Generating Synthetic Computed Tomography and Synthetic Magnetic Resonance (sMR: sT1w/sT2w) Images of the Brain Using Atlas-Based Method

Document Type : Original Paper


1 Department of Medical Physics, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

2 Radiation Therapy and Medical Physics Department, Golestan Hospital, Jundishapur University of Medical Sciences, Ahvaz, Iran

3 Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.


Introduction: Nowadays, magnetic resonance imaging (MRI) in combination with computed-tomography (CT) is increasingly being used in radiation therapy planning. MR and CT images are applied to determine the target volume and calculate dose distribution, respectively. Since the use of these two imaging modalities causes registration uncertainty and increases department workload and costs, in this study, brain synthetic CT (sCT) and synthetic MR (sMR: sT1w/sT2w) images were generated using Atlas-based method; consequently, just one type of image (CT or MR) is taken from the patient.
Material and Methods: The dataset included MR and CT paired images from 10 brain radiotherapy (RT) patients. To generate sCT/sMR images, first each MR/CT Atlas was registered to the MR/CT target image, the resulting transformation was applied to the corresponding CT/MR Atlas, which created the set of deformed images. Then, the deformed images were fused to generate a single sCT/sMR image, and finally, the sCT/sMR images were compared to the real CT/MR images using the mean absolute error (MAE).
Results: The results showed that the MAE of sMR (sT1w/sT2w) was less than that of sCT images. Moreover, sCT images based on T1w were in better agreement with real CT than sCT-based T2w. In addition, sT1w images represented a lower MAE relative to sT2w.
Conclusion: The CT target image was more successful in transferring the geometry of the brain tissues to the synthetic image than MR target.


Main Subjects

  1. References


    1. Khoo V, Joon D. New developments in MRI for target volume delineation in radiotherapy. The British journal of radiology. 2006;79(1):S2-S15.
    2. Sciarra A, Barentsz J, Bjartell A, Eastham J, Hricak H, Panebianco V, et al. Advances in magnetic resonance imaging: how they are changing the management of prostate cancer. European urology. 2011;59(6):962-77.
    3. Gustafsson C, Nordström F, Persson E, Brynolfsson J, Olsson L. Assessment of dosimetric impact of system specific geometric distortion in an MRI only based radiotherapy workflow for prostate. Physics in Medicine & Biology. 2017;62(8):2976.
    4. Uh J, Merchant TE, Li Y, Li X, Hua C. MRI‐based treatment planning with pseudo CT generated through atlas registration. Medical physics. 2014;41(5).
    5. Seitz M, Shukla-Dave A, Bjartell A, Touijer K, Sciarra A, Bastian PJ, et al. Functional magnetic resonance imaging in prostate cancer. European urology. 2009;55(4):801-14.
    6. Rodriguez A. Principles of magnetic resonance imaging. Revista mexicana de física. 2004;50(3):272-86.
    7. Van Reeth E, Tham IW, Tan CH, Poh CL. Super‐resolution in magnetic resonance imaging: A review. Concepts in Magnetic Resonance Part A. 2012;40(6):306-25.
    8. Saboori M, Ahmadi J, Farajzadegan Z. Indications for brain CT scan in patients with minor head injury. Clinical neurology and neurosurgery. 2007;109(5):399-405.
    9. Noyola DE, Demmler GJ, Nelson CT, Griesser C, Williamson WD, Atkins JT, et al. Early predictors of neurodevelopmental outcome in symptomatic congenital cytomegalovirus infection. The Journal of pediatrics. 2001;138(3):325-31.
    10. Von Kummer R, Bourquain H, Bastianello S, Bozzao L, Manelfe C, Meier D, et al. Early prediction of irreversible brain damage after ischemic stroke at CT. Radiology. 2001;219(1):95-100.
    11. Hyatt AP. Computed tomography: physical principles, clinical applications, and quality control. Radiography. 2009;15(4):357-8.
    12. Korhonen J, Kapanen M, Keyriläinen J, Seppälä T, Tenhunen M. A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI‐based radiotherapy treatment planning of prostate cancer. Medical physics. 2014;41(1).
    13. Mazzara GP, Velthuizen RP, Pearlman JL, Greenberg HM, Wagner H. Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. International Journal of Radiation Oncology• Biology• Physics. 2004;59(1):300-12.
    14. Eilertsen K, Nilsen Tor Arne Vestad L, Geier O, Skretting A. A simulation of MRI based dose calculations on the basis of radiotherapy planning CT images. Acta Oncologica. 2008;47(7):1294-302.
    15. Andreasen D, Van Leemput K, Hansen RH, Andersen JA, Edmund JM. Patch‐based generation of a pseudo CT from conventional MRI sequences for MRI‐only radiotherapy of the brain. Medical physics. 2015;42(4):1596-605.
    16. Dowling JA, Lambert J, Parker J, Salvado O, Fripp J, Capp A, et al. An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. International Journal of Radiation Oncology• Biology• Physics. 2012;83(1):e5-e11.
    17. Lee YK, Bollet M, Charles-Edwards G, Flower MA, Leach MO, McNair H, et al. Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone. Radiotherapy and oncology. 2003;66(2):203-16.
    18. Johansson A, Karlsson M, Yu J, Asklund T, Nyholm T. Voxel‐wise uncertainty in CT substitute derived from MRI. Medical physics. 2012;39(6Part1):3283-90.
    19. Sjölund J, Forsberg D, Andersson M, Knutsson H. Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Physics in Medicine & Biology. 2015;60(2):825.
    20. Pennec X, Cachier P, Ayache N, editors. Understanding the “demon’s algorithm”: 3D non-rigid registration by gradient descent. International Conference on Medical Image Computing and Computer-Assisted Intervention; 1999: Springer.
    21. Cahill ND, Noble JA, Hawkes DJ, editors. A demons algorithm for image registration with locally adaptive regularization. International Conference on Medical Image Computing and Computer-Assisted Intervention; 2009: Springer.
    22. Edmund JM, Nyholm T. A review of substitute CT generation for MRI-only radiation therapy. Radiation Oncology. 2017;12(1):28.
    23. Hsu S-H, Cao Y, Huang K, Feng M, Balter JM. Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy. Physics in Medicine & Biology. 2013;58(23):8419.
    24. Stanescu T, Jans H, Pervez N, Stavrev P, Fallone B. A study on the magnetic resonance imaging (MRI)-based radiation treatment planning of intracranial lesions. Physics in Medicine & Biology. 2008;53(13):3579.
    25. Schreibmann E, Nye JA, Schuster DM, Martin DR, Votaw J, Fox T. MR‐based attenuation correction for hybrid PET‐MR brain imaging systems using deformable image registration. Medical physics. 2010;37(5):2101-9.
    26. Kops ER, Hautzel H, Herzog H, Antoch G, Shah NJ. Comparison of template-based versus CT-based attenuation correction for hybrid MR/PET scanners. IEEE Transactions on Nuclear Science. 2015;62(5):2115-21.
    27. Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. Journal of nuclear medicine. 2008;49(11):1875.
    28. Aouadi S, Vasic A, Paloor S, Torfeh T, McGarry M, Petric P, et al. Generation of synthetic CT using multi-scale and dual-contrast patches for brain MRI-only external beam radiotherapy. Physica Medica. 2017;42:174-84.
    29. Pileggi G, Speier C, Sharp GC, Izquierdo Garcia D, Catana C, Pursley J, et al. Proton range shift analysis on brain pseudo-CT generated from T1 and T2 MR. Acta Oncologica. 2018:1-11.
    30. Chen S, Quan H, Qin A, Yee S, Yan D. MR image‐based synthetic CT for IMRT prostate treatment planning and CBCT image‐guided localization. Journal of applied clinical medical physics. 2016;17(3):236-45.