Literature DB >> 12648793

Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone.

Young K Lee1, Marc Bollet, Geoffrey Charles-Edwards, Maggie A Flower, Martin O Leach, Helen McNair, Elizabeth Moore, Carl Rowbottom, Steve Webb.   

Abstract

PURPOSE: Accurate anatomical delineation of the gross tumour volume (GTV) is crucial for effective radiotherapy (RT) treatment of prostate cancers. Although reference to pelvic magnetic resonance (MR) for improved delineation of the prostate is a regular practice in some clinics, MR has not replaced CT due to its geometrical distortions and lack of electron-density information. The possibility and practicality of using MR only for RT treatment planning were studied.
MATERIALS AND METHODS: The addition of electron-density information to MR images for conformal radiotherapy (CRT) planning of the prostate was quantified by comparing dose distributions created on the homogeneous density- and bulk-density assigned images to original CT for four patients. To quantify the MR geometrical distortions measurements of a phantom imaged in CT (Siemens Somatom Plus 4) and FLASH 3D T1-weighted MR (1.5 T whole body Siemens Magnetom Vision) were compared. Dose statistics from CRT treatment plans made on CT and MR for five patient data were compared to determine if MR-only treatment plans can be made.
RESULTS: The differences between dose-plans on bulk-density assigned images when compared to CT were less than 2% when water and bone values were assigned. Dose differences greater than 2% were observed when images of homogeneous-density assignment were compared to the CT. Phantom measurements showed that the distortions in the FLASH 3D T1-weighted MR averaged 2 mm in the volume of interest for prostate RT planning. For the CT and MR prostate planning study, doses delivered to the planning target volume (PTV) in CT and MR were always inside a 93-107% dose range normalised to the isocentre. Also, the doses to the organs-at-risk in the MR images were similar to the doses delivered to the volumes in the registered CT image when the organ volumes between the two images were similar.
CONCLUSIONS: Negligible differences were observed in dose distribution between CRT plans using bone+water CT number bulk-assigned image and original CT. Also, the MR distortions were reduced to negligible amounts using large bandwidth MR sequence for prostate CRT planning. MR treatment planning was demonstrated using a large bandwidth sequence and bulk-assigned images. The development of higher quality, low distortion MR sequence will allow regular practice of this technique.

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Year:  2003        PMID: 12648793     DOI: 10.1016/s0167-8140(02)00440-1

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  51 in total

1.  Assessing the image quality of pelvic MR images acquired with a flat couch for radiotherapy treatment planning.

Authors:  M McJury; A O'Neill; M Lawson; C McGrath; A Grey; W Page; J M O'Sullivan
Journal:  Br J Radiol       Date:  2011-08       Impact factor: 3.039

2.  Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI.

Authors:  Nasr Makni; P Puech; R Lopes; A S Dewalle; O Colot; N Betrouni
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-12-03       Impact factor: 2.924

3.  MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method.

Authors:  Tonghe Wang; Nivedh Manohar; Yang Lei; Anees Dhabaan; Hui-Kuo Shu; Tian Liu; Walter J Curran; Xiaofeng Yang
Journal:  Med Dosim       Date:  2018-08-14       Impact factor: 1.482

4.  Registration accuracy for MR images of the prostate using a subvolume based registration protocol.

Authors:  Joakim H Jonsson; Patrik Brynolfsson; Anders Garpebring; Mikael Karlsson; Karin Söderström; Tufve Nyholm
Journal:  Radiat Oncol       Date:  2011-06-16       Impact factor: 3.481

5.  Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

Authors:  Ghazal Shafai-Erfani; Tonghe Wang; Yang Lei; Sibo Tian; Pretesh Patel; Ashesh B Jani; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Dosim       Date:  2019-02-01       Impact factor: 1.482

6.  Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions.

Authors:  Joakim H Jonsson; Magnus G Karlsson; Mikael Karlsson; Tufve Nyholm
Journal:  Radiat Oncol       Date:  2010-06-30       Impact factor: 3.481

7.  MRI-based treatment planning with pseudo CT generated through atlas registration.

Authors:  Jinsoo Uh; Thomas E Merchant; Yimei Li; Xingyu Li; Chiaho Hua
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

8.  An evaluation of four CT-MRI co-registration techniques for radiotherapy treatment planning of prone rectal cancer patients.

Authors:  C J Dean; J R Sykes; R A Cooper; P Hatfield; B Carey; S Swift; S E Bacon; D Thwaites; D Sebag-Montefiore; A M Morgan
Journal:  Br J Radiol       Date:  2012-01       Impact factor: 3.039

Review 9.  MRI-only treatment planning: benefits and challenges.

Authors:  Amir M Owrangi; Peter B Greer; Carri K Glide-Hurst
Journal:  Phys Med Biol       Date:  2018-02-26       Impact factor: 3.609

10.  Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy.

Authors:  Shu-Hui Hsu; Yue Cao; Ke Huang; Mary Feng; James M Balter
Journal:  Phys Med Biol       Date:  2013-11-11       Impact factor: 3.609

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