IVUS Longitudinal and Axial Registration for Atherosclerosis Progression Evaluation
Nikos Tsiknakis,
Constantinos Spanakis,
Panagiota Tsompou,
Georgia Karanasiou,
Gianna Karanasiou,
Antonis Sakellarios,
George Rigas,
Savvas Kyriakidis,
Michael Papafaklis,
Sotirios Nikopoulos,
Frank Gijsen,
Lampros Michalis,
Dimitrios I. Fotiadis,
Kostas Marias
Affiliations
Nikos Tsiknakis
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, Greece
Constantinos Spanakis
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, Greece
Panagiota Tsompou
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 45115 Ioannina, Greece
Georgia Karanasiou
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 45115 Ioannina, Greece
Gianna Karanasiou
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 45115 Ioannina, Greece
Antonis Sakellarios
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 45115 Ioannina, Greece
George Rigas
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Savvas Kyriakidis
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 45115 Ioannina, Greece
Michael Papafaklis
Department of Cardiology, Medical School, University of Ioannina, 45110 Ioannina, Greece
Sotirios Nikopoulos
Department of Cardiology, Medical School, University of Ioannina, 45110 Ioannina, Greece
Frank Gijsen
Department of Cardiology, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
Lampros Michalis
Department of Cardiology, Medical School, University of Ioannina, 45110 Ioannina, Greece
Dimitrios I. Fotiadis
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 45115 Ioannina, Greece
Kostas Marias
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, Greece
Intravascular ultrasound (IVUS) imaging offers accurate cross-sectional vessel information. To this end, registering temporal IVUS pullbacks acquired at two time points can assist the clinicians to accurately assess pathophysiological changes in the vessels, disease progression and the effect of the treatment intervention. In this paper, we present a novel two-stage registration framework for aligning pairs of longitudinal and axial IVUS pullbacks. Initially, we use a Dynamic Time Warping (DTW)-based algorithm to align the pullbacks in a temporal fashion. Subsequently, an intensity-based registration method, that utilizes a variant of the Harmony Search optimizer to register each matched pair of the pullbacks by maximizing their Mutual Information, is applied. The presented method is fully automated and only required two single global image-based measurements, unlike other methods that require extraction of morphology-based features. The data used includes 42 synthetically generated pullback pairs, achieving an alignment error of 0.1853 frames per pullback, a rotation error 0.93° and a translation error of 0.0161 mm. In addition, it was also tested on 11 baseline and follow-up, and 10 baseline and post-stent deployment real IVUS pullback pairs from two clinical centres, achieving an alignment error of 4.3±3.9 for the longitudinal registration, and a distance and a rotational error of 0.56±0.323 mm and 12.4°±10.5°, respectively, for the axial registration. Although the performance of the proposed method does not match that of the state-of-the-art, our method relies on computationally lighter steps for its computations, which is crucial in real-time applications. On the other hand, the proposed method performs even or better that the state-of-the-art when considering the axial registration. The results indicate that the proposed method can support clinical decision making and diagnosis based on sequential imaging examinations.
