A Review of MRI-Based Synthetic CT Image Generation
Jian Yingchao, Fu Dongshan*, Wang Wei*
(Tianjin Medical University Cancer Institute and Hospital & National Clinical Research Center for Cancer & Key Laboratory of Cancer Prevention and Therapy, Tianjin & Tianjin′s Clinical Research Center for Cancer,Tianjin 300060,China)
Abstract：Radiation therapy is one of the important ways in tumor treatments,and CT is the main reference image of current radiation therapy. Compared with CT,MRI has good soft tissue contrast and is completely harmless to the human body. In recent years,MRI has been increasingly used for soft tissue delineation and radiation therapy guidance. However,the ionizing radiation of CT scan affects the health of patients,at the same time,scanning CT and MRI increase the economic burden of patients,and the registration fusion of CT and MRI introduces systematic errors.Radiotherapy using MRI only has received more and more attention from researchers. Nevertheless,MRI has no connection with electron density and cannot be directly used for dose calculation and X-ray based patient placement verification,therefore it is necessary to study the correlation algorithm to obtain the electron density information or HU value of the patient tissue according to the MRI image,that is to generate synthetic CT or pseudo CT. Currently existing methods for generating synthetic CT or pseudo CT are classified into three categories,including voxel-based,atlas-based and hybrid methods. According to the applied methods,we summarized and analyzed the sequence images,the number of applications and the anatomical parts. This study may represent a new direction in radiation therapy,which can avoid the use of ionizing radiation from traditional CT,and can use high-resolution MRI images to monitor tumors and enable patients to perform more precise radiation therapy.
菅影超, 付东山, 王伟. 基于MRI的合成CT生成方法综述[J]. 中国生物医学工程学报, 2020, 39(4): 485-492.
Jian Yingchao, Fu Dongshan, Wang Wei. A Review of MRI-Based Synthetic CT Image Generation. Chinese Journal of Biomedical Engineering, 2020, 39(4): 485-492.
[1] Hsu SH,Cao Y,Huang K,et al.Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy[J].Phys Med Biol,2013,58(23):8419-8435. [2] Stanescu T,Jans H,Pervez N,et al.A study on the magnetic resonance imaging (MRI)-based radiation treatment planning of intracranial lesions[J].Phys Med Biol,2008,53(13):3579-3593. [3] Hofmann M,Pichler B,Bernhard S,et al.Towards quantitative PET/MRI:A review of MR-based attenuation correction techniques[J].Eur J Nucl Mol Imaging,2009,36 (Suppl 1):S93-S104. [4] Commandeur F,Simon A,Mathieu R,et al.MRI to CT prostate registration for improved targeting in cancer external beam radiotherapy[J].IEEE J Biomed Health Inform,2017,21(4):1015-1026. [5] Zhang Weili,Kim JP,Mo K,et al.Magnetic resonance-based automatic air segmentation for generation of synthetic computed tomography scans in the head region[J].Int J Radiat Oncol Biol Phys,2015,93(3):497-506. [6] Ulin K,Urie MM,Cherlow JM.Results of a multi-institutional benchmark test for cranial CT/MR image registration[J].Int J Radiat Oncol Biol Phys,2010,77(5):1584-1589. [7] Edmund JM,Kjer HM,Leemput KV,et al.A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times[J].Phys Med Biol,2014,59(23):7501-7519. [8] Chen Lili,Nguyen B,Jones E,et al.Magnetic resonance-based treatment planning for prostate intensity-modulated radiotherapy:creation of digitally reconstructed radiographs[J].Int J Radiat Oncol Biol Phys,2007,68(3):903-911. [9] Sjolund J,Forsberg D,Andersson M,et al.Generating patient specific pseudo-CT of the head from MR using atlas-based regression[J].Phys Med Biol,2015,60(2):825-839. [10] Dowling JA,Lambert J,Parker J,et al.An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy[J].Int J Radiat Oncol Biol Phys,2012,83(1):e5-e11. [11] Demol B,Boydev C,Korhonen J,et al.Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images[J].Med Phys,2016,43(12):6557-6568. [12] Gudur MSR,Hara W,Le QT,et al.A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning[J].Phys Med Biol,2014,59(21):6595-6606. [13] Han Xiao.MR-based synthetic CT generation using a deep convolutional neural network method[J].Med Phys,2017,44(4):1408-1419. [14] Dice LR.Measures of the amount of ecologic association between species[J].J Ecol,1945,26(3):297-302. [15] Low DA,Harms WB,Mutic S,et al.A technique for the quantitative evaluation of dose distributions[J].Med Phys,1998,25(5):656-661. [16] Yu Huan,Caldwell C,Balogh J,et al.Toward magnetic resonance-only simulation:Segmentation of bone in MR for radiation therapy verification of the head[J].Int J Radiation Oncol Biol Phys,2014,89(3):649-657. [17] James A.Photon,electron,proton and neutron interaction data for body tissues:ICRU Report 46[J].J Nucl Med,1993,34(1):171. [18] Rank CM,Hünemohr N,Nagel AM,et al.MRI-based simulation of treatment plans for ion radiotherapy in the brain region[J].Radiother Oncol,2013,109(3):414-418. [19] Rank CM,Tremmel C,Hünemohr N,et al.MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach[J].Radiat Oncol,2013,8(1):1-23. [20] Navalpakkam BK,Braun H,Kuwert T,et al.Magnetic resonance-based attenuation correction for PET/MR hybrid imaging using continuous valued attenuation maps[J].Invest Radiol,2013,48(5):323-332. [21] Johansson A,Karlsson M,Nyholm T.CT substitute derived from MRI sequences with ultrashort echo time.[J].Med Phys,2011,38(5):2708-2714. [22] Johansson A,Garpebring A,Karlsson M,et al.Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information-potential application for MR-only radiotherapy and attenuation correction in positron emission tomography[J].Acta Oncologica,2013,52(7):1369-1373. [23] Chen Yasheng,Juttukonda M,Su Y,et al.Probabilistic air segmentation and sparse regression estimated pseudo CT for PET/MR attenuation correction[J].Radiology,2015,275(2):562-569. [24] Yang Xiaofeng,Yanglei,Shu Huikuo,et al.Pseudo CT estimation from MRI using patch-based random forest[J].Proceedings of SPIE,2017,10133:1-8. [25] Largent A,Nunes JC,Saintjalmes H,et al.Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning[C]//The 25th European Signal Processing Conference.Kos:EURASIP 2017:46-50. [26] Siversson C,Nordstrom F,Nilsson T,et al.Technical Note:MRI only prostate radiotherapy planning using the statistical decomposition algorithm[J].Med Phys,2015,42(10):6090-6097. [27] Nie Dong,Cao Xiaohuan,Gao Yaozong,et al.Estimating CT image from MRI data using 3D fully convolutional networks[C]//International Workshop on Deep Learning in Medical Image Analysis/International Workshop on Large-Scale Annotation of Biomedical Data and Expert.Berlin:Springer International Publishing,2016:170-178. [28] 胡永生,张立毅.基于非局部自相似图像块字典学习的伪CT图像预测[J].信号处理,2017,33(3):346-351. [29] Ronneberger O,Fischer P,Brox T.U-Net:Convolutional networks for biomedical image segmentation[EB/OL].https://www.researchgate.net/publication/276923248,2015-05-18/2019-8-25. [30] Dinkla AM,Wolterink JM,Matteo M,et al.MR-only brain radiation therapy:Dosimetric evaluation of synthetic CTs generated by a dilated convolutional neural network[J].Int J Radiation Oncol Biol Phys,2018,3(1):1-12. [31] Fu Jie,Yang Yingli,Singhrao K,et al.Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging[J].Med Phys,2019,46(9):3788-3798. [32] Emami H,Dong M,Nejad-Davarani SP,et al.Generating synthetic CTs from magnetic resonance images using generative adversarial networks[J].Med Phys,2018,45(8):3627-3636. [33] Kazemifar S,Mcguire S,Timmerman R,et al.MRI-only brain radiotherapy:Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach[J].Radiother Oncol,2019,136:56-63. [34] Prokopenko D,Stadelmann VJ,Schulz H,et al.Synthetic CT generation from MRI using improved dualGAN[EB/OL].https://www.researchgate.net/publication/335926112,2019-09-19/2020-03-25. [35] Maspero M,Savenije MHF,Dinkla AM,et al.Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy[J].Phys Med Biol,2018,63(18):1-17. [36] Fu Jie,Singhrao K,Cao Minsong,et al.Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy[J].Biomed Phys Eng Express,2020,6:1-9. [37] Guerreiro F,Burgos N,Dunlop A,et al.Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning[J].Phys Medica,2017,35:7-17. [38] Schreibmann E,Nye J A,Schuster DM,et al.MR‐based attenuation correction for hybrid PET‐MR brain imaging systems using deformable image registration[J].Med Phys,2010,37(5):2101-2109. [39] Burgos N,Cardoso MJ,Guerreiro F,et al.Robust CT synthesis for radiotherapy planning:Application to the head and neck region[C]//The 18th International Conference on Medical Image Computing and Computer-Assisted Intervention.Munich:Springer International Publishing,2015:476-484. [40] Andreasen D,Van Leemput K,Hansen RH,et al.Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain[J].Med Physics,2015,42(4):1596-1605. [41] Andreasen D,Van Leemput K,Edmund JM.A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis[J].Med Phys,2016,43(8):4742-4752. [42] Torrado-Carvajal A,Alcain E,Montemayor AS,et al.Fast pseudo-CT synthesis from MRI T1-weighted images using a patch-based approach[J].Proc of SPIE,2015,9681:1-7. [43] Uh J,Merchant TE,Li Y,et al.MRI-based treatment planning with pseudo CT generated through atlas registration[J].Med Phys,2014,41(5):1-8. [44] Hofmann M,Steinke F,Scheel V,et al.MRI-based attenuation correction for PET/MRI:A novel approach combining pattern recognition and atlas registration[J].J Nucl Med,2008,49(11):1875-1883. [45] Merida I,Costes N,Heckemann R,et al.Pseudo-CT generation in brain MR-PET attenuation correction:comparison of several multi-atlas methods[J].EJNMMI Physics,2015,2(Suppl 1):A29. [46] Ren S,Hara W,Wang Lei,et al.Robust estimation of electron density from anatomic magnetic resonance imaging of the brain using a unifying multi-atlas approach[J].Int J Radiation Oncol Biol Phys,2017,97(4):849-857. [47] Koivula L,Wee L,Korhonen J.Feasibility of MRI-only treatment planning for proton therapy in brain and prostate cancers:Dose calculation accuracy in substitute CT images[J].Med Phys,2016,43(8):4634-4642. [48] Robson MD,Gatehouse PD,Bydder M,et al.Magnetic resonance:An introduction to ultrashort TE (UTE) imaging[J].J Comput Assist Tomo,2003,27(6):825-846. [49] Keereman V,Fierens Y,Broux T,et al.MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences[J].J Nucl Med,2010,51(5):812-818. [50] Korhonen J,Kapanen M,Keyrilainen J,et al.A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer[J].Med Phys,2014,41(1):1-12.
