Validation of Registration for Renal Dynamic Contrast Enhanced MRI Imaging

Yıl 2016, Cilt: 4 Sayı: 3, 57 - 65, 01.11.2016

Seniha Esen Yuksel

https://doi.org/10.18201/ijisae.45496

Öz

In Dynamic Contrast Enhanced Resonance Imaging (DCE-MRI), abdomen is scanned repeatedly and rapidly after injection of a contrast agent. During data acquisition, collected images suffer from the motion induced by the patient if he/she moves or breathes heavily during the scan. Therefore, these images should be aligned accurately to correct the motion. Recently, mutual information (MI) registration has become the first tool to register renal DCE-MRI images before any further processing. However, MI registration is sensitive to initial conditions and optimization methods, and it is bound to fail under certain conditions such as extreme movement or noise in the image. Therefore, if automated image analysis for renal DCE-MRI is to enter the clinical settings, it is necessary to have validation strategies that show the limitations of registration models on known datasets. In this study, two methods are introduced for the validation of registration of renal DCE-MRI images. The first method demonstrates how to use the inverse transform to generate realistic looking DCE-MRI kidney images and use them in validation. The second method shows how to generate checkerboard images and how to evaluate the goodness of registration for real DCE-MRI images. These validation methods can be incorporated into the registration studies to quantitatively and qualitatively demonstrate the success and the limitations of registration models.

Anahtar Kelimeler

Validation, Inverse Transform, Renal, DCE-MRI

Kaynakça

• M. Notohamiprodjo, M. F. Reiser, S. P. Sourbron, Diffusion and perfusion of the kidney, European Journal of Radiology 76 (3) (2010) 337 – 347.
• G. Brix, S. Zwick, J. Griebel, C. Fink, F. Kiessling, Estima- tion of tissue perfusion by dynamic contrast-enhanced imag- ing: simulation-based evaluation of the steepest slope method, European Radiology 20 (9) (2010) 2166–2175.
• V. Positano, I. Bernardeschi, V. Zampa, M. Marinelli, L. Lan- dini, M. Santarelli, Automatic 2D registration of renal per- fusion image sequences by mutual information and adaptive prediction, Magnetic Resonance Materials in Physics, Biology and Medicine 26 (3) (2013) 325–335.
• F. Khalifa, G. Beache, T. El-Diasty, G. Gimelfarb, M. Kong, A. El-Baz, Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection, IEEE transactions on medical imaging 32 (10) (2013), 1910–1927.
• F. Khalifa, M. Abou El-Ghar, B. Abdollahi, H. B. Frieboes, T. El-Diasty, A. El-Baz, A comprehensive noninvasive framework for automated evaluation of acute renal transplant rejection using DCE-MRI, NMR in Biomedicine 26(11) (2013).
• F. Zollner, R. Sance, P. Rogelj, M. J. Ledesma-Carbayo, J. Rorvik, A. Santos, A. Lundervold, Assessment of 3D DCE- MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses., Comp. Med. Imag. and Graph. 33 (3) (2009) 171–181.
• X. Li, X. Chen, J. Yao, X. Zhang, F. Yang, J. Tian, Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search, Medical Imaging, IEEE Transactions on 31 (10) (2012) 1849–1860.
• O. Gloger, K. Tonnies, V. Liebscher, B. Kugelmann, R. Laqua, H. Volzke, Prior shape level set segmentation on multistep generated probability maps of MR datasets for fully automatic kidney parenchyma volumetry, Medical Imaging, IEEE Transactions on 31 (2) (2012) 312–325.
• P. Gujral, M. Amrhein, D. Bonvin, J.P. Vallee, X. Montet, N. Michoux, Classification of magnetic resonance images from rabbit renal perfusion, Chemometrics and Intelligent Laboratory Systems 98 (2) (2009) 173 – 181.
• G. Chiusano, A. Stagliano, C. Basso, A. Verri, DCE-MRI Analysis Using Sparse Adaptive Representations, Vol. 7009, Machine Learning in Medical Imaging, Lecture Notes in Computer Science, Springer, 2011, pp. 67–74.
• L. Ruthotto, E. Hodneland, J. Modersitzki, Registration of dynamic contrast enhanced MRI with local rigidity constraint, in: Proceedings of the 5th international conference on Biomedical Image Registration, Springer-Verlag, 2012, pp. 190–198.
• S. E. Yuksel, A. El-Baz, A. A. Farag, M. El-Ghar, T. Eldiasty, M. A. Ghoneim, A kidney segmentation framework for dynamic contrast enhanced magnetic resonance imaging, Journal of Vibration and Control 13 (9-10) (2007) 1505–1516.
• S. E. Yuksel, A. El-Baz, A. A. Farag, M. El-Ghar, T. Eldiasty, M. A. Ghoneim, Automatic detection of renal rejection after kidney transplantation, in: Proc. of Computer Assisted Radiology and Surgery (CARS), 2005, pp. 773–778.
• A. A. Farag, A. El-Baz, S. E. Yuksel, M. El-Ghar, T. Eldiasty, A framework for the detection of acute renal rejection with dy- namic contrast enhanced magnetic resonance imaging, in: Proceedings of International Symposium on Biomedical Imaging (ISBI), 2006, pp. 418–421.
• A. El-Baz, R. Fahmi, S. E. Yuksel, A. A. Farag, W. Miller, M. El-Ghar, T. Eldiasty, A new CAD system for the evaluation of kidney diseases using DCE–MRI, in: Proc. of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Vol. 2, 2006, pp. 446–453.
• B. Chevaillier, J.L. Collette, D. Mandry, M. Claudon, O. Pietquin, Objective assessment of renal DCE-MRI image segmentation, in: Proceedings of the European Signal Processing Conference (EUSIPCO), 2010, pp. 1214–1218.
• L. Bokacheva, H. Rusinek, J. L. Zhang, V. S. Lee, Assessment of renal function with dynamic contrast-enhanced MR imaging, Magnetic Resonance Imaging Clinics of North America 16 (4) (2008) 597 – 611.
• B. Chevaillier, Y. Ponvianne, J. Collette, D. Mandry, M. Claudon, O. Pietquin, Functional semi-automated segmentation of renal DCE-MRI sequences, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2008, pp. 525–528.
• M. S. Aslan, H. A. Munim, A. A. Farag, M. A. El-Ghar, Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, IGI Global, 2010, Ch. Assessment of Kidney Function Using Dynamic Contrast En- hanced MRI Techniques, pp. 214–233.
• L. Dalla-Palma, G. Panzetta, R. Pozzi-Mucelli, G. Galli, M. Cova, S. Meduri, Dynamic magnetic resonance imaging in the assessment of chronic medical nephropathies with impaired renal function, Eur Radiol 10(2) (2000) 280–286.
• N. Michoux, J.-P. Vallee, A. Pechere-Bertschi, X. Montet, L. Buehler, B. Van Beers, Analysis of contrast-enhanced MR images to assess renal function, Magnetic Resonance Materials in Physics, Biology and Medicine 19 (2006) 167–179.
• Y.Sun,J.Moura,C.Ho,Subpixelregistrationinrenalperfusion MR image sequence, in: Proc. 2004 IEEE Int. Symp. Biomed- ical Imaging, 2004, pp. 700–703.
• A. Agildere, N. Tarhan, G. Bozdagi, A. Demirag, E. Niron, M. Haberal, Correlation of quantitative dynamic magnetic resonance imaging findings with pathology results in renal transplants: A preliminary report, Transplantation Proceedings 31(8) (1999) 3312–3316.
• D. Mahapatra, Y. Sun, Rigid registration of renal perfusion images using a neurobiology-based visual saliency model, Eurasip Journal of Image and Video Proc. (2010) 4:1–4:22.
• D. Mahapatra, Y. Sun, Registration of dynamic renal MR images using neurobiological model of saliency, in: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2008, pp. 1119–1122.
• Y.Boykov,V.Lee,H.Rusinek,R.Bansal,Segmentation of dynamic N-D data sets via graph cuts using Markov models, in: Proceedings of the 4th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2001, pp. 1058–1066.
• T. Song, V. S. Lee, Q. Chen, H. Rusinek, A. F. Laine, An auto- mated three-dimensional plus time registration framework for dynamic mr renography, Journal of Visual Communication and Image Representation 21 (1) (2010) 1–8.
• G. Gerig, R. Kikinis, W. Kuoni, G. van Schulthess, O. Kubler, Semiautomated ROI analysis in dynamic MRI studies: Part I: image analysis tools for automatic correction of organ dis- placements, IEEE Trans. Image Proc. 11:(2) (1992) 221–232.
• E. Giele, J. de Priester, J. Blom, J. den Boer, J. van En- gelshoven, A. Hasman, M. Geerlings, Movement correction of the kidney in dynamic MRI scans using FFT phase difference movement detection, J. Magn Reson Imaging 14(6) (2001) 741–749.
• J. dePriester, A. Kessels, E. Giele, J. denBoer, M. Christiaans, A. Hasman, J. van Engelshoven, MR renography by semiautomated image analysis: performance in renal transplant recipients, J. Magn Reson Imaging 14(2) (2001) 134–140.
• F. Khalifa, G. M. Beache, G. Gimel’farb, J. S. Suri, A. S. El- Baz, Multi modality state-of-the-art medical image segmentation and registration methodologies, Springer New York, 2011, Ch. State-of-the-Art Medical Image Registration Methodologies: A Survey, pp. 235–280.
• A. El-Baz, G. Gimel’farb, M. A. El-Ghar, New motion correction models for automatic identification of renal transplant rejection, in: Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention (MICCAI), 2007, pp. 235–243.
• R. Sance, M. J. L. Carbayo, A. Lundervold, A. Santos, Image registration for quantitative analysis of kidney function using MRI, in: 5th International Workshop on Information Optics (WIO), Vol. 860, 2006, pp. 420–426.
• R. Sance, M. Ledesma-Carbayo, A. Lundervold, A. Santos, Alignment of 3D DCE-MRI abdominal series for optimal quantification of kidney function, in: 5th Int. Symp. on Image and Signal Proc. and Analysis, 2007, pp. 413–417.
• Y. Sun, M. Jolly, J. M. F. Moura, Integrated registration of dynamic renal perfusion MR images, in: Proc. 2004 IEEE Int. Conf. on Image Proc., 2004, pp. 1923–1926.
• T. Song,V. Lee, H. Rusinek, S. Wong, A. Laine, Integrated four dimensional registration and segmentation of dynamic renal MR images, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Vol. 4191, 2006, pp. 758–765.
• P. Yim, H. Marcos, M. McAuliffe, D. McGarry, I. Heaton, P. Choyke, Registration of time-series contrast enhanced magnetic resonance images for renography, in: Proc. 14th IEEE Symp. Computer Based Medical Systems, 2001, pp. 516–520.
• K. Passera, L. Mainardi, D. McGrath, J. Naish, D. Buckley, S. Cheung, Y. Watson, A. Caunce, G. Buonaccorsi, J. Logue, M. Taylor, C. Taylor, J. Waterton, H. Young, G. Parker, A non-linear registration method for DCE-MRI and DCE-CT comparison in bladder tumors, in: IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, 2008, pp. 1095–1098.
• D. Zikic, S. Sourbron, X. Feng, H. J. Michaely, A. Khamene, N. Navab, Automatic alignment of renal DCE-MRI image series for improvement of quantitative tracer kinetic studies, in: SPIE Medical Imaging, San Diego, California, USA, 2008.
• A. D. Merrem, A variational approach to image registration in DCE-MRI of human kidney, in: Proc. Intl. Soc. Mag. Reson. Med., Vol. 19, 2011, p. 815.
• K. E. Jannin P, W. S., Guest editorial validation in medical image processing, Medical Imaging, IEEE Transactions on 25 (11) (2006) 1405–1409.
• J. Pluim, J. Maintz, M. Viergever, Mutual information based registration of medical images: A survey, IEEE Trans on Med- ical Imaging 22 (8) (2003) 986–1004.
• A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Seutens, G. Marchal, Automated multimodality image reg- istration using information theory, in: Proceedings of Informa- tion Processing in Medical Images, 1995, pp. 263–274.
• P. Viola, W. Wells, Alignment by maximization of mutual information, in: Proc. 5th Int. Conf. Computer Vision, 1995, pp. 16–23.
• F. Maes, D. Vandermeulen, P. Suetens, Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information, Medical Image Analysis 3 (1999) 373–386.
• F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Suetens, Multimodality image registration by maximization of mutual information, IEEE transactions on Medical Imaging 16 (2) (1997) 187–198.
• C. Studholme, D. L. G. Hill, D. J. Hawkes, An overlap invariant entropy measure of 3D medical image alignment, Pattern Recognition 32 (1) (1999) 71–86.
• L. Ibanez, W. Schroeder, L. Ng, J. C. et al., The ITK Software Guide, Kitware, Inc., 2005. URL http://www.itk.org/
• L. Devroye, Non-Uniform Random Variate Generation, Springer-Verlag New York, 1986.