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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ran Niu, Mikhail Skliar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/75d5a9c7a9d94f14b15d091253387e95
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:75d5a9c7a9d94f14b15d091253387e95
record_format	dspace
spelling	oai:doaj.org-article:75d5a9c7a9d94f14b15d091253387e952021-11-18T07:35:10ZIdentification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images.1932-620310.1371/journal.pone.0026830https://doaj.org/article/75d5a9c7a9d94f14b15d091253387e952011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22073204/?tool=EBIhttps://doaj.org/toc/1932-6203Medical 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.Ran NiuMikhail SkliarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 11, p e26830 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ran Niu Mikhail Skliar Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images.
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.
format	article
author	Ran Niu Mikhail Skliar
author_facet	Ran Niu Mikhail Skliar
author_sort	Ran Niu
title	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_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_sort	identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/75d5a9c7a9d94f14b15d091253387e95
work_keys_str_mv	AT ranniu identificationofcontrolledcomplexitythermaltherapymodelsderivedfrommagneticresonancethermometryimages AT mikhailskliar identificationofcontrolledcomplexitythermaltherapymodelsderivedfrommagneticresonancethermometryimages
_version_	1718423228974628864

Identification of controlled-complexity thermal therapy models derived from magnetic resonance thermometry images.

Ejemplares similares