Literature DB >> 25393873

A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times.

Jens M Edmund1, Hans M Kjer, Koen Van Leemput, Rasmus H Hansen, Jon A L Andersen, Daniel Andreasen.   

Abstract

Radiotherapy (RT) based on magnetic resonance imaging (MRI) as the only modality, so-called MRI-only RT, would remove the systematic registration error between MR and computed tomography (CT), and provide co-registered MRI for assessment of treatment response and adaptive RT. Electron densities, however, need to be assigned to the MRI images for dose calculation and patient setup based on digitally reconstructed radiographs (DRRs). Here, we investigate the geometric and dosimetric performance for a number of popular voxel-based methods to generate a so-called pseudo CT (pCT). Five patients receiving cranial irradiation, each containing a co-registered MRI and CT scan, were included. An ultra short echo time MRI sequence for bone visualization was used. Six methods were investigated for three popular types of voxel-based approaches; (1) threshold-based segmentation, (2) Bayesian segmentation and (3) statistical regression. Each approach contained two methods. Approach 1 used bulk density assignment of MRI voxels into air, soft tissue and bone based on logical masks and the transverse relaxation time T2 of the bone. Approach 2 used similar bulk density assignments with Bayesian statistics including or excluding additional spatial information. Approach 3 used a statistical regression correlating MRI voxels with their corresponding CT voxels. A similar photon and proton treatment plan was generated for a target positioned between the nasal cavity and the brainstem for all patients. The CT agreement with the pCT of each method was quantified and compared with the other methods geometrically and dosimetrically using both a number of reported metrics and introducing some novel metrics. The best geometrical agreement with CT was obtained with the statistical regression methods which performed significantly better than the threshold and Bayesian segmentation methods (excluding spatial information). All methods agreed significantly better with CT than a reference water MRI comparison. The mean dosimetric deviation for photons and protons compared to the CT was about 2% and highest in the gradient dose region of the brainstem. Both the threshold based method and the statistical regression methods showed the highest dosimetrical agreement.Generation of pCTs using statistical regression seems to be the most promising candidate for MRI-only RT of the brain. Further, the total amount of different tissues needs to be taken into account for dosimetric considerations regardless of their correct geometrical position.

Entities:  

Mesh:

Year:  2014        PMID: 25393873     DOI: 10.1088/0031-9155/59/23/7501

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


  22 in total

1.  Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.

Authors:  Yang Lei; Hui-Kuo Shu; Sibo Tian; Jiwoong Jason Jeong; Tian Liu; Hyunsuk Shim; Hui Mao; Tonghe Wang; Ashesh B Jani; Walter J Curran; Xiaofeng Yang
Journal:  J Med Imaging (Bellingham)       Date:  2018-08-24

2.  Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy.

Authors:  Reza Farjam; Neelam Tyagi; Harini Veeraraghavan; Aditya Apte; Kristen Zakian; Margie A Hunt; Joseph O Deasy
Journal:  Med Phys       Date:  2017-06-01       Impact factor: 4.071

Review 3.  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

4.  MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model.

Authors:  Yang Lei; Jiwoong Jason Jeong; Tonghe Wang; Hui-Kuo Shu; Pretesh Patel; Sibo Tian; Tian Liu; Hyunsuk Shim; Hui Mao; Ashesh B Jani; Walter J Curran; Xiaofeng Yang
Journal:  J Med Imaging (Bellingham)       Date:  2018-12-05

5.  MRI-based synthetic CT generation using semantic random forest with iterative refinement.

Authors:  Yang Lei; Joseph Harms; Tonghe Wang; Sibo Tian; Jun Zhou; Hui-Kuo Shu; Jim Zhong; Hui Mao; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2019-04-05       Impact factor: 3.609

Review 6.  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

7.  Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering.

Authors:  Kuan-Hao Su; Lingzhi Hu; Christian Stehning; Michael Helle; Pengjiang Qian; Cheryl L Thompson; Gisele C Pereira; David W Jordan; Karin A Herrmann; Melanie Traughber; Raymond F Muzic; Bryan J Traughber
Journal:  Med Phys       Date:  2015-08       Impact factor: 4.071

8.  Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy.

Authors:  Yuhei Koike; Yuichi Akino; Iori Sumida; Hiroya Shiomi; Hirokazu Mizuno; Masashi Yagi; Fumiaki Isohashi; Yuji Seo; Osamu Suzuki; Kazuhiko Ogawa
Journal:  J Radiat Res       Date:  2020-01-23       Impact factor: 2.724

9.  Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors.

Authors:  Samaneh Kazemifar; Ana M Barragán Montero; Kevin Souris; Sara T Rivas; Robert Timmerman; Yang K Park; Steve Jiang; Xavier Geets; Edmond Sterpin; Amir Owrangi
Journal:  J Appl Clin Med Phys       Date:  2020-03-26       Impact factor: 2.102

10.  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
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.