In this paper, we present an automatic solution for segmentation and quantification of the left atrium (LA) from 3D cardiac ultrasound. A model-based framework is applied, making use of (deformable) active surfaces to model the endocardial surfaces of cardiac chambers, allowing incorporation of a priori anatomical information in a simple fashion. A dual-chamber model (LA and left ventricle) is used to detect and track the atrio-ventricular (AV) plane, without any user input. Both chambers are represented by parametric surfaces and a Kalman filter is used to fit the model to the position of the endocardial walls detected in the image, providing accurate detection and tracking during the whole cardiac cycle. This framework was tested in 20 transthoracic cardiac ultrasound volumetric recordings of healthy volunteers, and evaluated using manual traces of a clinical expert as a reference. The 3D meshes obtained with the automatic method were close to the reference contours at all cardiac phases (mean distance of 0.03±0.6 mm). The AV plane was detected with an accuracy of −0.6±1.0 mm. The LA volumes assessed automatically were also in agreement with the reference (mean ±1.96 SD): 0.4±5.3 ml, 2.1±12.6 ml, and 1.5±7.8 ml at end-diastolic, end-systolic and pre-atrial-contraction frames, respectively. This study shows that the proposed method can be used for automatic volumetric assessment of the LA, considerably reducing the analysis time and effort when compared to manual analysis.
Ultrasound image has already been proved to be a useful tool for non-invasive strain quantifications in soft tissue.
While clinical applications only include cardiac imaging, the development of techniques suitable for musculoskeletal
system is an active area of research. On this study, a technique for speckle tracking on ultrasound images
using non-rigid image registration is presented. This approach is based on a single 2D+t registration procedure,
in which the temporal changes on the B-mode speckle patterns are locally assessed. This allows estimating
strain from ultrasound image sequences of tissues under deformation while imposing temporal smoothness in
the deformation field, originating smooth strain curves. METHODS: The tracking algorithm was systematically
tested on synthetic images and gelatin phantoms, under sinusoidal deformations with amplitudes between 0.5%
and 4.0%, at frequencies between 0.25Hz and 2.0Hz. Preliminary tests were also performed on Achilles tendons
isolated from human cadavers. RESULTS: The strain was estimated with deviations of -0.011%±0.053% on the
synthetic images and agreements of ±0.28% on the phantoms. Some tests with real tendons show good tracking
results. However, significant variability between the trials still exists. CONCLUSIONS: The proposed image
registration methodology constitutes a robust tool for motion and deformation tracking in both simulated and
real phantom data. Strain estimation in both cases reveals that the proposed method is accurate and provides
good precision. Although the ex-vivo results are still preliminary, the potential of the proposed algorithm is
promising. This suggests that further improvements, together with systematic testing, can lead to in-vivo and
clinical applications.
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