A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound

In IVUS imaging, constant linear velocity and a constant angular velocity of 1800 rev/min causes displacement of the calcium in subsequent image frames. To overcome this error in intravascular ultrasound video, IVUS image frames must be registered prior to the lesion quantification. This paper presents a comprehensive comparison of four registration methods, namely: Rigid, Affine, B-Splines and Demons on five set of calcium lesion quantification parameters namely: (i) the mean lesion area, (ii) mean lesion arc, (iii) mean lesion span, (iv) mean lesion length, and (v) mean lesion distance from catheter. Using our IRB approved data of 100 patient volumes, our results shows that all four registrations showed a decrease in five calcium lesion parameters as follows: for Rigid registration, the values were: 4.92%, 5.84%, 5.89%, 5.27%, and 4.57%, respectively, for Affine registration the values were: 6.06%, 6.51%, 7.28%, 6.50%, and 5.94%, respectively, for B-Splines registration the values were: 7.35%, 8.03%, 9.54%, 8.18%, and 7.62%, respectively, and for Demons registration the five parameters were 7.32%, 8.02%, 10.11%, 7.94%, and 8.92% respectively. The relative overlap of identified lesions decreased by 5.91% in case of Rigid registration, 6.23% in case of Affine registration, 4.48% for Demons registration, whereas it increased by 3.05% in case of B-Splines registration. Rigid and Affine transformation-based registration took only 0.1936 and 0.2893 s per frame, respectively. Demons and B-Splines framework took only 0.5705 and 0.9405 s per frame, respectively, which were significantly slower than Rigid and Affine transformation based image registration.

[1]  P. Libby,et al.  Progress and challenges in translating the biology of atherosclerosis , 2011, Nature.

[2]  Seungyong Lee,et al.  Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions , 2000, Graph. Model..

[3]  K. Kennedy,et al.  Diabetes Duration Is Associated With Increased Thin-Cap Fibroatheroma Detected by Intravascular Ultrasound With Virtual Histology , 2009, Circulation. Cardiovascular interventions.

[4]  Petia Radeva,et al.  Non-rigid Registration of Vessel Structures in IVUS Images , 2003, IbPRIA.

[5]  Ayman El-Baz,et al.  State-of-the-Art Medical Image Registration Methodologies: A Survey , 2011 .

[6]  D. Rueckert,et al.  Comparison and evaluation of rigid, affine, and nonrigid registration of breast MR images. , 1999, Journal of computer assisted tomography.

[7]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[8]  T F Budinger,et al.  Nuclear Magnetic Resonance (NMR) In Vivo Studies: Known Thresholds for Health Effects , 1981, Journal of computer assisted tomography.

[9]  Nassir Navab,et al.  Iterative Self-Organizing Atherosclerotic Tissue Labeling in Intravascular Ultrasound Images and Comparison With Virtual Histology , 2012, IEEE Transactions on Biomedical Engineering.

[10]  Radhika Sivaramakrishna,et al.  Breast image registration techniques: a survey , 2006, Medical and Biological Engineering and Computing.

[11]  Josepa Mauri,et al.  MULTIMODAL REGISTRATION OF INTRAVASCULAR ULTRASOUND IMAGES AND ANGIOGRAPHY , 2002 .

[12]  João Manuel R S Tavares,et al.  Medical image registration: a review , 2014, Computer methods in biomechanics and biomedical engineering.

[13]  D. Louis Collins,et al.  Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric , 2012, MICCAI.

[14]  Petia Radeva,et al.  Fast Rigid Registration of Vascular Structures in IVUS Sequences , 2009, IEEE Transactions on Information Technology in Biomedicine.

[15]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[16]  Christos P. Loizou,et al.  Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery , 2014, Comput. Methods Programs Biomed..

[17]  J. Suri,et al.  Link between automated coronary calcium volumes from intravascular ultrasound to automated carotid IMT from B-mode ultrasound in coronary artery disease population. , 2014, International angiology : a journal of the International Union of Angiology.

[18]  J. Weiss,et al.  Strain measurement in coronary arteries using intravascular ultrasound and deformable images. , 2002, Journal of biomechanical engineering.

[19]  Shigeru Oshima,et al.  Coronary artery calcification as a new predictor of non-target lesion revascularization during the chronic phase after successful percutaneous coronary intervention , 2014, Cardiovascular Intervention and Therapeutics.

[20]  A. El-Baz.  Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume 1 , 2016 .

[21]  Savita Gupta,et al.  A hybrid framework for registration of carotid ultrasound images combining iconic and geometric features , 2013, Medical & Biological Engineering & Computing.

[22]  Petia Radeva,et al.  Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation , 2011, IbPRIA.

[23]  Jasjit S. Suri,et al.  Effect of Geometric-Based Coronary Calcium Volume as a Feature Along with its Shape-Based Attributes for Cardiological Risk Prediction from Low Contrast Intravascular Ultrasound , 2014 .

[24]  Daniel Rueckert,et al.  Non-rigid registration using free-form deformations , 2015 .

[25]  D. Louis Collins,et al.  Self-similarity weighted mutual information: A new nonrigid image registration metric , 2014, Medical Image Anal..

[26]  Nacim Betrouni,et al.  Elastic image registration for guiding focal laser ablation of prostate cancer: Preliminary results , 2012, Comput. Methods Programs Biomed..

[27]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[28]  Boudewijn P. F. Lelieveldt,et al.  Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology , 2013, The International Journal of Cardiovascular Imaging.

[29]  P. Serruys,et al.  Imaging Imaging of coronary atherosclerosis : intravascular ultrasound , 2010 .

[30]  Patrick W Serruys,et al.  Imaging of coronary atherosclerosis: intravascular ultrasound. , 2010, European heart journal.

[31]  David Gur,et al.  Bidirectional elastic image registration using B-spline affine transformation , 2014, Comput. Medical Imaging Graph..

[32]  Dongjin Han,et al.  A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA , 2014, Comput. Methods Programs Biomed..

[33]  Morton H. Friedman,et al.  Quantification of cross-sectional artery wall motion with IVUS image registration , 2004, SPIE Medical Imaging.

[34]  V. Bhargava,et al.  Axial movement of the intravascular ultrasound probe during the cardiac cycle: implications for three-dimensional reconstruction and measurements of coronary dimensions. , 1999, American heart journal.

[35]  Wan-Yuo Guo,et al.  Quantitative analysis of digital subtraction angiography using optical flow method on occlusive cerebrovascular disease , 2013, Comput. Methods Programs Biomed..

[36]  Morton H. Friedman,et al.  Estimation of Arterial Wall Strain Based on IVUS Image Registration , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[37]  Olivier Ecabert,et al.  Image-based Co-Registration of Angiography and Intravascular Ultrasound Images , 2013, IEEE Transactions on Medical Imaging.