses a specific type of imaging--the magnetic resonance thermometry--to identify accurate and computationally efficient site and patient-specific computer models fo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22073204/?tool=EBI |
| _version_ | 1818404669450354688 |
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| author | Ran Niu Mikhail Skliar |
| author_facet | Ran Niu Mikhail Skliar |
| author_sort | Ran Niu |
| collection | DOAJ |
| description | Medical imaging provides information valuable in diagnosis, planning, and control of therapies. In this paper, we develop a method that uses a specific type of imaging--the magnetic resonance thermometry--to identify accurate and computationally efficient site and patient-specific computer models for thermal therapies, such as focused ultrasound surgery, hyperthermia, and thermally triggered targeted drug delivery. The developed method uses a sequence of acquired MR thermometry images to identify a treatment model describing the deposition and dissipation of thermal energy in tissues. The proper orthogonal decomposition of thermal images is first used to identify a set of empirical eigenfunctions, which captures spatial correlations in the thermal response of tissues. Using the reduced subset of eigenfunction as a functional basis, low-dimensional thermal response and the ultrasound specific absorption rate models are then identified. Once identified, the treatment models can be used to plan, optimize, and control the treatment. The developed approach is validated experimentally using the results of MR thermal imaging of a tissue phantom during focused ultrasound sonication. The validation demonstrates that our approach produces accurate low-dimensional treatment models and provides a convenient tool for balancing the accuracy of model predictions and the computational complexity of the treatment models. |
| first_indexed | 2024-12-14T08:43:50Z |
| format | Article |
| id | doaj.art-75d5a9c7a9d94f14b15d091253387e95 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T08:43:50Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-75d5a9c7a9d94f14b15d091253387e952022-12-21T23:09:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01611e2683010.1371/journal.pone.0026830Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images.Ran NiuMikhail SkliarMedical imaging provides information valuable in diagnosis, planning, and control of therapies. In this paper, we develop a method that uses a specific type of imaging--the magnetic resonance thermometry--to identify accurate and computationally efficient site and patient-specific computer models for thermal therapies, such as focused ultrasound surgery, hyperthermia, and thermally triggered targeted drug delivery. The developed method uses a sequence of acquired MR thermometry images to identify a treatment model describing the deposition and dissipation of thermal energy in tissues. The proper orthogonal decomposition of thermal images is first used to identify a set of empirical eigenfunctions, which captures spatial correlations in the thermal response of tissues. Using the reduced subset of eigenfunction as a functional basis, low-dimensional thermal response and the ultrasound specific absorption rate models are then identified. Once identified, the treatment models can be used to plan, optimize, and control the treatment. The developed approach is validated experimentally using the results of MR thermal imaging of a tissue phantom during focused ultrasound sonication. The validation demonstrates that our approach produces accurate low-dimensional treatment models and provides a convenient tool for balancing the accuracy of model predictions and the computational complexity of the treatment models.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22073204/?tool=EBI |
| spellingShingle | Ran Niu Mikhail Skliar Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images. PLoS ONE |
| title | Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images. |
| title_full | Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images. |
| title_fullStr | Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images. |
| title_full_unstemmed | Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images. |
| title_short | Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images. |
| title_sort | identification of controlled complexity thermal therapy models derived from magnetic resonance thermometry images |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22073204/?tool=EBI |
| work_keys_str_mv | AT ranniu identificationofcontrolledcomplexitythermaltherapymodelsderivedfrommagneticresonancethermometryimages AT mikhailskliar identificationofcontrolledcomplexitythermaltherapymodelsderivedfrommagneticresonancethermometryimages |