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Proceedings Paper

Robust temporal alignment of multimodal cardiac sequences
Author(s): Andrea Perissinotto; Sandro Queirós; Pedro Morais; Maria J. Baptista; Mark Monaghan; Nuno F. Rodrigues; Jan D'hooge; João L. Vilaça; Daniel Barbosa
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Paper Abstract

Given the dynamic nature of cardiac function, correct temporal alignment of pre-operative models and intraoperative images is crucial for augmented reality in cardiac image-guided interventions. As such, the current study focuses on the development of an image-based strategy for temporal alignment of multimodal cardiac imaging sequences, such as cine Magnetic Resonance Imaging (MRI) or 3D Ultrasound (US). First, we derive a robust, modality-independent signal from the image sequences, estimated by computing the normalized cross-correlation between each frame in the temporal sequence and the end-diastolic frame. This signal is a resembler for the left-ventricle (LV) volume curve over time, whose variation indicates different temporal landmarks of the cardiac cycle. We then perform the temporal alignment of these surrogate signals derived from MRI and US sequences of the same patient through Dynamic Time Warping (DTW), allowing to synchronize both sequences. The proposed framework was evaluated in 98 patients, which have undergone both 3D+t MRI and US scans. The end-systolic frame could be accurately estimated as the minimum of the image-derived surrogate signal, presenting a relative error of 1.6 ± 1.9% and 4.0 ± 4.2% for the MRI and US sequences, respectively, thus supporting its association with key temporal instants of the cardiac cycle. The use of DTW reduces the desynchronization of the cardiac events in MRI and US sequences, allowing to temporally align multimodal cardiac imaging sequences. Overall, a generic, fast and accurate method for temporal synchronization of MRI and US sequences of the same patient was introduced. This approach could be straightforwardly used for the correct temporal alignment of pre-operative MRI information and intra-operative US images.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131K (20 March 2015); doi: 10.1117/12.2082712
Show Author Affiliations
Andrea Perissinotto, ICVS/3B’s - PT Government Associate Lab. (Portugal)
Sandro Queirós, ICVS/3B’s - PT Government Associate Lab. (Portugal)
KU Leuven (Belgium)
Univ. do Minho (Portugal)
Pedro Morais, ICVS/3B’s - PT Government Associate Lab. (Portugal)
Maria J. Baptista, ICVS/3B’s - PT Government Associate Lab. (Portugal)
Mark Monaghan, King's College Hospital (United Kingdom)
Nuno F. Rodrigues, ICVS/3B’s - PT Government Associate Lab. (Portugal)
Univ. do Minho (Portugal)
Instituto Politécnico do Cávado e do Ave (Portugal)
Jan D'hooge, KU Leuven (Belgium)
João L. Vilaça, ICVS/3B’s - PT Government Associate Lab. (Portugal)
Instituto Politécnico do Cávado e do Ave (Portugal)
Daniel Barbosa, ICVS/3B’s - PT Government Associate Lab. (Portugal)
Instituto Politécnico do Cávado e do Ave (Portugal)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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