Literature DB >> 26460991

Magnetic Resonance-Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region.

Weili Zheng1, Joshua P Kim1, Mo Kadbi2, Benjamin Movsas1, Indrin J Chetty1, Carri K Glide-Hurst3.   

Abstract

PURPOSE: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. METHODS AND MATERIALS: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis.
RESULTS: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone-air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort.
CONCLUSIONS: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain.
Copyright © 2015 Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 26460991     DOI: 10.1016/j.ijrobp.2015.07.001

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


  19 in total

1.  Zero TE MRI for Craniofacial Bone Imaging.

Authors:  A Lu; K R Gorny; M-L Ho
Journal:  AJNR Am J Neuroradiol       Date:  2019-09       Impact factor: 3.825

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

3.  Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach.

Authors:  Shangjie Ren; Wendy Hara; Lei Wang; Mark K Buyyounouski; Quynh-Thu Le; Lei Xing; Ruijiang Li
Journal:  Int J Radiat Oncol Biol Phys       Date:  2016-12-14       Impact factor: 7.038

4.  Female pelvic synthetic CT generation based on joint intensity and shape analysis.

Authors:  Lianli Liu; Shruti Jolly; Yue Cao; Karen Vineberg; Jeffrey A Fessler; James M Balter
Journal:  Phys Med Biol       Date:  2017-03-17       Impact factor: 3.609

Review 5.  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

6.  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

7.  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

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.  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

10.  Assessing the Dosimetric Accuracy of Magnetic Resonance-Generated Synthetic CT Images for Focal Brain VMAT Radiation Therapy.

Authors:  Eric Paradis; Yue Cao; Theodore S Lawrence; Christina Tsien; Mary Feng; Karen Vineberg; James M Balter
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-09-04       Impact factor: 7.038

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