Successful Treatment of Severe Paravalvular Leak by Repositioning a Self-Expandable Percutaneous Aortic Valve Bioprosthesis (Evolut PRO+) Using the "Double Snare" Technique.

Significant (moderate or severe) paravalvular leak (PVL) after transcatheter aortic valve replacement (TAVR) remains a common phenomenon and has been associated with decrease survival and quality of life. Transcatheter valve embolization and migration (TVEM) is a rare post-TAVR complication that can occur in 1% of cases and has been associated with worse patient outcomes. Valve embolization or migration into the left ventricle can result in significant PVL causing hemodynamic instability, shock, heart failure, and hemolytic anemia. Although this complication most commonly occurs in the acute setting (90%) within 4 hours of TAVR, it can also present late (4 hr-43 days later) in 10% of cases. There are no clear guidelines as to how this condition should be managed; however, several percutaneous bailout techniques exist that can ultimately spare the patient from emergent cardiovascular surgery. We present a rare case of late ventricular transcatheter aortic valve migration 3 days after TAVR causing severe PVL and heart failure symptoms that was successfully treated using the percutaneous "double snare" technique.

Spherical nonlinear correlations for global invariant three-dimensional object recognition.

We define a nonlinear filtering based on correlations on unit spheres to obtain both rotation- and scale-invariant three-dimensional (3D) object detection. Tridimensionality is expressed in terms of range images. The phase Fourier transform (PhFT) of a range image provides information about the orientations of the 3D object surfaces. When the object is sequentially rotated, the amplitudes of the different PhFTs form a unit radius sphere. On the other hand, a scale change is equivalent to a multiplication of the amplitude of the PhFT by a constant factor. The effect of both rotation and scale changes for 3D objects means a change in the intensity of the unit radius sphere. We define a 3D filtering based on nonlinear operations between spherical correlations to achieve both scale- and rotation-invariant 3D object recognition.

Phase Fourier vector model for scale invariant three-dimensional image detection.

A scale invariant 3D object detection method based on phase Fourier transform (PhFT) is addressed. Three-dimensionality is expressed in terms of range images. The PhFT of a range image gives information about the orientations of the surfaces in the 3D object. When the object is scaled, the PhFT becomes a distribution multiplied by a constant factor which is related to the scale factor. Then 3D scale invariant detection can be solved as illumination invariant detection process. Several correlation operations based on vector space representation are applied. Results show the tolerance of detection method to scale besides discrimination against false objects.

Three-dimensional object detection under arbitrary lighting conditions.

A novel method of 3D object recognition independent of lighting conditions is presented. The recognition model is based on a vector space representation using an orthonormal basis generated by the Lambertian reflectance functions obtained with distant light sources. Changing the lighting conditions corresponds to multiplying the elementary images by a constant factor and because of that, all possible lighting views will be elements that belong to that vector space. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated 3D object and that subspace. We define the angle in terms of linear correlations to get shift and illumination-invariant detection.

Nonlinear pattern recognition correlators based on color-encoding single-channel systems.

In color pattern recognition, color channels are normally processed separately and afterward the correlation outputs are combined. This is the definition of multichannel processing. We combine a single-channel method with nonlinear filtering based on nonlinear correlations. These nonlinear correlations yield better discrimination than common matched filtering. The method codes color information as amplitude and phase distributions and is followed by correlations related to binary decompositions. The technique is based on binary decompositions of the red, green, and blue and the hue, saturation, and intensity monochromatic channels of the reference and of the input scene, after which the binary information on the red, green, and blue channels and that of the hue, saturation, and intensity channels are encoded as different angles of a phase distribution. We have applied the method to images degraded by high levels of substitutive noise. Results show that the sliced orthogonal nonlinear generalized correlation detects the target with a high degree of discrimination when other methods fail.