Prostate Cancer Imaging 2010: Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention pp 25-33 | Cite as
Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer
Abstract
Prostate radiation therapy dose planning currently requires computed tomography (CT) scans as they contain electron density information needed for patient dose calculations. However magnetic resonance imaging (MRI) images have significantly superior soft-tissue contrast for segmenting organs of interest and determining the target volume for treatment. This paper describes work on the development of an alternative treatment workflow enabling both organ delineation and dose planning to be performed using MRI alone. This is achieved by atlas based segmentation and the generation of pseudo-CT scans from MRI. Planning and dosimetry results for three prostate cancer patients from Calvary Mater Newcastle Hospital (Australia) are presented supporting the feasibility of this workflow. Good DSC scores were found for the atlas based segmentation of the prostate (mean 0.84) and bones (mean 0.89). The agreement between MRI/pseudo-CT and CT planning was quantified by dose differences and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi values indicate that the planning CT and pseudo-CT dose distributions are equivalent.
Keywords
Magnetic Resonance Imaging Dice Similarity Coefficient Planning Compute Tomography Probabilistic Atlas Original Compute TomographyPreview
Unable to display preview. Download preview PDF.
References
- 1.Australian Institute of Health and Welfare (AIHW): Australian Cancer Incidence and Mortality (ACIM) Books. AIHW, Canberra (2007)Google Scholar
- 2.Australian Institute of Health and Welfare (AIHW) and Australasian Association of Cancer Registries (AACR): Cancer in Australia: an overview, 2008. AIHW, Canberra (2008)Google Scholar
- 3.Swallow, T., Kirby, R.: Cancer of the prostate gland. Surgery 26(5), 213–217 (2008)Google Scholar
- 4.Prabhakar, R., et al.: Feasibility of using MRI alone for 3D radiation treatment planning in brain tumors. Jpn. J. Clin. Oncol. 37(6), 405–411 (2007)CrossRefGoogle Scholar
- 5.Roach, M., et al.: Prostate volumes defined by Magnetic Resonance Imaging and computerized tomographic scans for three-dimensional conformal radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 35(5), 1011–1018 (1996)CrossRefGoogle Scholar
- 6.Debois, M., et al.: The contribution of magnetic resonance imaging to the three-dimensional treatment planning of localized prostate cancer. Int. J. Radiat. Oncol. Biol. Phys. 45(4), 857–865 (1999)CrossRefGoogle Scholar
- 7.Rasch, C., et al.: Definition of the prostate in CT and MRI: a multi-observer study. Int. J. Radiat. Oncol. Biol. Phys. 43(1), 57–66 (1999)CrossRefGoogle Scholar
- 8.Dowling, J., et al.: Importing Contours from DICOM-RT structure sets. Insight Journal (July-December 2009)Google Scholar
- 9.Gorthi, S., et al.: Exporting Contours to DICOM-RT Structure Set. Insight Journal (January-June 2009)Google Scholar
- 10.Martin, S., et al.: Atlas-based prostate segmentation using an hybrid registration. Int. J. CARS 3, 485–492 (2008)CrossRefGoogle Scholar
- 11.Klein, S., et al.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 35(4), 1407–1417 (2008)CrossRefGoogle Scholar
- 12.Crum, W.R., et al.: Non-rigid image registration: theory and practice. Br. J. Radiol. 77(spec No. 2), S140–S153 (2004)CrossRefGoogle Scholar
- 13.Salvado, O., et al.: Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization. IEEE Trans. Med. Imaging 25(5), 539–552 (2006)CrossRefGoogle Scholar
- 14.Dowling, J., et al.: Nonrigid correction of interleaving artefacts in pelvic MRI. In: Pluim, J.P.W., Dawant, B.M. (eds.) SPIE MI, p. 72592P (2009)Google Scholar
- 15.Rohlfing, T., et al.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21(4), 1428–1442 (2004)CrossRefGoogle Scholar
- 16.Ourselin, S., et al.: Reconstructing a 3D Structure from Serial Histological Sections. Image and Vision Computing 19(1-2), 25–31 (2001)CrossRefGoogle Scholar
- 17.Vercauteren, T., et al.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 18.Vercauteren, T., et al.: Diffeomorphic demons using ITK’s finite difference solver hierarchy. The Insight Journal (2007)Google Scholar
- 19.Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 18, 712–721 (1999)CrossRefGoogle Scholar
- 20.Dice, L.R.: Measures of the Amount of Ecologic Association Between Species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
- 21.Bakai, A., et al.: A revision of the γ-evaluation concept for the comparison of dose distributions. Phys. Med. Biol. 48, 3543–3553 (2003)CrossRefGoogle Scholar