Literature DB >> 26371425

A new methodology for inter- and intrafraction plan adaptation for the MR-linac.

Charis Kontaxis1, G H Bol, J J W Lagendijk, B W Raaymakers.   

Abstract

The new era of hybrid MRI and linear accelerator machines, including the MR-linac currently being installed in the University Medical Center Utrecht (Utrecht, The Netherlands), will be able to provide the actual anatomy and real-time anatomy changes of the patient's target(s) and organ(s) at risk (OARs) during radiation delivery. In order to be able to take advantage of this input, a new generation of treatment planning systems is needed, that will allow plan adaptation to the latest anatomy state in an online regime. In this paper, we present a treatment planning algorithm for intensity-modulated radiotherapy (IMRT), which is able to compensate for patient anatomy changes. The system consists of an iterative sequencing loop open to anatomy updates and an inter- and intrafraction adaptation scheme that enables convergence to the ideal dose distribution without the need of a final segment weight optimization (SWO). The ability of the system to take into account organ motion and adapt the plan to the latest anatomy state is illustrated using artificial baseline shifts created for three different kidney cases. Firstly, for two kidney cases of different target volumes, we show that the system can account for intrafraction motion, delivering the intended dose to the target with minimal dose deposition to the surroundings compared to conventional plans. Secondly, for a third kidney case we show that our algorithm combined with the interfraction scheme can be used to deliver the prescribed dose while adapting to the changing anatomy during multi-fraction treatments without performing a final SWO.

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Year:  2015        PMID: 26371425     DOI: 10.1088/0031-9155/60/19/7485

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  23 in total

Review 1.  MR-guided radiation therapy: transformative technology and its role in the central nervous system.

Authors:  Yue Cao; Chia-Lin Tseng; James M Balter; Feifei Teng; Hemant A Parmar; Arjun Sahgal
Journal:  Neuro Oncol       Date:  2017-04-01       Impact factor: 12.300

Review 2.  Magnetic resonance image guidance in external beam radiation therapy planning and delivery.

Authors:  Ilamurugu Arivarasan; Chandrasekaran Anuradha; Shanmuga Subramanian; Ayyalusamy Anantharaman; Velayudham Ramasubramanian
Journal:  Jpn J Radiol       Date:  2017-06-13       Impact factor: 2.374

3.  A conceptual study on real-time adaptive radiation therapy optimization through ultra-fast beamlet control.

Authors:  Rodney D Wiersma; Xinmin Liu
Journal:  Biomed Phys Eng Express       Date:  2019-08-30

Review 4.  Integrated MRI-guided radiotherapy - opportunities and challenges.

Authors:  Paul J Keall; Caterina Brighi; Carri Glide-Hurst; Gary Liney; Paul Z Y Liu; Suzanne Lydiard; Chiara Paganelli; Trang Pham; Shanshan Shan; Alison C Tree; Uulke A van der Heide; David E J Waddington; Brendan Whelan
Journal:  Nat Rev Clin Oncol       Date:  2022-04-19       Impact factor: 65.011

Review 5.  Magnetic resonance imaging in precision radiation therapy for lung cancer.

Authors:  Hannah Bainbridge; Ahmed Salem; Rob H N Tijssen; Michael Dubec; Andreas Wetscherek; Corinne Van Es; Jose Belderbos; Corinne Faivre-Finn; Fiona McDonald
Journal:  Transl Lung Cancer Res       Date:  2017-12

Review 6.  The transformation of radiation oncology using real-time magnetic resonance guidance: A review.

Authors:  William A Hall; Eric S Paulson; Uulke A van der Heide; Clifton D Fuller; B W Raaymakers; Jan J W Lagendijk; X Allen Li; David A Jaffray; Laura A Dawson; Beth Erickson; Marcel Verheij; Kevin J Harrington; Arjun Sahgal; Percy Lee; Parag J Parikh; Michael F Bassetti; Clifford G Robinson; Bruce D Minsky; Ananya Choudhury; Robert J H A Tersteeg; Christopher J Schultz
Journal:  Eur J Cancer       Date:  2019-10-12       Impact factor: 9.162

7.  Investigation of dosimetric variations of liver radiotherapy using deformable registration of planning CT and cone-beam CT.

Authors:  Pu Huang; Gang Yu; Jinhu Chen; Changsheng Ma; Shaohua Qin; Yong Yin; Yueqiang Liang; Hongsheng Li; Dengwang Li
Journal:  J Appl Clin Med Phys       Date:  2016-12-05       Impact factor: 2.102

8.  MR-based treatment planning in radiation therapy using a deep learning approach.

Authors:  Fang Liu; Poonam Yadav; Andrew M Baschnagel; Alan B McMillan
Journal:  J Appl Clin Med Phys       Date:  2019-03       Impact factor: 2.102

Review 9.  Roadmap: proton therapy physics and biology.

Authors:  Harald Paganetti; Chris Beltran; Stefan Both; Lei Dong; Jacob Flanz; Keith Furutani; Clemens Grassberger; David R Grosshans; Antje-Christin Knopf; Johannes A Langendijk; Hakan Nystrom; Katia Parodi; Bas W Raaymakers; Christian Richter; Gabriel O Sawakuchi; Marco Schippers; Simona F Shaitelman; B K Kevin Teo; Jan Unkelbach; Patrick Wohlfahrt; Tony Lomax
Journal:  Phys Med Biol       Date:  2021-02-26       Impact factor: 4.174

Review 10.  Medical physics challenges in clinical MR-guided radiotherapy.

Authors:  Christopher Kurz; Giulia Buizza; Guillaume Landry; Florian Kamp; Moritz Rabe; Chiara Paganelli; Guido Baroni; Michael Reiner; Paul J Keall; Cornelis A T van den Berg; Marco Riboldi
Journal:  Radiat Oncol       Date:  2020-05-05       Impact factor: 3.481

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