A review on methods to estimate a CT from MRI data in the context of MRI-alone RT

Authors

  • Boukellouz Wafa Ferhat abbas university Setif-1,Algeria Author
  • Abdelouahab Moussaoui Ferhat Abbas University,Setif, Algeria Author

DOI:

https://doi.org/10.26415/2572-004X-vol2iss1p150-178

Keywords:

MRI-alone RTP, Pseudo-CT, Electron density, Radiotherapy treatment planning

Abstract

Background: In recent years, Radiation Therapy (RT) has undergone many developments and provided progress in the field of cancer treatment. However, dose optimisation each treatment session puts the patient at risk of successive X-Ray exposure from Computed Tomography CT scans since this imaging modality is the reference for dose planning. Add to this difficulties related to contour propagation. Thus, approaches are focusing on the use of MRI as the only modality in RT. In this paper, we review methods for creating pseudo-CT images from MRI data for MRI-alone RT. Each class of methods is explained and underlying works are presented in detail with performance results. We discuss the advantages and limitations of each class.

Methods: We classified recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods and the classification was based on considering the general technique applied.

Results: Most research focused on the brain and the pelvic regions. The mean absolute error ranged from 80 to 137 HU and from 36.4 to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition.

Conclusion: Radiation therapy is emerging towards the generalisation of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images and the development of specialised MR sequences favouring bone visualisation. However, a benchmark needs to be established to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

Downloads

Download data is not yet available.

References

1) O'Neill, B., Salerno, G., Thomas, K., Tait, D. and Brown, G. (2009). MR vs CT imaging: low rectal cancer tumour delineation for three-dimensional conformal radiotherapy. The British Journal of Radiology, [online] 82(978), pp.509-513. Available at: http://dx.doi.org/10.1259/bjr/60198873. [Accessed 8 Jun. 2017].
2) Rasch, C., Barillot, I., Remeijer, P., Touw, A., van Herk, M. and Lebesque, J. (1999). Definition of the prostate in CT and MRI: a multi-observer study. International Journal of Radiation Oncology*Biology*Physics, [online] 43(1), pp.57-66. Available at: http://dx.doi.org/10.1016/s0360-3016(98)00351-4. [Accessed 8 Jun. 2017].
3) Moser, E., Stadlbauer, A., Windischberger, A., Quick, H H., Ladd, M E., (2009). Magnetic resonance imaging methodology. European Journal of Nuclear Medicine and Molecular Imaging. [online] 36(1):30. Available at: http://dx.doi.org/10.1007/s00259-008-0938-3.
4) Chao, M., Xie, Y. and Xing, L. (2008). Auto-propagation of contours for adaptive prostate radiation therapy. Physics in Medicine and Biology, [online] 53(17), pp.4533-4542. Available at: http://dx.doi.org/10.1088/0031-9155/53/17/005. [Accessed 8 Jun. 2017].
5) Thor, M., Petersen, J., Bentzen, L., Høyer, M. and Muren, L. (2011). Deformable image registration for contour propagation from CT to cone-beam CT scans in radiotherapy of prostate cancer. Acta Oncologica, [online] 50(6), pp.918-925. Available at: http://dx.doi.org/10.3109/0284186x.2011.577806. [Accessed 8 Jun. 2017].
6) Thörnqvist, S., Petersen, J., Høyer, M., Bentzen, L. and Muren, L. (2010). Propagation of target and organ at risk contours in radiotherapy of prostate cancer using deformable image registration. Acta Oncologica, [online] 49(7), pp.1023-1032. Available at: http://dx.doi.org/10.3109/0284186x.2010.503662. [Accessed 8 Jun. 2017].
7) Van der Put, R., Kerkhof, E., Raaymakers, B., Jürgenliemk-Schulz, I. and Lagendijk, J. (2009). Contour propagation in MRI-guided radiotherapy treatment of cervical cancer: the accuracy of rigid, non-rigid and semi-automatic registrations. Physics in Medicine and Biology, [online] 54(23), pp.7135-7150. Available at: http://dx.doi.org/10.1088/0031-9155/54/23/007. [Accessed 8 Jun. 2017].
8) Faggiano, E., Fiorino, C., Scalco, E., Broggi, S., Cattaneo, M., Maggiulli, E., Dell 'O ca, I., Di Muzio, N., Calandrino, R. and Rizzo, G. (2011). An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy. Physics in Medicine and Biology, [online] 56(3), pp.775-791. Available at: http://dx.doi.org/10.1088/0031-9155/56/3/015. [Accessed 8 Jun. 2017].
9) Commandeur, F., Simon, A., Mathieu, R., Nassef, M., Ospina, J., Rolland, Y., Haigron, P., De Crevoisier, R. and Acosta, O. (2016). MRI to CT prostate registration for improved targeting in cancer external beam radiotherapy. IEEE Journal of Biomedical and Health Informatics, [online] pp.1-1. Available at: http://dx.doi.org/10.1109/jbhi.2016.2581881. [Accessed 8 Jun. 2017].
10) Cattaneo, G., Reni, M., Rizzo, G., Castellone, P., Ceresoli, G., Cozzarini, C., Ferreri, A., Passoni, P. and Calandrino, R. (2005). Target delineation in post-operative radiotherapy of brain gliomas: Interobserver variability and impact of image registration of MR (pre-operative) images on treatment planning CT scans. Radiotherapy and Oncology, [online] 75(2), pp.217-223. Available at: http://dx.doi.org/10.1016/j.radonc.2005.03.012. [Accessed 8 Jun. 2017].
11) Nyholm, T., Nyberg, M., Karlsson, M. and Karlsson, M. (2009). Systematization of spatial uncertainties for comparison between a MR and a CT-based radiotherapy workflow for prostate treatments. Radiation Oncology, [online] 4(1), p.54. Available at: http://dx.doi.org/10.1186/1748-717x-4-54. [Accessed 8 Jun. 2017].
12) Ulin, K., Urie, M. and Cherlow, J. (2010). Results of a Multi-Institutional Benchmark Test for Cranial CT/MR Image Registration. International Journal of Radiation Oncology*Biology*Physics, [online] 77(5), pp.1584-1589. Available at: http://dx.doi.org/10.1016/j.ijrobp.2009.10.017. [Accessed 8 Jun. 2017].
13) Beavis, A., Gibbs, P., Dealey, R. and Whitton, V. (1998). Radiotherapy treatment planning of brain tumours using MRI alone. The British Journal of Radiology, [online] 71(845), pp.544-548. Available at: http://dx.doi.org/10.1259/bjr.71.845.9691900. [Accessed 8 Jun. 2017].
14) Lee, Y. (2003). Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone. Radiotherapy and Oncology, [online] 66(2), pp.203-216. Available at: http://dx.doi.org/10.1016/s0167-8140(02)00440-1. [Accessed 8 Jun. 2017].
15) Jonsson, J., Karlsson, M., Karlsson, M. and Nyholm, T. (2010). Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions. Radiation Oncology, [online] 5(1), p.62. Available at: http://dx.doi.org/10.1186/1748-717x-5-62. [Accessed 8 Jun. 2017].
16) Lambert, J., Greer, P., Menk, F., Patterson, J., Parker, J., Dahl, K., Gupta, S., Capp, A., Wratten, C., Tang, C., Kumar, M., Dowling, J., Hauville, S., Hughes, C., Fisher, K., Lau, P., Denham, J. and Salvado, O. (2011). MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning. Radiotherapy and Oncology, [online] 98(3), pp.330-334. Available at: http://dx.doi.org/10.1016/j.radonc.2011.01.012. [Accessed 8 Jun. 2017].
17) Gudur, M., Hara, W., Le, Q., Wang, L., Xing, L. and Li, R. (2014). A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning. Physics in Medicine and Biology, [online] 59(21), pp.6595-6606. Available at: http://dx.doi.org/10.1088/0031-9155/59/21/6595. [Accessed 8 Jun. 2017].
18) Chen, L., Price, R., Wang, L., Li, J., Qin, L., McNeeley, S., Ma, C., Freedman, G. and Pollack, A. (2004). MRI-based treatment planning for radiotherapy: Dosimetric verification for prostate IMRT. International Journal of Radiation Oncology*Biology*Physics, [online] 60(2), pp.636-647. Available at: http://dx.doi.org/10.1016/j.ijrobp.2004.05.068. [Accessed 8 Jun. 2017].
19) Eilertsen, K., Nilsen Tor Arne Vestad, L., Geier, O. and Skretting, A. (2008). A simulation of MRI based dose calculations on the basis of radiotherapy planning CT images. Acta Oncologica, [online] 47(7), pp.1294-1302. Available at: http://dx.doi.org/10.1080/02841860802256426. [Accessed 8 Jun. 2017].
20) Pasquier, D., Palos, G., Castelain, B., Lartigau, E. and Rousseau, J. (2004). MRI simulation for conformal radiation therapy of prostate cancer. International Journal of Radiation Oncology*Biology*Physics, [online] 60(1), pp.S636-S637. Available at: http://dx.doi.org/10.1016/j.ijrobp.2004.07.656. [Accessed 8 Jun. 2017].
21) Hoogcarspel, S., Van der Velden, J., Lagendijk, J., van Vulpen, M. and Raaymakers, B. (2014). The feasibility of utilizing pseudo CT-data for online MRI based treatment plan adaptation for a stereotactic radiotherapy treatment of spinal bone metastases. Physics in Medicine and Biology, [online] 59(23), pp.7383-7391. Available at: http://dx.doi.org/10.1088/0031-9155/59/23/7383. [Accessed 8 Jun. 2017].
22) Karotki, A., Mah, K., Meijer, G. and Meltsner, M. (2011). Comparison of bulk electron density and voxel-based electron density treatment planning. Journal of Applied Clinical Medical Physics, [online] 12(4), pp.97-104. Available at: http://dx.doi.org/10.1120/jacmp.v12i4.3522. [Accessed 8 Jun. 2017].
23) Dice, L. (1945). Measures of the Amount of Ecologic Association Between Species. Ecology, [online] 26(3), pp.297-302. Available at: http://dx.doi.org/10.2307/1932409. [Accessed 9 Jun. 2017].
24) Low, D., Harms, W., Mutic, S. and Purdy, J. (1998). A technique for the quantitative evaluation of dose distributions. Medical Physics, [online] 25(5), pp.656-661. Available at: http://dx.doi.org/10.1118/1.598248. [Accessed 9 Jun. 2017].
25) Edmund, J. and Nyholm, T. (2017). A review of substitute CT generation for MRI-only radiation therapy. Radiation Oncology, [online] 12(1). Available at: http://dx.doi.org/10.1186/s13014-016-0747-y. [Accessed 8 Jun. 2017].
26) Berker, Y., Franke, J., Salomon, A., Palmowski, M., Donker, H., Temur, Y., Mottaghy, F., Kuhl, C., Izquierdo-Garcia, D., Fayad, Z., Kiessling, F. and Schulz, V. (2012). MRI-Based Attenuation Correction for Hybrid PET/MRI Systems: A 4-Class Tissue Segmentation Technique Using a Combined Ultrashort-Echo-Time/Dixon MRI Sequence. Journal of Nuclear Medicine, [online] 53(5), pp.796-804. Available at: http://dx.doi.org/10.2967/jnumed.111.092577. [Accessed 8 Jun. 2017].
27) Dixon, W. (1984). Simple proton spectroscopic imaging. Radiology, [online] 153(1), pp.189-194. Available at: http://dx.doi.org/10.1148/radiology.153.1.6089263. [Accessed 8 Jun. 2017].
28) Glover, G. and Schneider, E. (1991). Three-point dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magnetic Resonance in Medicine, [online] 18(2), pp.371-383. Available at: http://dx.doi.org/10.1002/mrm.1910180211. [Accessed 8 Jun. 2017].
29) Su, K., Hu, L., Stehning, C., Helle, M., Qian, P., Thompson, C., Pereira, G., Jordan, D., Herrmann, K., Traughber, M., Muzic, R. and Traughber, B. (2015). Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering. Medical Physics, [online] 42(8), pp.4974-4986. Available at: http://dx.doi.org/10.1118/1.4926756. [Accessed 8 Jun. 2017].
30) Zaidi, H., Montandon, M. and Slosman, D. (2003). Magnetic resonance imaging-guided attenuation and scatter corrections in three-dimensional brain positron emission tomography. Medical Physics, [online] 30(5), pp.937-948. Available at: http://dx.doi.org/10.1118/1.1569270. [Accessed 8 Jun. 2017].
31) Hsu, S., Cao, Y., Huang, K., Feng, M. and Balter, J. (2013). Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy. Physics in Medicine and Biology, [online] 58(23), pp.8419-8435. Available at: http://dx.doi.org/10.1088/0031-9155/58/23/8419. [Accessed 8 Jun. 2017].
32) Khateri, P., Rad, H., Jafari, A. and Ay, M. (2014). A novel segmentation approach for implementation of MRAC in head PET/MRI employing Short-TE MRI and 2-point Dixon method in a fuzzy C-means framework. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, [online] 734, pp.171-174. Available at: http://dx.doi.org/10.1016/j.nima.2013.09.006. [Accessed 8 Jun. 2017].
33) Khateri, P., Saligheh Rad, H., Jafari, A., Fathi Kazerooni, A., Akbarzadeh, A., Shojae Moghadam, M., Aryan, A., Ghafarian, P. and Ay, M. (2015). Generation of a Four-Class Attenuation Map for MRI-Based Attenuation Correction of PET Data in the Head Area Using a Novel Combination of STE/Dixon-MRI and FCM Clustering. Molecular Imaging and Biology, [online] 17(6), pp.884-892. Available at: http://dx.doi.org/10.1007/s11307-015-0849-1. [Accessed 8 Jun. 2017].
34) Boettger, T., Nyholm, T., Karlsson, M., Nunna, C. and Celi, J. (2008). Radiation therapy planning and simulation with magnetic resonance images. Medical imaging, [online] Vol. 6918 (69181C-1). Available at: http://dx.doi.org/10.1117/12.770016.
35) Rahmer, J., Blume, U. and Börnert, P. (2007). Selective 3D ultrashort TE imaging: comparison of "dual-echo" acquisition and magnetization preparation for improving short-T2 contrast. Magnetic Resonance Materials in Physics, Biology and Medicine, [online] 20(2), pp.83-92. Available at: http://dx.doi.org/10.1007/s10334-007-0070-6. [Accessed 8 Jun. 2017].
36) Rank, C., Tremmel, C., Hünemohr, N., Nagel, A., Jäkel, O. and Greilich, S. (2013). MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach. Radiation Oncology, [online] 8(1), p.51. Available at: http://dx.doi.org/10.1186/1748-717x-8-51. [Accessed 8 Jun. 2017].
37) Navalpakkam, B., Braun, H., Kuwert, T. and Quick, H. (2013). Magnetic Resonance Based Attenuation Correction for PET/MR Hybrid Imaging Using Continuous Valued Attenuation Maps. Investigative Radiology, [online] 48(5), pp.323-332. Available at: http://dx.doi.org/10.1097/rli.0b013e318283292f. [Accessed 8 Jun. 2017].
38) Liu, L., Jolly, S., Cao, Y., Vineberg, K., Fessler, J. and Balter, J. (2017). Female pelvic synthetic CT generation based on joint intensity and shape analysis. Physics in Medicine and Biology, [online] 62(8), pp.2935-2949. Available at: http://dx.doi.org/10.1088/1361-6560/62/8/2935. [Accessed 8 Jun. 2017].
39) Liu, L., Cao, Y., Fessler, J., Jolly, S. and Balter, J. (2015). A female pelvic bone shape model for air/bone separation in support of synthetic CT generation for radiation therapy. Physics in Medicine and Biology, [online] 61(1), pp.169-182. Available at: http://dx.doi.org/10.1088/0031-9155/61/1/169. [Accessed 8 Jun. 2017].
40) Bredfeldt, J., Liu, L., Feng, M., Cao, Y. and Balter, J. (2017). Synthetic CT for MRI-based liver stereotactic body radiotherapy treatment planning. Physics in Medicine and Biology, [online] 62(8), pp.2922-2934. Available at: http://dx.doi.org/10.1088/1361-6560/aa5059. [Accessed 8 Jun. 2017].
41) Peng, Z., Zhong, J., Wee, W. and Lee, J. (2005). Automated vertebra detection and segmentation from the whole spine MR images. In: IEEE 27th annual international conference of the engineering in medicine and biology society, [online] pp.2527-2530, Available at: http://dx.doi.org/10.1109/IEMBS.2005.1616983.
42) Szu-Hao Huang, Yi-Hong Chu, Shang-Hong Lai and Novak, C. (2009). Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI. IEEE Transactions on Medical Imaging, [online] 28(10), pp.1595-1605. Available at: http://dx.doi.org/10.1109/tmi.2009.2023362. [Accessed 9 Jun. 2017].
43) Andreasen, D., Van Leemput, K., Hansen, R., Andersen, J. and Edmund, J. (2015). Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain. Medical Physics, [online] 42(4), pp.1596-1605. Available at: http://dx.doi.org/10.1118/1.4914158. [Accessed 8 Jun. 2017].
44) Edmund, J., Kjer, H., Van Leemput, K., Hansen, R., Andersen, J. and Andreasen, D. (2014). A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times. Physics in Medicine and Biology, [online] 59(23), pp.7501-7519. Available at: http://dx.doi.org/10.1088/0031-9155/59/23/7501. [Accessed 8 Jun. 2017].
45) Wu, Y., Yang, W., Lu, L., Lu, Z., Zhong, L., Huang, M., Feng, Y., Feng, Q. and Chen, W. (2016). Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction. Journal of Nuclear Medicine, [online] 57(10), pp.1635-1641. Available at: http://dx.doi.org/10.2967/jnumed.115.163121. [Accessed 8 Jun. 2017].
46) Roy, S., Carras, A., Jog, A., Prince, L. and Lee, J. (2014). MR to CT registration of brains using image synthesis. In SPIE Medical Imaging, [online] 9034. Available at: http://dx.doi.org/10.1117/12.2043954.
47) Roy, S., Wang, W., Carass, A., Prince, J., Butman, J. and Pham, D. (2014). PET Attenuation Correction Using Synthetic CT from Ultrashort Echo-Time MR Imaging. Journal of Nuclear Medicine, [online] 55(12), pp.2071-2077. Available at: http://dx.doi.org/10.2967/jnumed.114.143958. [Accessed 8 Jun. 2017].
48) Johansson, A., Karlsson, M. and Nyholm, T. (2011). CT substitute derived from MRI sequences with ultrashort echo time. Medical Physics, [online] 38(5), pp.2708-2714. Available at: http://dx.doi.org/10.1118/1.3578928. [Accessed 8 Jun. 2017].
49) Johansson, A., Karlsson, M., Yu, J., Asklund, T. and Nyholm, T. (2012). Voxel-wise uncertainty in CT substitute derived from MRI. Medical Physics, [online] 39(6Part1), pp.3283-3290. Available at: http://dx.doi.org/10.1118/1.4711807. [Accessed 8 Jun. 2017].
50) Johansson, A., Garpebring, A., Karlsson, M., Asklund, T. and Nyholm, T. (2013). 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. Acta Oncologica, [online] 52(7), pp.1369-1373. Available at: http://dx.doi.org/10.3109/0284186x.2013.819119. [Accessed 8 Jun. 2017].
51) Kapanen, M. and Tenhunen, M. (2012). T1/T2*-weighted MRI provides clinically relevant pseudo-CT density data for the pelvic bones in MRI-only based radiotherapy treatment planning. Acta Oncologica, [online] 52(3), pp.612-618. Available at: http://dx.doi.org/10.3109/0284186x.2012.692883. [Accessed 8 Jun. 2017].
52) Kim, J., Glide-Hurst, C., Doemer, A., Wen, N., Movsas, B. and Chetty, I. (2015). Implementation of a Novel Algorithm For Generating Synthetic CT Images From Magnetic Resonance Imaging Data Sets for Prostate Cancer Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics, [online] 91(1), pp.39-47. Available at: http://dx.doi.org/10.1016/j.ijrobp.2014.09.015. [Accessed 8 Jun. 2017].
53) Korhonen, J., Kapanen, M., Keyriläinen, J., Seppälä, T. and Tenhunen, M. (2013). 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, [online] 41(1), p.011704. Available at: http://dx.doi.org/10.1118/1.4842575. [Accessed 8 Jun. 2017].
54) Zhong, L., Lin, L., Lu, Z., Wu, Y., Lu, Z., Huang, M., Yang, W. and Feng, Q. (2016). Predict CT image from MRI data using KNN-regression with learned local descriptors. In: IEEE 13th international symposium on Biomedical imaging, [online] pp.743-746, Available at: http://dx.doi.org/10.1109/ISBI.2016.7493373.
55) Zhen, X., Wang, Z., Yu, M. and Li, S. (2015). Supervised descriptor learning for multi-output regression. In: EEE Conference on computer vision and pattern recognition, [online] pp.1211-1218, Available at: http://dx.doi.org/10.1109/CVPR.2015.7298725.
56) Huynh, T., Gao, Y., Kang, J., Wang, L., Zhang, P., Lian, J. and Shen, D. (2016). Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model. IEEE Transactions on Medical Imaging, [online] 35(1), pp.174-183. Available at: http://dx.doi.org/10.1109/tmi.2015.2461533. [Accessed 8 Jun. 2017].
57) Brown, L. (1992). A survey of image registration techniques. ACM Computing Surveys, [online] 24(4), pp.325-376. Available at: http://dx.doi.org/10.1145/146370.146374. [Accessed 8 Jun. 2017].
58) Rueckert, D and Schnabel. J. A. (2010) Medical image registration. Biomedical Image Processing, Springer, [online] pp. 131-154. Available at: http://dx.doi.org/10.1016/10.1007/978-3-642-15816-2_5.
59) Zitová, B. and Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, [online] 21(11), pp.977-1000. Available at: http://dx.doi.org/10.1016/s0262-8856(03)00137-9. [Accessed 8 Jun. 2017].
60) Maintz, J. and Viergever, M. (1998). A survey of medical image registration. Medical Image Analysis, [online] 2(1), pp.1-36. Available at: http://dx.doi.org/10.1016/s1361-8415(01)80026-8. [Accessed 8 Jun. 2017].
61) Modersitzki, J. (2004). Numerical methods for image registration. Oxford : Oxford Univ Press on demand.
62) Hajnal, J., Hill, D.L.G. and Hawkes, D.J (2001). Medical image registration. CRC Press.
63) Devic, S. (2012). MRI simulation for radiotherapy treatment planning. Medical Physics, [online] 39(11), pp.6701-6711. Available at: http://dx.doi.org/10.1118/1.4758068. [Accessed 8 Jun. 2017].
64) Lester, H. and Arridge, S. (1999). A survey of hierarchical non-linear medical image registration. Pattern Recognition, [online] 32(1), pp.129-149. Available at: http://dx.doi.org/10.1016/s0031-3203(98)00095-8. [Accessed 8 Jun. 2017].
65) Kops, E. and Hersog, H. (2007). Alternative methods for attenuation correction for PET images in MR-PET scanners. In: IEEE Nuclear science symposium conference record, [online] 6, pp.4327-4330. Available at: http://dx.doi.org/10.1109/NSSMIC.2007.4437073. [Accessed 8 Jun. 2017].
66) Schreibmann, E., Nye, J., Schuster, D., Martin, D., Votaw, J. and Fox, T. (2010). MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration. Medical Physics, [online] 37(5), pp.2101-2109. Available at: http://dx.doi.org/10.1118/1.3377774. [Accessed 8 Jun. 2017].
67) Greer, P., Dowling, J., Lambert, J., Frippe, J., Parker, J., Denham, J., Wratten, C., Capp, A. and Salvado, O. (2011). A magnetic resonance imaging-based workflow for planning radiation therapy for prostate cancer. Medical journal of Australia, [online] 194(4), pp.S24-S27. PMID: 21401484.
68) Dowling, J., Lambert, J., Parker, J., Salvado, O., Fripp, J., Capp, A., Wratten, C., Denham, J. and Greer, P. (2012). 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, [online] 83(1), pp.e5-e11. Available at: http://dx.doi.org/10.1016/j.ijrobp.2011.11.056. [Accessed 8 Jun. 2017].
69) Burgos, N., Cardoso, M., Thielemans, K., Duncan, J., Atkinson, D., Arridge, S., Hutton, B. and Ourselin, S. (2014). Attenuation correction synthesis for hybrid PET-MR scanners: validation for brain study applications. EJNMMI Physics, [online] 1(1), p.A52. Available at: http://dx.doi.org/10.1186/2197-7364-1-s1-a52. [Accessed 8 Jun. 2017].
70) Uh, J., Merchant, T., Li, Y., Li, X. and Hua, C. (2014). MRI-based treatment planning with pseudo CT generated through atlas registration. Medical Physics, [online] 41(5), p.051711. Available at: http://dx.doi.org/10.1118/1.4873315. [Accessed 8 Jun. 2017].
71) Sjölund, J., Forsberg, D., Andersson, M. and Knutsson, H. (2015). Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Physics in Medicine and Biology, [online] 60(2), pp.825-839. Available at: http://dx.doi.org/10.1088/0031-9155/60/2/825. [Accessed 8 Jun. 2017].
72) Knutsson, H. and Andersson, M. (2005). Morphons: segmentation using elastic canvas and paint on priors. In: IEEE International conference on image processing, [online] 2, pp.II-1226. Available at: http://dx.doi.org/10.1009/ICIP.2005.1530283. [Accessed 8 Jun. 2017].
73) Mérida, I., Costes, N., Heckemann, R. and Hammers, A. (2015). Pseudo-CT generation in brain MR-PET attenuation correction: comparison of several multi-atlas methods. EJNMMI Physics, [online] 2(1), p.A29. Available at: http://dx.doi.org/10.1186/2197-7364-2-s1-a29. [Accessed 8 Jun. 2017].
74) Arabi, H., Koutsouvelis, N., Rouzaud, M., Miralbell, R. and Zaidi, H. (2016). Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET/MRI-guided radiotherapy treatment planning. Physics in Medicine and Biology, [online] 61(17), pp.6531-6552. Available at: http://dx.doi.org/10.1088/0031-9155/61/17/6531. [Accessed 8 Jun. 2017].
75) Kraus, K., Jäkel, O., Niebuhr, N. and Pfaffenberger, A. (2017). Generation of synthetic CT data using patient specific daily MR image data and image registration. Physics in Medicine and Biology, [online] 62(4), pp.1358-1377. Available at: http://dx.doi.org/10.1088/1361-6560/aa5200. [Accessed 8 Jun. 2017].
76) Izquierdo-Garcia, D., Hansen, A., Forster, S., Benoit, D., Schachoff, S., Furst, S., Chen, K., Chonde, D. and Catana, C. (2014). An SPM8-Based Approach for Attenuation Correction Combining Segmentation and Nonrigid Template Formation: Application to Simultaneous PET/MR Brain Imaging. Journal of Nuclear Medicine, [online] 55(11), pp.1825-1830. Available at: http://dx.doi.org/10.2967/jnumed.113.136341. [Accessed 8 Jun. 2017].
77) Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Scholkopf, B. and Pichler, B. (2008). MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration. Journal of Nuclear Medicine, [online] 49(11), pp.1875-1883. Available at: http://dx.doi.org/10.2967/jnumed.107.049353. [Accessed 8 Jun. 2017].
78) Chen, Y., Juttukonda, M., Lee, Y., Su, Y., Espinoza, F., Lin, W., Shen, D., Lulash, D. and An, H. (2014). MRI based attenuation correction for PET/MRI via MRF segmentation and sparse regression estimated CT. In: IEEE 11th international symposium on Biomedical imaging, [online] 60(2), pp.1364-1367. Available at: http://dx.doi.org/10.1109/ISBI.2014.6868131. [Accessed 8 Jun. 2017].
79) Siversson, C., Nordström, F., Nilsson, T., Nyholm, T., Jonsson, J., Gunnlaugsson, A. and Olsson, L. (2015). Technical Note: MRI only prostate radiotherapy planning using the statistical decomposition algorithm. Medical Physics, [online] 42(10), pp.6090-6097. Available at: http://dx.doi.org/10.1118/1.4931417. [Accessed 8 Jun. 2017].
80) Burgos, N., Guerreiro, F., McClelland, J., Presles, B., Modat, M., Nill, S., Dearnaley, D., deSouza, N., Oelfke, U., Knopf, A., Ourselin, S. and Jorge Cardoso, M. (2017). Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning. Physics in Medicine and Biology, [online] 62(11), pp.4237-4253. Available at: http://dx.doi.org/10.1088/1361-6560/aa66bf. [Accessed 8 Jun. 2017].
81) Demol, B., Boydev, C., Korhonen, J. and Reynaert, N. (2016). Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images. Medical Physics, [online] 43(12), pp.6557-6568. Available at: http://dx.doi.org/10.1118/1.4967480. [Accessed 8 Jun. 2017].
82) Wang, Z., Bovik, A., Sheikh, H. and Simoncelli, E. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, [online] 13(4), pp.600-612. Available at: http://dx.doi.org/10.1109/tip.2003.819861. [Accessed 8 Jun. 2017].
83) Stanescu, T., Jans, H., Pervez, N., Stavrev, P. and Fallone, B. (2008). A study on the magnetic resonance imaging (MRI)-based radiation treatment planning of intracranial lesions. Physics in Medicine and Biology, [online] 53(13), pp.3579-3593. Available at: http://dx.doi.org/10.1088/0031-9155/53/13/013. [Accessed 8 Jun. 2017].
84) Catana, C., van der Kouwe, A., Benner, T., Michel, C., Hamm, M., Fenchel, M., Fischl, B., Rosen, B., Schmand, M. and Sorensen, A. (2010). Toward Implementing an MRI-Based PET Attenuation-Correction Method for Neurologic Studies on the MR-PET Brain Prototype. Journal of Nuclear Medicine, [online] 51(9), pp.1431-1438. Available at: http://dx.doi.org/10.2967/jnumed.109.069112. [Accessed 8 Jun. 2017].
85) Keereman, V., Fierens, Y., Broux, T., De Deene, Y., Lonneux, M. and Vandenberghe, S. (2010). MRI-Based Attenuation Correction for PET/MRI Using Ultrashort Echo Time Sequences. Journal of Nuclear Medicine, [online] 51(5), pp.812-818. Available at: http://dx.doi.org/10.2967/jnumed.109.065425. [Accessed 8 Jun. 2017].
86) Aitken, A., Giese, D., Tsoumpas, C., Schleyer, P., Kozerke, S., Prieto, C. and Schaeffter, T. (2013). Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring. Medical Physics, [online] 41(1), p.012302. Available at: http://dx.doi.org/10.1118/1.4837315. [Accessed 8 Jun. 2017].
87) Buerger, C., Aitken, A., Tsoumpas, C., King, A., Schulz, V., Marsden, P. and Schaeffer, T. (2011). Investigation of 4D PET attenuation correction using ultra-short-echo time MR. In: Nuclear science symposium and medical imaging conference (NSS/MIC). IEEE, [online] 60(2), pp.3558-3561. Available at: http://dx.doi.org/10.1109/NSSMIC.2011.6153668. [Accessed 8 Jun. 2017].
88) Ghose, S., Dowling, J., Rai, R. and Liney, G. (2017). Substitute CT generation from a single ultra short time echo MRI sequence: preliminary study. Physics in Medicine and Biology, [online] 62(8), pp.2950-2960. Available at: http://dx.doi.org/10.1088/1361-6560/aa508a. [Accessed 8 Jun. 2017].
89) Juttukonda, M., Mersereau, B., Chen, Y., Su, Y., Rubin, B., Benzinger, T., Lalush, D. and An, H. (2015). MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units. NeuroImage, [online] 112, pp.160-168. Available at: http://dx.doi.org/10.1016/j.neuroimage.2015.03.009. [Accessed 8 Jun. 2017].
90) Delso, G., Wiesinger, F., Sacolick, L., Kaushik, S., Shanbhag, D., Hullner, M. and Veit-Haibach, P. (2015). Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull. Journal of Nuclear Medicine, [online] 56(3), pp.417-422. Available at: http://dx.doi.org/10.2967/jnumed.114.149997. [Accessed 8 Jun. 2017].
91) Wiesinger, F., Sacolick, L., Menini, A., Kaushik, S., Ahn, S., Veit-Haibach, P., Delso, G. and Shanbhag, D. (2015). Zero TEMR bone imaging in the head. Magnetic Resonance in Medicine, [online] 75(1), pp.107-114. Available at: http://dx.doi.org/10.1002/mrm.25545. [Accessed 8 Jun. 2017].
92) Kim, J., Lee, J., Song, I. and Lee, D. (2012). Comparison of Segmentation-Based Attenuation Correction Methods for PET/MRI: Evaluation of Bone and Liver Standardized Uptake Value with Oncologic PET/CT Data. Journal of Nuclear Medicine, [online] 53(12), pp.1878-1882. Available at: http://dx.doi.org/10.2967/jnumed.112.104109. [Accessed 8 Jun. 2017].
93) Reza Ay, M., Akbarzadeh, A., Ahmadian, A. and Zaidi, H. (2014). Classification of bones from MR images in torso PET-MR imaging using a statistical shape model. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, [online] 734, pp.196-200. Available at: http://dx.doi.org/10.1016/j.nima.2013.09.007. [Accessed 8 Jun. 2017].
94) Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J. and Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, [online] 46(3), pp.726-738. Available at: http://dx.doi.org/10.1016/j.neuroimage.2009.02.018. [Accessed 8 Jun. 2017].
95) Artaechevarria, X., Munoz-Barrutia, A. and Ortiz-de-Solorzano, C. (2009). Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data. IEEE Transactions on Medical Imaging, [online] 28(8), pp.1266-1277. Available at: http://dx.doi.org/10.1109/tmi.2009.2014372. [Accessed 8 Jun. 2017].
96) Mehranian, A., Arabi, H. and Zaidi, H. (2016). Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI. NeuroImage, [online] 130, pp.123-133. Available at: http://dx.doi.org/10.1016/j.neuroimage.2016.01.060. [Accessed 8 Jun. 2017].
97) Ren, S., Hara, W., Wang, L., Buyyounouski, M., Le, Q., Xing, L. and Li, R. (2017). Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. International Journal of Radiation Oncology*Biology*Physics, [online] 97(4), pp.849-857. Available at: http://dx.doi.org/10.1016/j.ijrobp.2016.11.053. [Accessed 8 Jun. 2017].
98) Whelan, B., Kumar, S., Dowling, J., Begg, J., Lambert, J., Lim, K., Vinod, S., Greer, P. and Holloway, L. (2015). Utilising pseudo-CT data for dose calculation and plan optimization in adaptive radiotherapy. Australasian Physical & Engineering Sciences in Medicine, [online] 38(4), pp.561-568. Available at: http://dx.doi.org/10.1007/s13246-015-0376-z. [Accessed 8 Jun. 2017].
99) Zerda, A., Armbruster, B. and Xing, L. (2007). Formulating adaptive radiation therapy (ART) treatment planning into a closed-loop control framework. Physics in Medicine and Biology, [online] 52(14), pp.4137-4153. Available at: http://dx.doi.org/10.1088/0031-9155/52/14/008. [Accessed 8 Jun. 2017].
100) Roberson, P., McLaughlin, P., Narayana, V., Troyer, S., Hixson, G. and Kessler, M. (2005). Use and uncertainties of mutual information for computed tomography/magnetic resonance (CT/MR) registration post permanent implant of the prostate. Medical Physics, [online] 32(2), pp.473-482. Available at: http://dx.doi.org/10.1118/1.1851920. [Accessed 8 Jun. 2017].
101) Yang, Y., Schreibmann, E., Li, T., Wang, C. and Xing, L. (2007). Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation. Physics in Medicine and Biology, [online] 52(3), pp.685-705. Available at: http://dx.doi.org/10.1088/0031-9155/52/3/011. [Accessed 8 Jun. 2017].
102) Korsholm, M., Waring, L. and Edmund, J. (2014). A criterion for the reliable use of MRI-only radiotherapy. Radiation Oncology, [online] 9(1), p.16. Available at: http://dx.doi.org/10.1186/1748-717x-9-16. [Accessed 8 Jun. 2017].
103) Walker, A., Liney, G., Metcalfe, P. and Holloway, L. (2014). MRI distortion: considerations for MRI based radiotherapy treatment planning. Australian Physical & Engineering Sciences in Medicine, [online] 37(1), pp.103-113. Available at: http://dx.doi.org/10.1007/s13246-014-0252-2. [Accessed 8 Jun. 2017].

Published

2018-03-19

Issue

Section

Medical technologies