Literature DB >> 17089853

Comparison of MR-thermography and planning calculations in phantoms.

J Gellermann1, M Weihrauch, C H Cho, W Wlodarczyk, H Fähling, R Felix, V Budach, M Weiser, J Nadobny, P Wust.   

Abstract

A systematic comparison of three-dimensional MR (magnetic resonance) thermography and planning calculations in phantoms for the hyperthermia (HT) SIGMA-Eye applicator. We performed 2 x 6 experiments in a homogeneous cylindrical and a heterogeneous elliptical phantom by adjusting 82 different patterns with different phase control inside an MR tomograph (Siemens Magnetom Symphony, 1.5 Tesla). For MR thermography, we employed the proton resonance frequency shift method with a drift correction based on silicon tubes. For the planning calculations, we used the finite-difference time-domain (FDTD) method and, in addition, modeled the antennas and the transforming network. We generated regions according to a segmentation of bones and tissue, and used an interpolation technique with a subgrid of 0.5 cm size at the interfaces. A Gauss-Newton solver has been developed to adapt phases and amplitudes. A qualitative agreement between the planning program and measurements was obtained, including a correct prediction of hot spot locations. The final deviation between planning and measurement is in the range of 2-3 W/kg, i.e., below 10%. Additional HT phase and amplitude adaptation, as well as position correction of the phantom in the SIGMA-Eye, further improve the results. HT phase corrections in the range of 30-40 degrees and HT amplitude corrections of +/- 20-30% are required for the best agreement. The deviation /MR-FDTD/, and the HT phase/amplitude corrections depend on the type of phantom, certain channel groups, pattern steering, and the positioning error. Appropriate agreement between three-dimensional specific absorption rate distributions measured by MR-thermography and planning calculations is achieved, if the correct position and adapted feed point parameters are considered. As long as feed-point parameters are uncertain (i.e., cannot be directly measured during therapy), a prospective planning will remain difficult. However, we can use the information of MR thermography to better predict the patterns in the future even without the knowledge of feed-point parameters.

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Year:  2006        PMID: 17089853     DOI: 10.1118/1.2348761

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  10 in total

1.  Mathematical formulation and analysis of the nonlinear system reconstruction of the online image-guided adaptive control of hyperthermia.

Authors:  Kung-Shan Cheng; Mark W Dewhirst; Paul F Stauffer; Shiva Das
Journal:  Med Phys       Date:  2010-03       Impact factor: 4.071

2.  Fast temperature optimization of multi-source hyperthermia applicators with reduced-order modeling of 'virtual sources'.

Authors:  Kung-Shan Cheng; Vadim Stakhursky; Oana I Craciunescu; Paul Stauffer; Mark Dewhirst; Shiva K Das
Journal:  Phys Med Biol       Date:  2008-02-25       Impact factor: 3.609

3.  A heterogeneous human tissue mimicking phantom for RF heating and MRI thermal monitoring verification.

Authors:  Yu Yuan; Cory Wyatt; Paolo Maccarini; Paul Stauffer; Oana Craciunescu; James Macfall; Mark Dewhirst; Shiva K Das
Journal:  Phys Med Biol       Date:  2012-03-20       Impact factor: 3.609

4.  Accuracy of real time noninvasive temperature measurements using magnetic resonance thermal imaging in patients treated for high grade extremity soft tissue sarcomas.

Authors:  Oana I Craciunescu; Paul R Stauffer; Brian J Soher; Cory R Wyatt; Omar Arabe; Paolo Maccarini; Shiva K Das; Kung-Shan Cheng; Terence Z Wong; Ellen L Jones; Mark W Dewhirst; Zeljko Vujaskovic; James R MacFall
Journal:  Med Phys       Date:  2009-11       Impact factor: 4.071

5.  Real-time MRI-guided hyperthermia treatment using a fast adaptive algorithm.

Authors:  Vadim L Stakhursky; Omar Arabe; Kung-Shan Cheng; James Macfall; Paolo Maccarini; Oana Craciunescu; Mark Dewhirst; Paul Stauffer; Shiva K Das
Journal:  Phys Med Biol       Date:  2009-03-13       Impact factor: 3.609

Review 6.  Simulation techniques in hyperthermia treatment planning.

Authors:  Margarethus M Paulides; Paul R Stauffer; Esra Neufeld; Paolo F Maccarini; Adamos Kyriakou; Richard A M Canters; Chris J Diederich; Jurriaan F Bakker; Gerard C Van Rhoon
Journal:  Int J Hyperthermia       Date:  2013-05-14       Impact factor: 3.914

7.  Online feedback focusing algorithm for hyperthermia cancer treatment.

Authors:  Kung-Shan Cheng; Vadim Stakhursky; Paul Stauffer; Mark Dewhirst; Shiva K Das
Journal:  Int J Hyperthermia       Date:  2007-11       Impact factor: 3.914

8.  Optimization of Single Voxel MR Spectroscopy Sequence Parameters and Data Analysis Methods for Thermometry in Deep Hyperthermia Treatments.

Authors:  J Hartmann; J Gellermann; T Brandt; M Schmidt; S Pyatykh; J Hesser; O Ott; R Fietkau; C Bert
Journal:  Technol Cancer Res Treat       Date:  2016-07-14

9.  Quantitative, Multi-institutional Evaluation of MR Thermometry Accuracy for Deep-Pelvic MR-Hyperthermia Systems Operating in Multi-vendor MR-systems Using a New Anthropomorphic Phantom.

Authors:  Sergio Curto; Bassim Aklan; Tim Mulder; Oliver Mils; Manfred Schmidt; Ulf Lamprecht; Michael Peller; Ruediger Wessalowski; Lars H Lindner; Rainer Fietkau; Daniel Zips; Gennaro G Bellizzi; Netteke van Holthe; Martine Franckena; Margarethus M Paulides; Gerard C van Rhoon
Journal:  Cancers (Basel)       Date:  2019-11-02       Impact factor: 6.639

10.  Thermometry of red blood cell concentrate: magnetic resonance decoding warm up process.

Authors:  Gert Reiter; Ursula Reiter; Thomas Wagner; Noemi Kozma; Jörg Roland; Helmut Schöllnast; Franz Ebner; Gerhard Lanzer
Journal:  PLoS One       Date:  2013-02-28       Impact factor: 3.240

  10 in total

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