Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data

This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

[1]  Phillip Wolff,et al.  Causal reasoning with forces , 2015, Front. Hum. Neurosci..

[2]  Ricardo Vigário,et al.  Self-Supervised MRI Tissue Segmentation by Discriminative Clustering , 2014, Int. J. Neural Syst..

[3]  A Mang,et al.  A Generic Framework for Modeling Brain Deformation as a Constrained Parametric Optimization Problem to Aid Non-diffeomorphic Image Registration in Brain Tumor Imaging , 2012, Methods of Information in Medicine.

[4]  O. Faugeras,et al.  A variational approach to multi-modal image matching , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[5]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[6]  Rafael Verdú,et al.  Frequency implementation of the Euler-Lagrange equations for variational image registration , 2008, IEEE Signal Process. Lett..

[7]  Daniela Mier,et al.  Advantages in functional imaging of the brain , 2015, Front. Hum. Neurosci..

[8]  Larry D. Olson,et al.  Localization of epileptic foci using Multimodality neuroimaging , 2013, Int. J. Neural Syst..

[9]  Brian B. Avants,et al.  Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge , 2011, IEEE Transactions on Medical Imaging.

[10]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[11]  Dirk Vordermark,et al.  Ten years of progress in radiation oncology , 2011, BMC Cancer.

[12]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[13]  Juan Morales-Sánchez,et al.  Optimal Parameters Selection for Non-parametric Image Registration Methods , 2006, ACIVS.

[14]  Angelo Gemignani,et al.  Adaptive Filtering and Random Variables coefficient for Analyzing Functional Magnetic Resonance Imaging Data , 2013, Int. J. Neural Syst..

[15]  Christos Davatzikos,et al.  Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights , 2014, IEEE Transactions on Medical Imaging.

[16]  Jan Modersitzki,et al.  Numerical Methods for Image Registration , 2004 .

[17]  Christos Davatzikos,et al.  Graph-based deformable image registration , 2015 .

[18]  Nasser Kehtarnavaz,et al.  Brain Functional Localization: A Survey of Image Registration Techniques , 2007, IEEE Transactions on Medical Imaging.

[19]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[20]  G. Sharp,et al.  Vision 20/20: perspectives on automated image segmentation for radiotherapy. , 2014, Medical physics.

[21]  Rafael Verdú,et al.  A Fourier Domain Framework for Variational Image Registration , 2008, Journal of Mathematical Imaging and Vision.

[22]  Suleman Surti,et al.  Update on Time-of-Flight PET Imaging , 2015, The Journal of Nuclear Medicine.

[23]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[24]  J. Modersitzki,et al.  A unified approach to fast image registration and a new curvature based registration technique , 2004 .

[25]  Angelo Gemignani,et al.  Singular Spectrum Analysis and Adaptive Filtering Enhance the Functional connectivity Analysis of resting State fMRI Data , 2014, Int. J. Neural Syst..

[26]  Zhen Yuan,et al.  PET/SPECT molecular imaging in clinical neuroscience: recent advances in the investigation of CNS diseases. , 2015, Quantitative imaging in medicine and surgery.

[27]  Brigitte Rack,et al.  Apoptosis-related deregulation of proteolytic activities and high serum levels of circulating nucleosomes and DNA in blood correlate with breast cancer progression , 2011, BMC Cancer.