Literature DB >> 25832050

Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain.

Daniel Andreasen1, Koen Van Leemput2, Rasmus H Hansen3, Jon A L Andersen4, Jens M Edmund4.   

Abstract

PURPOSE: In radiotherapy (RT) based on magnetic resonance imaging (MRI) as the only modality, the information on electron density must be derived from the MRI scan by creating a so-called pseudo computed tomography (pCT). This is a nontrivial task, since the voxel-intensities in an MRI scan are not uniquely related to electron density. To solve the task, voxel-based or atlas-based models have typically been used. The voxel-based models require a specialized dual ultrashort echo time MRI sequence for bone visualization and the atlas-based models require deformable registrations of conventional MRI scans. In this study, we investigate the potential of a patch-based method for creating a pCT based on conventional T1-weighted MRI scans without using deformable registrations. We compare this method against two state-of-the-art methods within the voxel-based and atlas-based categories.
METHODS: The data consisted of CT and MRI scans of five cranial RT patients. To compare the performance of the different methods, a nested cross validation was done to find optimal model parameters for all the methods. Voxel-wise and geometric evaluations of the pCTs were done. Furthermore, a radiologic evaluation based on water equivalent path lengths was carried out, comparing the upper hemisphere of the head in the pCT and the real CT. Finally, the dosimetric accuracy was tested and compared for a photon treatment plan.
RESULTS: The pCTs produced with the patch-based method had the best voxel-wise, geometric, and radiologic agreement with the real CT, closely followed by the atlas-based method. In terms of the dosimetric accuracy, the patch-based method had average deviations of less than 0.5% in measures related to target coverage.
CONCLUSIONS: We showed that a patch-based method could generate an accurate pCT based on conventional T1-weighted MRI sequences and without deformable registrations. In our evaluations, the method performed better than existing voxel-based and atlas-based methods and showed a promising potential for RT of the brain based only on MRI.

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Year:  2015        PMID: 25832050     DOI: 10.1118/1.4914158

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


  37 in total

1.  mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

Authors:  Pengjiang Qian; Yangyang Chen; Jung-Wen Kuo; Yu-Dong Zhang; Yizhang Jiang; Kaifa Zhao; Rose Al Helo; Harry Friel; Atallah Baydoun; Feifei Zhou; Jin Uk Heo; Norbert Avril; Karin Herrmann; Rodney Ellis; Bryan Traughber; Robert S Jones; Shitong Wang; Kuan-Hao Su; Raymond F Muzic
Journal:  IEEE Trans Med Imaging       Date:  2019-08-16       Impact factor: 10.048

2.  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

3.  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

4.  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

5.  Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning.

Authors:  Junghoon Lee; Aaron Carass; Amod Jog; Can Zhao; Jerry L Prince
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-02-24

6.  MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

Authors:  Paul Kyu Han; Debra E Horng; Kuang Gong; Yoann Petibon; Kyungsang Kim; Quanzheng Li; Keith A Johnson; Georges El Fakhri; Jinsong Ouyang; Chao Ma
Journal:  Med Phys       Date:  2020-05-11       Impact factor: 4.071

7.  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

8.  Pseudo CT Estimation from MRI Using Patch-based Random Forest.

Authors:  Xiaofeng Yang; Yang Lei; Hui-Kuo Shu; Peter Rossi; Hui Mao; Hyunsuk Shim; Walter J Curran; Tian Liu
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-02

9.  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

10.  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

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