Robust spatio-temporal registration of 4D cardiac ultrasound sequences

Abstract

Registration of multiple 3D ultrasound sectors in order to provide an extended field of view is important for the appreciation of larger anatomical structures at high spatial and temporal resolution. In this paper, we present a method for fully automatic spatio-temporal registration between two partially overlapping 3D ultrasound sequences. The temporal alignment is solved by aligning the normalized cross correlation-over-time curves of the sequences. For the spatial alignment, corresponding 3D Scale Invariant Feature Transform (SIFT) features are extracted from all frames of both sequences independently of the temporal alignment. A rigid transform is then calculated by least squares minimization in combination with random sample consensus. The method is applied to 16 echocardiographic sequences of the left and right ventricles and evaluated against manually annotated temporal events and spatial anatomical landmarks. The mean distances between manually identified landmarks in the left and right ventricles after automatic registration were (mean ± SD) 4.3 ± 1.2 mm compared to a reference error of 2.8 ± 0.6 mm with manual registration. For the temporal alignment, the absolute errors in valvular event times were 14.4 ± 11.6 ms for Aortic Valve (AV) opening, 18.6 ± 16.0 ms for AV closing, and 34.6 ± 26.4 ms for mitral valve opening, compared to a mean inter-frame time of 29 ms.

Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces

“…As the reference segmentations were performed separately for each sector, and in different tools, the fused references were slightly inconsistent, e.g., the septum of the LV-EPI and RV reference surfaces were slightly disjoint, introducing additional uncertainty in the surface error measurements. It is worth noting that methods for automated registration of multiview 3-D ultrasound have been reported with promising results, [26][27][28][29] which could eliminate this time-consuming task and potentially increase the registration accuracy, and consequently the segmentation accuracy as well.…”

Multi-View 3-D Fusion Echocardiography: Enhancing Clinical Feasibility with a Novel Processing Technique

“…3-D fusion by registration has been successfully applied to fuse 3-D data sets by several groups (Hill et al 2001;Grau et al 2007;Sra 2008;Gooding et al 2010;Szmigielski et al 2010;Rajpoot et al 2011b;Bersvendsen et al 2016;Danudibroto et al 2016;Peressutti et al 2017). However, on its own, registration is of limited value, because contributing data sets need to demonstrate a significant degree of overlap.…”

Uncertainty-aware asynchronous scattered motion interpolation using Gaussian process regression

“…Several groups have investigated the problem of spatiotemporal image registration (Bersvendsen et al (2016); Ledesma-Carbayo et al (2005); Perperidis et al (2005); Shi et al (2013); Vandemeulebroucke et al (2011)). Perperidis et al (2005) addressed the spatiotemporal iconic, i.e., intensity-based, registration of 3D image sequences by devising 4D affine and free-form deformation (FFD) (based on a 4D B-Spline model) models that are separated into spatial and temporal components.…”

“…Bersvendsen et al (2016) addressed the temporal alignment by optimizing the alignment of the normalized cross correlation (NCC)-over-time curves of the sequences (Perperidis et al (2005) also employed NCC, in the optimization of their temporal component), within their proposed fully automatic method for spatiotemporal (spatially rigid) registration between 145 two partially overlapping 3D image sequences. However, Bersvendsen et al (2016), Ledesma-Carbayo et al (2005), Perperidis et al (2005), and Shi et al (2013) do not estimate the transformation in an uncertainty-aware fashion. Moreover, in contrast to our approach, they do not address the problem of interpolating randomly non-uniformly scattered spatiotemporal motion signal samples, one that arises in the context of spatiotemporal geometric, i.e., landmark-based, image registration.…”