Robust CT Synthesis for Radiotherapy Planning: Application to the Head and Neck Region
In this work, we propose to tackle the problem of magnetic resonance (MR)-based radiotherapy treatment planning in the head & neck area by synthesising computed tomography (CT) from MR images using an iterative multi-atlas approach. The proposed method relies on pre-acquired pairs of non-rigidly aligned T2-weighted MRI and CT images of the neck. To synthesise a pseudo CT, all the MRIs in the database are first registered to the target MRI using a robust affine followed by a deformable registration. An initial pseudo CT is obtained by fusing the mapped atlases according to their morphological similarity to the target. This initial pseudo CT is then combined with the target MR image in order to improve both the registration and fusion stages and refine the synthesis in the bone region.
Results showed that the proposed iterative CT synthesis algorithm is able to generate pseudo CT images in a challenging region for registration algorithms. We demonstrate that the robust affine decreases the overall absolute error compared to a single affine transformation, mainly in images with small axial field-of-view, whilst the bone refinement process further reduces the error in the bone region, increasing image sharpness.
KeywordsCompute Tomogra Image Planning Target Volume Mean Absolute Error Magnetic Resonance Imaging Intensity Atlas Image
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- 1.Jonsson, J., Akhtari, M., Karlsson, M., Johansson, A., Asklund, T., Nyholm, T.: Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions. Radiation Oncology 10(1) (2015)Google Scholar
- 2.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 41(1), 011704 (2014)Google Scholar
- 3.Dowling, J.A., Lambert, J., Parker, J., Salvado, O., Fripp, J., Capp, A., Wratten, C., Denham, J.W., Greer, P.B.: 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 83(1), e5–e11 (2012)Google Scholar
- 6.Uh, J., Merchant, T.E., Li, Y., Li, X., Hua, C.: MRI-based treatment planning with pseudo CT generated through atlas registration. Med. Phys. 41(5) (2014)Google Scholar
- 7.Burgos, N., Cardoso, M.J., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Ahmed, R., Mahoney, C.J., Schott, J.M., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., Ourselin, S.: Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies. IEEE Transactions on Medical Imaging 33(12), 2332–2341 (2014)CrossRefGoogle Scholar
- 9.Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. Journal of Medical Imaging 1(2), 024003 (2014)Google Scholar
- 11.Kristan, M., Pernus, F.: Entropy based measure of camera focus. In: Proc. ERK 2004, pp. 179–182 (2004)Google Scholar