Multiview 3-D Echocardiography Fusion with Breath-Hold Position Tracking Using an Optical Tracking System

• Published in: Ultrasound in medicine & biology Vol. 42; no. 8; pp. 1998 - 2009
• Main Authors: Punithakumar, Kumaradevan, Hareendranathan, Abhilash R, McNulty, Alexander, Biamonte, Marina, He, Allen, Noga, Michelle, Boulanger, Pierre, Becher, Harald
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.08.2016
• Subjects: Breath Holding; Echocardiography; Echocardiography, Three-Dimensional - methods; Electrocardiogram; Evaluation Studies as Topic; Humans; Image Processing, Computer-Assisted - methods; Male; Multiview fusion; Observer Variation; Optical Imaging - methods; Optical tracking; Radiology; Reference Values; Reproducibility of Results; Signal-To-Noise Ratio; Wavelet transformation; Optical tracking; Multiview fusion; Echocardiography; Wavelet transformation; Electrocardiogram
• ISSN: 0301-5629; 1879-291X
• DOI: 10.1016/j.ultrasmedbio.2016.03.019

Abstract Recent advances in echocardiography allow real-time 3-D dynamic image acquisition of the heart. However, one of the major limitations of 3-D echocardiography is the limited field of view, which results in an acquisition insufficient to cover the whole geometry of the heart. This study proposes the novel approach of fusing multiple 3-D echocardiography images using an optical tracking system that incorporates breath-hold position tracking to infer that the heart remains at the same position during different acquisitions. In six healthy male volunteers, 18 pairs of apical/parasternal 3-D ultrasound data sets were acquired during a single breath-hold as well as in subsequent breath-holds. The proposed method yielded a field of view improvement of 35.4 ± 12.5%. To improve the quality of the fused image, a wavelet-based fusion algorithm was developed that computes pixelwise likelihood values for overlapping voxels from multiple image views. The proposed wavelet-based fusion approach yielded significant improvement in contrast (66.46 ± 21.68%), contrast-to-noise ratio (49.92 ± 28.71%), signal-to-noise ratio (57.59 ± 47.85%) and feature count (13.06 ± 7.44%) in comparison to individual views.