Phase-Based Nonrigid Deformation for Digital Subtraction Angiography

We present a method for digital subtraction angiography based on phase-based nonrigid deformation with specific consideration for changes between the image pairs. Input images are transformed into a scale-space representation using complex-valued filter responses. We apply a novel selection criterion to discern object motion and actual change by comparing the magnitudes in the responses. By manipulating phase, we directly generate a deformed image without explicit calculation of motion vectors. Our method is particularly useful in angiographic imaging where subtle changes between the image pairs should be preserved within the deformation and subtraction process. The experiments show that the proposed method preserves contrast for vessels and tumor stains while reducing motion artifact, which is clinically meaningful.

[1]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

[2]  Max A. Viergever,et al.  Image Registration for Digital Subtraction Angiography , 1999, International Journal of Computer Vision.

[3]  Jesse Tanguay,et al.  A theoretical comparison of x-ray angiographic image quality using energy-dependent and conventional subtraction methods. , 2011, Medical physics.

[4]  Ian A. Cunningham,et al.  Theoretical and experimental comparison of image signal and noise for dual-energy subtraction angiography and conventional x-ray angiography , 2015, Medical Imaging.

[5]  Celestino Ordóñez,et al.  Detection of human vital signs in hazardous environments by means of video magnification , 2018, PloS one.

[6]  Ian A Cunningham,et al.  Energy subtraction angiography is comparable to digital subtraction angiography in terms of iodine Rose SNR. , 2016, Medical physics.

[7]  Jundong Liu,et al.  Local frequency representations for robust multimodal image registration , 2002, IEEE Transactions on Medical Imaging.

[8]  Kalpathi Ramakrishnan,et al.  DSA image registration using non-uniform MRF model and pivotal control points , 2013, Comput. Medical Imaging Graph..

[9]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

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

[11]  Max Grosse,et al.  Phase-based frame interpolation for video , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[13]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[14]  Hans Knutsson,et al.  Phase-based multidimensional volume registration , 2000, IEEE Transactions on Medical Imaging.

[15]  Hossein Pourghassem,et al.  Multiresolution Image Registration in Digital X-Ray Angiography with Intensity Variation Modeling , 2014, Journal of Medical Systems.

[16]  Frédo Durand,et al.  Phase-based video motion processing , 2013, ACM Trans. Graph..

[17]  Yasuyuki Matsushita,et al.  Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Mansour Nejati,et al.  Nonrigid Image Registration in Digital Subtraction Angiography Using Multilevel B-Spline , 2013, BioMed research international.

[19]  Yu Zhang,et al.  EEG classification using sparse Bayesian extreme learning machine for brain–computer interface , 2018, Neural Computing and Applications.

[20]  Nikos Komodakis,et al.  Approximate Labeling via Graph Cuts Based on Linear Programming , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Y. Bentoutou,et al.  An invariant approach for image registration in digital subtraction angiography , 2002, Pattern Recognit..

[22]  Hideaki Haneishi,et al.  Respiratory-synchronized digital subtraction angiography based on a respiratory phase matching method , 2018, Signal Image Video Process..

[23]  Stefan Roth,et al.  MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[24]  Patrick Bouthemy,et al.  Determining Occlusions from Space and Time Image Reconstructions , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Rachid Deriche,et al.  Symmetrical Dense Optical Flow Estimation with Occlusions Detection , 2002, ECCV.

[26]  Yi Yang,et al.  Occlusion Aware Unsupervised Learning of Optical Flow , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Ulrich Fink,et al.  Automated Pixel Shifting in Digital Subtraction Angiography — an Application of Cepstral Filtering , 1993 .

[28]  Xiaohui Zhang,et al.  A vessel segmentation method for serialized cerebralvascular DSA images based on spatial feature point set of rotating coordinate system , 2018, Comput. Methods Programs Biomed..