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Aims Foreshortening of apical views is a common problem in echocardiography. It results in an abnormally thick false apex and a shortened left ventricular (LV) long axis. We sought to evaluate the impact of foreshortened (FS) on LV ejection fraction (LVEF) and layer-specific 2D speckle tracking based segmental (S) and global (G) longitudinal strain (LS) measurements. Methods and results We examined 72 participants using a GE Vivid E9 system. FS apical views were collected from an imaging window one rib-space higher than the optimal images. Ejection fraction as well as layer-specific GLS and SLS measurements were analysed by GE EchoPAC v201 and TomTec Image Arena 4.6 and compared between optimal and FS images. On average, LV long axis was 10% shorter in FS images than in optimal images. FS induced a relative change in LVEF of 3.3% and 6.9% for GE and TomTec, respectively (both, P Conclusion Optimal image geometry is crucial for accurate LV function assessment. Foreshorhening of apical views has a substantial impact on longitudinal strain measurements, predominantly in the apex and in the endocardial layer. Our data suggest that measuring midwall strain might therefore be the more robust approach for clinical routine use.

Segmentation of left ventricle (LV) from cardiac ultrasound images provides a quantitative functional analysis of the heart to diagnose disease. Active Shape Model (ASM) is widely used for LV segmentation, but it suffers from the drawback that initialization of the shape model is not sufficiently close to the target, especially when dealing with abnormal shapes in disease. In this work, a two-step framework is improved to achieve a fast and efficient LV segmentation. First, a robust and efficient detection based on Hough forest localizes cardiac feature points. Such feature points are used to predict the initial fitting of the LV shape model. Second, ASM is applied to further fit the LV shape model to the cardiac ultrasound image. With the robust initialization, ASM is able to achieve more accurate segmentation. The performance of the proposed method is evaluated on a dataset of 810 cardiac ultrasound images that are mostly abnormal shapes. This proposed method is compared with several combinations of ASM and existing initialization methods. Our experiment results demonstrate that accuracy of the proposed method for feature point detection for initialization was 40% higher than the existing methods. Moreover, the proposed method significantly reduces the number of necessary ASM fitting loops and thus speeds up the whole segmentation process. Therefore, the proposed method is able to achieve more accurate and efficient segmentation results and is applicable to unusual shapes of heart with cardiac diseases, such as left atrial enlargement., {"references":["C. Corsi and G. Saracino, \"Left ventricular volume estimation for\nreal-time three-dimensional echocardiography,\" IEEE Trans. Med. Imag.,\nvol. 21, no. 9, pp. 1202–1208, Sep. 2002.","T. McInerney, \"Deformable models in medical image analysis: a survey,\"\nMed. Image Anal., vol. 1, no. 2, pp. 91–108, 1996.","B. Georgescu, \"Databased-guided segmentation of anatomical structures\nwith complex appearance,\" Proc. Conf. CVPR, pp. 429–436, 2005.","T. Cootes, \"Active shape models—Their training and application,\"\nComput. Vis. Image Understand., vol. 61, no. 1, pp. 38–59, Jan. 1995.","J. Gall, \"Hough forests for object detection, tracking, and action\nrecognition,\" IEEE Trans. on PAMI., vol. 33, no. 11, pp. 2188–2202,\n2011.","P. Viola and M. Jones, \"Robust real-time face detection,\" Int. J. Comput.\nVis., vol. 57, no. 2, pp. 137–154, 2004."]}