Literature DB >> 24803040

Toward magnetic resonance-only simulation: segmentation of bone in MR for radiation therapy verification of the head.

Huan Yu1, Curtis Caldwell2, Judith Balogh3, Katherine Mah4.   

Abstract

PURPOSE: To develop a practical method to localize bones in magnetic resonance (MR) images, to create "computed tomography-like" MR images (ctMRI) that could be used for radiation therapy verification, and to generate MR-based digitally reconstructed radiographs (DRR). METHODS AND MATERIALS: Using T1-weighted MR images, an air mask was derived from the manual contouring of all airways within the head and neck region using axial images at 6 anatomic levels. Compact bone, spongy bone, and soft tissue masks were then automatically generated using the statistical data derived from MR intensities and the air mask. ctMRI were then generated by mapping the MR intensities of the voxels within these masks into the CT number ranges of corresponding tissues. MR-based DRRs created from ctMRI were quantitatively evaluated using the co-registered MR and CT head images of 20 stereotactic radiosurgery patients. Ten anatomical points, positioned on the skull segmented using a threshold of 300 HU, in CT and ctMRI, were used to determine the differences in distance between MR-based DRRs and CT-based DRRs, and to evaluate the geometric accuracy of ctMRI and MR-based DRRs.
RESULTS: The bony structures were identified on ctMRI and were visible in the MR-based DRRs. From the 20 patient cases, the mean geometric difference and standard deviation between the 10 anatomical points on MR-based and CT-based DRRs was -0.05 ± 0.85 mm, respectively. This included uncertainty in image fusion. The maximum distance difference was 1.88 mm.
CONCLUSIONS: A practical method was developed to segment bone from MR images. The ctMRI created can be used for radiation treatment verification when MR-only simulation is performed. MR-based DRRs can be used in place of CT-based DRRs. Crown
Copyright © 2014. Published by Elsevier Inc. All rights reserved.

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Year:  2014        PMID: 24803040     DOI: 10.1016/j.ijrobp.2014.03.028

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  12 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.  k-space sampling optimization for ultrashort TE imaging of cortical bone: applications in radiation therapy planning and MR-based PET attenuation correction.

Authors:  Lingzhi Hu; Kuan-Hao Su; Gisele C Pereira; Anu Grover; Bryan Traughber; Melanie Traughber; Raymond F Muzic
Journal:  Med Phys       Date:  2014-10       Impact factor: 4.071

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.  Image Guided Radiation Therapy Using Synthetic Computed Tomography Images in Brain Cancer.

Authors:  Ryan G Price; Joshua P Kim; Weili Zheng; Indrin J Chetty; Carri Glide-Hurst
Journal:  Int J Radiat Oncol Biol Phys       Date:  2016-03-10       Impact factor: 7.038

5.  Accuracy of UTE-MRI-based patient setup for brain cancer radiation therapy.

Authors:  Yingli Yang; Minsong Cao; Tania Kaprealian; Ke Sheng; Yu Gao; Fei Han; Caitlin Gomez; Anand Santhanam; Stephen Tenn; Nzhde Agazaryan; Daniel A Low; Peng Hu
Journal:  Med Phys       Date:  2016-01       Impact factor: 4.071

6.  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

7.  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

Review 8.  A review of substitute CT generation for MRI-only radiation therapy.

Authors:  Jens M Edmund; Tufve Nyholm
Journal:  Radiat Oncol       Date:  2017-01-26       Impact factor: 3.481

9.  Assessment of MRI-Based Attenuation Correction for MRI-Only Radiotherapy Treatment Planning of the Brain.

Authors:  Iiro Ranta; Jarmo Teuho; Jani Linden; Riku Klén; Mika Teräs; Mika Kapanen; Jani Keyriläinen
Journal:  Diagnostics (Basel)       Date:  2020-05-14

10.  MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning.

Authors:  Xiangyu Ma; Xinyuan Chen; Jingwen Li; Yu Wang; Kuo Men; Jianrong Dai
Journal:  Front Oncol       Date:  2021-09-08       Impact factor: 6.244

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