Literature DB >> 11764035

Discretizing large traceable vessels and using DE-MRI perfusion maps yields numerical temperature contours that match the MR noninvasive measurements.

O I Craciunescu1, B W Raaymakers, A N Kotte, S K Das, T V Samulski, J J Lagendijk.   

Abstract

The success of hyperthermia treatments is dependent on thermal dose distribution. However, the three-dimensional temperature distribution remains largely unknown. Without this knowledge, the relationship between thermal dose and outcome is noisy, and therapy cannot be optimized. Accurate computations of thermal distribution can contribute to an optimized therapy. The hyperthermia modeling group in the Department of Radiotherapy, University Medical Center Utrecht devised a Discrete Vasculature [Kotte et al., Phys. Med. Biol. 41, 865-884 (1996)] model that accounts for the presence of vessel trees in the computational domain. The vessel tree geometry is tracked using magnetic resonance (MR) angiograms to a minimum diameter between 0.6 and 1 mm. However, smaller vessels (0.2-0.6 mm) are known to account for significant heat transfer. The hyperthermia group at Duke University Medical Center has proposed using perfusion maps derived from dynamic-enhanced magnetic resonance imaging to account for the tissue perfusion heterogeneity [Craciunescu et al., Int. J. Hyperthermia 17, 221-239 (2001)]. In addition, techniques for noninvasive temperature measurements have been devised to measure temperatures in vivo [Samulski et al., Int. J. Hypertherminal, 819-829 (1992)]. In this work, a patient with high-grade sarcoma has been retrospectively modeled to determine the temperature distribution achieved during a hyperthermia treatment. Available for this model were MR depicted geometry, angiograms, perfusion maps, as necessary for accurate thermal modeling, as well as MR thermometry data for validation purposes. The vasculature assembly through modifiable potential program [Van Leeuwen et al., IEEE Trans. Biomed. Eng. 45, 596-604 (1998)] was used in order to incorporate the traceable large vessels. Temperature simulations were made using different approaches to describe perfusion. The simulated cases were the bioheat equation with constant perfusion rates per tissue type, perfusion maps alone, tracked vessel tree and perfusion maps, and generated vessel tree. The results were compared with MR thermometry data for a single patient data set, concluding that a combination between large traceable vessels and perfusion map yields the best results for this particular patient. The technique has to be repeated on several patients, first with the same type of malignancy, and after that, on patients having malignancies at other different sites.

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Year:  2001        PMID: 11764035     DOI: 10.1118/1.1408619

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


  10 in total

1.  SAR and temperature: simulations and comparison to regulatory limits for MRI.

Authors:  Zhangwei Wang; James C Lin; Weihua Mao; Wanzhan Liu; Michael B Smith; Christopher M Collins
Journal:  J Magn Reson Imaging       Date:  2007-08       Impact factor: 4.813

2.  Hyperthermia MRI temperature measurement: evaluation of measurement stabilisation strategies for extremity and breast tumours.

Authors:  Cory Wyatt; Brian Soher; Paolo Maccarini; H Cecil Charles; Paul Stauffer; James Macfall
Journal:  Int J Hyperthermia       Date:  2009       Impact factor: 3.914

3.  Correction of breathing-induced errors in magnetic resonance thermometry of hyperthermia using multiecho field fitting techniques.

Authors:  Cory R Wyatt; Brian J Soher; James R MacFall
Journal:  Med Phys       Date:  2010-12       Impact factor: 4.071

4.  Comprehensive analysis of the Cramer-Rao bounds for magnetic resonance temperature change measurement in fat-water voxels using multi-echo imaging.

Authors:  Cory Wyatt; Brian J Soher; Kavitha Arunachalam; James MacFall
Journal:  MAGMA       Date:  2011-03-27       Impact factor: 2.310

Review 5.  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

6.  An approach to rapid calculation of temperature change in tissue using spatial filters to approximate effects of thermal conduction.

Authors:  Giuseppe Carluccio; Danilo Erricolo; Sukhoon Oh; Christopher M Collins
Journal:  IEEE Trans Biomed Eng       Date:  2013-01-22       Impact factor: 4.538

7.  A generic bioheat transfer thermal model for a perfused tissue.

Authors:  Devashish Shrivastava; J Thomas Vaughan
Journal:  J Biomech Eng       Date:  2009-07       Impact factor: 2.097

Review 8.  Modelling of endoluminal and interstitial ultrasound hyperthermia and thermal ablation: applications for device design, feedback control and treatment planning.

Authors:  Punit Prakash; Vasant A Salgaonkar; Chris J Diederich
Journal:  Int J Hyperthermia       Date:  2013-06       Impact factor: 3.914

Review 9.  Thermal modelling using discrete vasculature for thermal therapy: A review.

Authors:  H Petra Kok; Johanna Gellermann; Cornelis A T van den Berg; Paul R Stauffer; Jeffrey W Hand; Johannes Crezee
Journal:  Int J Hyperthermia       Date:  2013-06       Impact factor: 3.914

Review 10.  Current state of the art of regional hyperthermia treatment planning: a review.

Authors:  H P Kok; P Wust; P R Stauffer; F Bardati; G C van Rhoon; J Crezee
Journal:  Radiat Oncol       Date:  2015-09-17       Impact factor: 3.481

  10 in total

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