Differential atrial performance at rest and exercise in athletes: Potential trigger for developing atrial dysfunction?

Highly trained athletes show an increased risk of atrial arrhythmias. Little is known about atrial volumes and function during exercise in this population. Our aim was to analyze atrial size and contractile function during exercise. Fifty endurance athletes with 11 ± 8 h of training per week and 30 sedentary control subjects were included. Echocardiography was performed at baseline and during exercise. Left (LA) and right atrial (RA) size and function were assessed by two-dimensional echocardiography. Peak negative strain (Sa) during atrial contraction and active atrial emptying volume (AEV) were measured. Athletes and control subjects showed a significant increment of deformation and AEV of both atria with exercise (P < 0.01 vs baseline for LA and RA). Among athletes, a subgroup with significant LA (n = 8)/RA (n = 15) dilatation (≥40 mL/m2 ) showed a significantly lower increment in AEV with exercise (LA∆AEV: 1.4 ± 1.1 mL/m2 vs 2.1 ± 0.9 mL/m2 , P = 0.04; RA∆AEV: 0.9 ± 0.8 mL/m2 vs 2.3 ± 1.1 mL/m2 , P < 0.01) and lower increment in deformation vs other athletes (LA∆Sa: -3.2 ± 2.9% vs -9.5 ± 4.4%, P < 0.01; RA∆Sa: -2.5 ± 3.3% vs. -9.8 ± 3.3%, P < 0.01). During exercise, active atrial strain increases, but less in athletes compared to controls, but due to larger atrial volumes, they reached similar increases in atrial emptying volume. However, this overall lesser deformation increases from a subgroup with significant atrial dilatation showing impairment in atrial contractile reserve.

Improved myocardial motion estimation combining tissue Doppler and B-mode echocardiographic images.

We propose a technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved. The velocity field is modeled continuously using B-splines and the spatiotemporal transform is constrained to be diffeomorphic. Images before scan conversion are used to improve the accuracy of the estimation. The similarity measure includes a model of the speckle pattern distribution of B-mode images. It also penalizes the disagreement between tissue Doppler velocities and the estimated velocity field. Registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset, obtaining mean displacement errors of about 1 mm. Finally, the method is demonstrated on data acquired from six volunteers, both at rest and during exercise. Robustness is tested against low image quality and fast heart rates during exercise. Results show that our method provides a robust motion estimate in these situations.

Cardiac motion estimation by joint alignment of tagged MRI sequences.

Image registration has been proposed as an automatic method for recovering cardiac displacement fields from tagged Magnetic Resonance Imaging (tMRI) sequences. Initially performed as a set of pairwise registrations, these techniques have evolved to the use of 3D+t deformation models, requiring metrics of joint image alignment (JA). However, only linear combinations of cost functions defined with respect to the first frame have been used. In this paper, we have applied k-Nearest Neighbors Graphs (kNNG) estimators of the α-entropy (H(α)) to measure the joint similarity between frames, and to combine the information provided by different cardiac views in an unified metric. Experiments performed on six subjects showed a significantly higher accuracy (p<0.05) with respect to a standard pairwise alignment (PA) approach in terms of mean positional error and variance with respect to manually placed landmarks. The developed method was used to study strains in patients with myocardial infarction, showing a consistency between strain, infarction location, and coronary occlusion. This paper also presents an interesting clinical application of graph-based metric estimators, showing their value for solving practical problems found in medical imaging.

Realistic simulation of cardiac magnetic resonance studies modeling anatomical variability, trabeculae, and papillary muscles.

Simulated magnetic resonance imaging brain studies have been generated for over a decade. Despite their useful potential, simulated cardiac studies are only emerging. This article focuses on the realistic simulation of cardiac magnetic resonance imaging datasets. The methodology is based on the XCAT phantom, which is modified to increase realism of the simulated images. Modifications include the modeling of trabeculae and papillary muscles based on clinical measurements and published data. To develop and evaluate our approach, the clinical database included 40 patients for anatomical measurements, 10 patients for papillary muscle modeling, and 10 patients for local gray value statistics. The virtual database consisted of 40 digital voxel phantoms. Histograms from different tissues were obtained from the real datasets and compared with histograms of the simulated datasets with the Chi-square dissimilarity metric (χ(2)) and Kullback-Leibler divergence. For the original phantom, χ(2) values averaged 0.65 ± 0.06 and Kullboek-Leibler values averaged 0.69 ± 0.38. For the modified phantom, χ(2) values averaged 0.34 ± 0.12 and Kullboek-Leibler values averaged 0.32 ± 0.15. The proposed approach demonstrated a noticeable improvement of the local appearance of the simulated images with respect to the ones obtained originally.