Time-resolved tracking of the atrioventricular plane displacement in Cardiovascular Magnetic Resonance (CMR) images

Background

Atrioventricular plane displacement (AVPD) is an indicator for systolic and diastolic function and accounts for 60% of the left ventricular, and 80% of the right ventricular stroke volume. AVPD is commonly measured clinically in echocardiography as mitral and tricuspid annular plane systolic excursion (MAPSE and TAPSE), but has not been applied widely in cardiovascular magnetic resonance (CMR). To date, there is no robust automatic algorithm available that allows the AVPD to be measured clinically in CMR with input in a single timeframe. This study aimed to develop, validate and provide a method that automatically tracks the left and right ventricular AVPD in CMR images, which can be used in the clinical setting or in applied cardiovascular research in multi-center studies.

Methods

The proposed algorithm is based on template tracking by normalized cross-correlation combined with a priori information by principal component analysis. The AVPD in each timeframe is calculated for the left and right ventricle separately using CMR long-axis cine images of the 2, 3, and 4-chamber views.

The algorithm was developed using a training set (n = 40), and validated in a test set (n = 113) of healthy subjects, athletes, and patients after ST-elevation myocardial infarction from 10 centers. Validation was done using manual measurements in end diastole and end systole as reference standard. Additionally, AVPD, peak emptying velocity, peak filling velocity, and atrial contraction was validated in 20 subjects, where time-resolved manual measurements were used as reference standard. Inter-observer variability was analyzed in 20 subjects.

Results

In end systole, the difference between the algorithm and the reference standard in the left ventricle was (mean ± SD) -0.6 ± 1.9 mm (R = 0.79), and −0.8 ± 2.1 mm (R = 0.88) in the right ventricle. Inter-observer variability in end systole was −0.6 ± 0.7 mm (R = 0.95), and −0.5 ± 1.4 mm (R = 0.95) for the left and right ventricle, respectively. Validation of peak emptying velocity, peak filling velocity, and atrial contraction yielded lower accuracy than the displacement measures.

Conclusions

The proposed algorithm show good agreement and low bias with the reference standard, and with an agreement in parity with inter-observer variability. Thus, it can be used as an automatic method of tracking and measuring AVPD in CMR.

Electronic supplementary material

The online version of this article (doi:10.1186/s12880-017-0189-5) contains supplementary material, which is available to authorized users.

Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery

Ultrasound images are acquired before and after the resection of brain tumors to help the surgeon to localize the tumor and its extent and to minimize the amount of residual tumor after the resection. Because the brain undergoes large deformation between these two acquisitions, deformable image-based registration of these data sets is of substantial clinical importance. In this work, we present an algorithm for non-rigid registration of ultrasound images (RESOUND) that models the deformation with free-form cubic B-splines. We formulate a regularized cost function that uses normalized cross-correlation as the similarity metric. To optimize the cost function, we calculate its analytic derivative and use the stochastic gradient descent technique to achieve near real-time performance. We further propose a robust technique to minimize the effect of non-corresponding regions such as the resected tumor and possible hemorrhage in the post-resection image. Using manually labeled corresponding landmarks in the pre- and post-resection ultrasound volumes, we illustrate that our registration algorithm reduces the mean target registration error from an initial value of 3.7 to 1.5 mm. We also compare RESOUND with the previous work of Mercier et al. (2013) and illustrate that it has three important advantages: (i) it is fully automatic and does not require a manual segmentation of the tumor, (ii) it produces smaller registration errors and (iii) it is about 30 times faster. The clinical data set is available online on the BITE database website.

Monitoring and staging abdominal aortic aneurysm disease with pulse wave imaging

The abdominal aortic aneurysm (AAA) is a silent and often deadly vascular disease caused by the localized weakening of the arterial wall. Previous work has indicated that local changes in wall stiffness can be detected with pulse wave imaging (PWI), which is a non-invasive technique for tracking the propagation of pulse waves along the aorta at high spatial and temporal resolutions. The aim of this study was to assess the capability of PWI to monitor and stage AAA progression in a murine model of the disease. ApoE/TIMP-1 knockout mice (N = 18) were given angiotensin II for 30 days via subcutaneously implanted osmotic pumps. The suprarenal sections of the abdominal aortas were imaged every 2-3 d after implantation using a 30-MHz VisualSonics Vevo 770 with 15-μm lateral resolution. Pulse wave propagation was monitored at an effective frame rate of 8 kHz by using retrospective electrocardiogram gating and by performing 1-D cross-correlation on the radiofrequency signals to obtain the displacements induced by the waves. In normal aortas, the pulse waves propagated at constant velocities (2.8 ± 0.9 m/s, r(2) = 0.89 ± 0.11), indicating that the composition of these vessels was relatively homogeneous. In the mice that developed AAAs (N = 10), the wave speeds in the aneurysm sac were 45% lower (1.6 ± 0.6 m/s) and were more variable (r(2) = 0.66 ± 0.23). Moreover, the wave-induced wall displacements were at least 80% lower within the sacs compared with the surrounding vessel. Finally, in mice that developed fissures (N = 5) or ruptures (N = 3) at the sites of their AAA, higher displacements directed out of the lumen and with no discernible wave pattern (r(2) < 0.20) were observed throughout the cardiac cycle. These findings indicate that PWI can be used to distinguish normal murine aortas from aneurysmal, fissured and ruptured ones. Hence, PWI could potentially be used to monitor and stage human aneurysms by providing information complementary to standard B-mode ultrasound.

Lesion edge preserved direct average strain estimation for ultrasound elasticity imaging

Elasticity imaging techniques with built-in or regularization-based smoothing feature for ensuring strain continuity are not intelligent enough to prevent distortion or lesion edge blurring while smoothing. This paper proposes a novel approach with built-in lesion edge preservation technique for high quality direct average strain imaging. An edge detection scheme, typically used in diffusion filtering is modified here for lesion edge detection. Based on the extracted edge information, lesion edges are preserved by modifying the strain determining cost function in the direct-average-strain-estimation (DASE) method. The proposed algorithm demonstrates approximately 3.42-4.25 dB improvement in terms of edge-mean-square-error (EMSE) than the other reported regularized or average strain estimation techniques in finite-element-modeling (FEM) simulation with almost no sacrifice in elastographic-signal-to-noise-ratio (SNRe) and elastographic-contrast-to-noise-ratio (CNRe) metrics. The efficacy of the proposed algorithm is also tested for the experimental phantom data and in vivo breast data. The results reveal that the proposed method can generate a high quality strain image delineating the lesion edge more clearly than the other reported strain estimation techniques that have been designed to ensure strain continuity. The computational cost, however, is little higher for the proposed method than the simpler DASE and considerably higher than that of the 2D analytic minimization (AM2D) method.