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Objectives: To validate deformable registration algorithms (DRAs) for cine balanced steady-state free precession (bSSFP) assessment of global longitudinal strain (GLS) and global circumferential strain (GCS) using harmonic phase (HARP) cardiovascular magnetic resonance as standard of reference (SoR)., Methods: Seventeen patients and 17 volunteers underwent short axis stack and 2-/4-chamber cine bSSFP imaging with matching slice long-axis and mid-ventricular spatial modulation of magnetization (SPAMM) myocardial tagging. Inverse DRA was applied on bSSFP data for assessment of GLS and GCS while myocardial tagging was processed using HARP. Intra- and inter-observer variability assessment was based on repeated analysis by a single observer and analysis by a second observer, respectively. Standard semi-automated short axis stack segmentation was performed for analysis of left ventricular (LV) volumes and ejection fraction (EF)., Results: DRA demonstrated strong relationships to HARP for myocardial GLS (R 2  = 0.75; p < 0.0001) and endocardial GLS (R 2  = 0.61; p < 0.0001). GCS result comparison also demonstrated significant relationships between DRA and HARP for myocardial strain (R 2  = 0.61; p < 0.0001) and endocardial strain (R 2  = 0.51; p < 0.0001). Both methods demonstrated small systematic errors for intra- and inter-observer variability but DRA demonstrated consistently lower CV. Global LVEF was significantly lower (p = 0.0099) in patients (53.7%; IQR 43.9/64.0%) than in healthy volunteers (62.6%; IQR 61.1/66.2%). DRA and HARP strain data demonstrated significant relationships to LVEF., Conclusions: Non-rigid deformation method-based DRA provides a reliable measure of peak systolic GCS and GLS based on cine bSSFP with superior intra- and inter-observer reproducibility compared to HARP., Key Point: • Myocardial strain can be reliably analyzed using inverse deformable registration algorithms (DRAs) on cine CMR. • Inverse DRA-derived strain shows higher reproducibility than tagged CMR. • DRA and tagged CMR-based myocardial strain demonstrate strong relationships to global left ventricular function.

The cine magnetic resonance imaging based technique feature tracking-cardiac magnetic resonance (FT-CMR) is emerging as a novel, simple and robust method to evaluate myocardial strain. We investigated the distribution characteristics of left-ventricular myocardial strain using a novel cine MRI based deformation registration algorithm (DRA) in a cohort of healthy Chinese subjects. A total of 130 healthy Chinese subjects were enrolled. Three components of orthogonal strain (radial, circumferential, longitudinal) of the left ventricle were analyzed using DRA on steady-state free precession cine sequence images. A distinct transmural circumferential strain gradient was observed in the left ventricle that showed universal increment from the epicardial to endocardial myocardial wall (epiwall: -15.4 ± 1.9%; midwall: -18.8 ± 2.0%; endowall: -22.3 ± 2.3%, P < 0.001). Longitudinal strain showed a similar trend from epicardial to endocardial layers (epiwall: -16.0 ± 2.9%; midwall: -15.6 ± 2.7%; endowall: -14.8 ± 2.4%, P < 0.001), but radial strain had a very heterogeneous distribution and variation. In the longitudinal direction from the base to the apex of the left ventricle, there was a trend of decreasing peak systolic longitudinal strain (basal: -23.3 ± 4.6%; mid: -13.7 ± 7.3%; apical: -13.2 ± 5.5%; P < 0.001). In conclusion, there are distinct distribution patterns of circumferential and longitudinal strain within the left ventricle in healthy Chinese subjects. These distribution patterns of strain may provide unique profiles for further study in different types of myocardial disease.

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73±11.24years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org)., (Copyright © 2013 Elsevier B.V. All rights reserved.)

This paper describes a system to automatically segment the left ventricle in all slices and all phases of cardiac cine magnetic resonance datasets. After localizing the left ventricle blood pool using motion, thresholding and clustering, slices are segmented sequentially. For each slice, deformable registration is used to align all the phases, candidates contours are recovered in the average image using shortest paths, and a minimal surface is built to generate the final contours. The advantage of our method is that the resulting contours follow the edges in each phase and are consistent over time. We demonstrate using 19 patient examples that the results are very good. The RMS distance between ground truth and our segmentation is only 1.6 pixels (2.7 mm) and the Dice coefficient is 0.89.

This paper presents a new segmentation algorithm which combines active shape model and robust point matching techniques. It can use any simple feature detector to extract a large number of feature points in the image. Robust point matching is then used to search for the correspondences between feature and model points while the model is being deformed along the modes of variation of the active shape model. Although the algorithm is generic, it is particularly suited for medical imaging applications where prior knowledge is available. The value of the proposed method is examined with two different medical imaging modalities (Ultrasound, MRI) and in both 2D and 3D. The experiments have shown that the proposed algorithm is immune to missing feature points and noise. It has demonstrated significant improvements when compared to RPM-TPS and ASM alone.