Morphometry-based measurements of the structural response to whole-brain radiation
- 216 Downloads
Morphometry techniques were applied to quantify the normal tissue therapy response in patients receiving whole-brain radiation for intracranial malignancies.
Pre- and Post-irradiation magnetic resonance imaging (MRI) data sets were retrospectively analyzed in N = 15 patients. Volume changes with respect to pre-irradiation were quantitatively measured in the cerebrum and ventricles. Measurements were correlated with the time interval from irradiation. Criteria for inclusion included craniospinal irradiation, pre-irradiation MRI, at least one follow-up MRI, and no disease progression. The brain on each image was segmented to remove the skull and registered to the initial pre-treatment scan. Average volume changes were measured using morphometry analysis of the deformation Jacobian and direct template registration-based segmentation of brain structures.
An average cerebral volume atrophy of \(-\)0.2 and \(-\)3 % was measured for the deformation morphometry and direct segmentation methods, respectively. An average ventricle volume dilation of 21 and 20 % was measured for the deformation morphometry and direct segmentation methods, respectively.
The presented study has developed an image processing pipeline for morphometric monitoring of brain tissue volume changes as a response to radiation therapy. Results indicate that quantitative morphometric monitoring is feasible and may provide additional information in assessing response.
KeywordsImage registration Radiation therapy response Morphometry Diffeomorphism Computational anatomy
This work is supported in part by the O’Donnell Foundation and NIH DP2OD007044, NIH DP2OD007044-01S1, and CPRIT RP101502 funding mechanisms. The authors would also like to thank the open source communities ITK, ANTs , itk-SNAP , and FSL  for providing enabling software for image processing and visualization.
Conflict of interest
The authors have no conflicts of interest to report.
- 8.Bro-Nielsen M, Gramkow C (1996) Fast fluid registration of medical images. Proc Vis Biomed Comput 4:267Google Scholar
- 16.Dupuis P, Grenander U, Miller MI (1998) Variational problems on flows of diffeomorphisms for image matching. Q Appl Math 56(3):587Google Scholar
- 19.Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl. NeuroImage 62(2):782–790Google Scholar
- 25.Mulhern RK, Palmer SL, Reddick WE, Glass JO, Kun LE, Taylor J, Langston J, Gajjar A (2001) Risks of young age for selected neurocognitive deficits in medulloblastoma are associated with white matter loss. J Clin Oncol 19(2):472–479Google Scholar
- 27.National Cancer Institute: A snapshot of: Brain and central nervous system cancers (2013)Google Scholar
- 29.Patchell RA, Tibbs PA, Regine WF, Dempsey RJ, Mohiuddin M, Kryscio RJ, Markesbery WR, Foon KA, Young B (1998) Postoperative radiotherapy in the treatment of single metastases to the brain. J Am Med Assoc 280(17):1485–1489Google Scholar
- 30.R Core Team: R (2013) A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- 31.Reddick WE, Russell JM, Glass JO, Xiong X, Mulhern RK, Langston JW, Merchant TE, Kun LE, Gajjar A (2000) Magn Reson Imaging 18(7):787–793Google Scholar
- 32.Tanner JM (1962) Growth at adolescence. Thomas, Springfield, ILGoogle Scholar
- 33.Thirion JP (1995) Fast non-rigid matching of 3D medical image. Tech. rep., Research Report RR-2547, Epidure Project, INRIA SophiaGoogle Scholar
- 36.Tustison NJ, Avants BB (2013) Explicit B-spline regularization in diffeomorphic image registration. Front Neuroinformatics 7:39Google Scholar
- 38.Zhang Y, Zou P, Mulhern RK, Butler RW, Laningham FH, Ogg RJ (2008) Brain structural abnormalities in survivors of pediatric posterior fossa brain tumors: a voxel-based morphometry study using free-form deformation. NeuroImage 42(1):218–29. doi: 10.1016/j.neuroimage.2008.04.181. URL: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2591023&tool=pmcentrez&rendertype=abstract