Average volume reference space for large scale registration of whole-body magnetic resonance images


Autoři: Martino Pilia aff001;  Joel Kullberg aff001;  Håkan Ahlström aff001;  Filip Malmberg aff001;  Simon Ekström aff001;  Robin Strand aff001
Působiště autorů: Department of Surgical Sciences, Uppsala University, Uppsala, Sweden aff001;  Department of Information Technology, Uppsala University, Uppsala, Sweden aff002;  Antaros Medical, Uppsala, Sweden aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222700

Souhrn

Background and objectives

The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is neutral with respect to local tissue volume is valuable in correlation studies. The purpose of this work is to generate a reference space for whole-body imaging that has zero voxel-wise average volume change when mapped to a cohort.

Methods

This work proposes an approach to register multiple whole-body images to a common template using volume changes to generate a synthetic reference space, starting with an initial reference and refining it by warping it with a deformation that brings the voxel-wise average volume change associated to the mappings of all the images in the cohort to zero.

Results

Experiments on fat/water separated whole-body MR images show how the method effectively generates a reference space neutral with respect to volume changes, without reducing the quality of the registration nor introducing artefacts in the anatomy, while providing better alignment when compared to an implicit reference groupwise approach.

Conclusions

The proposed method allows to quickly generate a reference space neutral with respect to local volume changes, that retains the registration quality of a sharp template, and that can be used for statistical analysis of voxel-wise correlations in large datasets of whole-body image data.

Klíčová slova:

Atrophy – Deformation – Fats – Imaging techniques – Magnetic resonance imaging – Neuroimaging – Positron emission tomography – Statistical data


Zdroje

1. Strand R, Malmberg F, Johansson L, Lind L, Sundbom M, Ahlström H, et al. A concept for holistic whole body MRI data analysis, Imiomics. PloS one. 2017, 12(2). doi: 10.1371/journal.pone.0169966

2. Carlbom L. Positron Emission Tomography and Magnetic Resonance Techniques in Diabetes. PhD thesis, Uppsala University. 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-340008.

3. Lind L, Kullberg J, Ahlström H, Michaëlsson K, Strand R. Proof of principle study of a detailed whole-body image analysis technique, “Imiomics”, regarding adipose and lean tissue distribution. Sci Rep. 2019, 9(1): 7388. doi: 10.1038/s41598-019-43690-w

4. Joshi S, Davis B, Jomier M, Gerig G. Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage. 2004, 23: 151–160. doi: 10.1016/j.neuroimage.2004.07.068

5. Balci SK, Golland P, Shenton M, Wells WM. Free-Form B-spline Deformation Model for Groupwise Registration. MICCAI. 2006; 10: 23–30.

6. Guimond A, Meunier J, Thirion JP. Average brain models: A convergence study. Comput Vis Image Underst. 2000; 77(2): 192–210. doi: 10.1006/cviu.1999.0815

7. Wu G, Jia H, Wang Q, Shen D. SharpMean: groupwise registration guided by sharp mean image and tree-based registration. Neuroimage. 2011; 56(4): 1968–1981. doi: 10.1016/j.neuroimage.2011.03.050 21440646

8. van Eede MC, Scholz J, Chakravarty MM, Henkelman RM, Lerch JP. Mapping registration sensitivity in MR mouse brain images. Neuroimage. 2013; 82: 226–236. doi: 10.1016/j.neuroimage.2013.06.004 23756204

9. Karaçali B, Davatzikos C. Simulation of tissue atrophy using a topology preserving transformation model. IEEE Trans Med Imaging. 2006; 25(5): 649–652. doi: 10.1109/TMI.2006.873221 16689268

10. Khanal B, Lorenzi M, Ayache N, Pennec X. A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer’s disease. Neuroimage. 2016; 134: 35–52. doi: 10.1016/j.neuroimage.2016.03.061 27039699

11. Smith ADC, Crum WR, Hill DL, Thacker NA, Bromiley PA. Biomechanical simulation of atrophy in MR images. Proc SPIE Int Soc Opt Eng. 2003; 5032: 481–490.

12. Camara-Rey O, Schweiger M, Scahill RI, Crum WR, Schnabel JA, Hill DL, et al. Simulation of local and global atrophy in Alzheimer’s disease studies. MICCAI. 2006: 937–945.

13. Vovk U, Pernuš F, Likar B. Intensity inhomogeneity correction of multispectral MR images. Neuroimage. 2006;32(1):54–61. doi: 10.1016/j.neuroimage.2006.03.020 16647862

14. Ekström S, Malmberg F, Ahlström H, Kullberg J, Strand R. Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images. 2018. Preprint. Available from: arXiv:1810.08427. Cited 9 December 2018.

15. Modersitzki J. Numerical Methods for Image Registration. Numerical Mathematics and Scientific Computation. Oxford University Press; 2004.

16. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945; 26(3): 297–302. doi: 10.2307/1932409

17. Christensen GE, Johnson HJ. Consistent image registration. IEEE Trans Med Imaging. 2001; 20(7): 568–582. doi: 10.1109/42.932742 11465464

18. Rohlfing T. Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans Med Imaging. 2012; 31(2): 153–163. doi: 10.1109/TMI.2011.2163944 21827972

19. Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC. The Insight ToolKit image registration framework. Front Neuroinform. 2014; 8: 44. doi: 10.3389/fninf.2014.00044 24817849

20. Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. Elastix: A toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010; 29(1): 196–205. doi: 10.1109/TMI.2009.2035616 19923044

21. Metz C, Klein S, Schaap M, van Walsum T, Niessen WJ. Nonrigid registration of dynamic medical imaging data using nD+ t B-splines and a groupwise optimization approach. Med Image Anal. 2011; 15(2): 238–249. doi: 10.1016/j.media.2010.10.003 21075672

22. Huizinga W, Poot DH, Guyader JM, Klaassen R, Coolen BF, van Kranenburg M, et al. PCA-based groupwise image registration for quantitative MRI. Med Image Anal. 2016; 29: 65–78. doi: 10.1016/j.media.2015.12.004 26802910


Článek vyšel v časopise

PLOS One


2019 Číslo 10

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