Literature DB >> 27908187

Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images.

Benjamin Demol1, Christine Boydev2, Juha Korhonen3, Nick Reynaert2.   

Abstract

PURPOSE: Magnetic resonance imaging (MRI)-only radiotherapy treatment planning requires accurate pseudo-CT (pCT) images for precise dose calculation. The current work introduced an atlas-based method combined with MR intensity information. pCT analyses and Monte Carlo dose calculations for intracranial stereotactic treatments were performed.
METHODS: Twenty-two patients, representing 35 tumor targets, were scanned using a 3D T1-weighted MRI sequence according to the clinical protocol. The MR atlas image was registered to the MR patient image using a deformable algorithm, and the deformation was then applied to the atlas CT. Two methods were applied. The first method (MRdef) was based on deformations only, while the second (MRint) also used the actual MR intensities. pCT analysis was performed using the mean (absolute) error, as well as an in-house tool based on a gamma index. Dose differences between pCT and true CT were analyzed using dose-volume histogram (DVH) parameters, statistical tests, the gamma index, and probability density functions. An unusual case, where the patient underwent an operation (part of the skull bone was removed), was studied in detail.
RESULTS: Soft tissues presented a mean error inferior to 50 HUs, while low-density tissues and bones presented discrepancies up to 600 HUs for hard bone. The MRdef method led to significant dose differences compared with the true CT (p-value < 0.05; Wilcoxon-signed-rank test). The MRint method performed better. The DVH parameter differences compared with CT were between -2.9% and 3.1%, except for two cases where the tumors were located within the sphenoid bone. For these cases, the dose errors were up to 6.6% and 5.4% (D98 and D95). Furthermore, for 85% of the tested patients, the mean dose to the planning target volume agreed within 2% with the calculation using the actual CT. Fictitious bone was generated in the unusual case using atlas-based methods.
CONCLUSIONS: Generally, the atlas-based method led to acceptable dose distributions. The use of common T1 sequences allows the implementation of this method in clinical routine. However, unusual patient anatomy may produce large dose calculation errors. The detection of large anatomic discrepancies using MR image subtraction can be realized, but an alternative way to produce synthetic CT numbers in these regions is still required.

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Mesh:

Year:  2016        PMID: 27908187     DOI: 10.1118/1.4967480

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


  13 in total

Review 1.  Emerging role of MRI in radiation therapy.

Authors:  Hersh Chandarana; Hesheng Wang; R H N Tijssen; Indra J Das
Journal:  J Magn Reson Imaging       Date:  2018-09-08       Impact factor: 4.813

2.  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

Review 3.  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

4.  Generating synthetic CTs from magnetic resonance images using generative adversarial networks.

Authors:  Hajar Emami; Ming Dong; Siamak P Nejad-Davarani; Carri K Glide-Hurst
Journal:  Med Phys       Date:  2018-06-14       Impact factor: 4.071

5.  Using synthetic CT for partial brain radiation therapy: Impact on image guidance.

Authors:  Eric D Morris; Ryan G Price; Joshua Kim; Lonni Schultz; M Salim Siddiqui; Indrin Chetty; Carri Glide-Hurst
Journal:  Pract Radiat Oncol       Date:  2018-04-06

6.  Feasibility of generating synthetic CT from T1-weighted MRI using a linear mixed-effects regression model.

Authors:  Anant Pandey; Yoganathan Sa; Beibei Guo; Rui Zhang
Journal:  Biomed Phys Eng Express       Date:  2019-06-24

7.  MRI-Based Proton Treatment Planning for Base of Skull Tumors.

Authors:  Ghazal Shafai-Erfani; Yang Lei; Yingzi Liu; Yinan Wang; Tonghe Wang; Jim Zhong; Tian Liu; Mark McDonald; Walter J Curran; Jun Zhou; Hui-Kuo Shu; Xiaofeng Yang
Journal:  Int J Part Ther       Date:  2019-09-30

8.  Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning.

Authors:  Niloofar Yousefi Moteghaed; Ahmad Mostaar; Keivan Maghooli; Mohammad Houshyari; Ahmad Ameri
Journal:  Rep Pract Oncol Radiother       Date:  2020-07-11

9.  Effects of different CT value assignment methods on dose calculations in radiotherapy planning for brain metastases.

Authors:  Jianxin Ren; Guanzhong Gong; Xingmin Ma; Xinsen Yao; Yong Yin
Journal:  Transl Cancer Res       Date:  2020-02       Impact factor: 1.241

10.  Impact of geometric distortion on dose deviation for photon and proton treatment plans.

Authors:  Yue Yan; Jinzhong Yang; Yuting Li; Yao Ding; Mo Kadbi; Jihong Wang
Journal:  J Appl Clin Med Phys       Date:  2022-02-02       Impact factor: 2.102

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