MV-GNN: Generation of continuous geometric representations of mitral valve motion from 3D+t echocardiography.

We present a geometric deep-learning method for reconstructing a temporally continuous mitral valve surface mesh from 3D transesophageal echocardiography sequences. Our approach features a supervised end-to-end deep learning architecture that combines a convolutional neural network-based voxel encoder and decoder with a graph neural network-based multi-resolution mesh decoder, all trained on sparse landmark annotations. Key elements of our methodology include a tube-shaped prototype mesh with labeled vertices, a specialized loss function to preserve the known inlet and outlet, and a rigid alignment system for anatomical landmarks.

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.

Background: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies.

CdSe/ZnS quantum dots as a booster in the active layer of distributed ternary organic photovoltaics.

Organic solar cells are a promising candidate for practical use because of their low material cost and simple production procedures. The challenge is selecting materials with the right properties and how they interrelate in the context of manufacturing the device. This paper presents studies on CdSe/ZnS nanodots as dopants in a polymer-fullerene matrix for application in organic solar cells.

A simulation-based phantom model for generating synthetic mitral valve image data-application to MRI acquisition planning.

Purpose: Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV).

RGO nanosheet wrapped β-phase NiCuS nanorods for advanced supercapacitor applications.

A new integration strategy of transition metal sulfide with carbon-based materials is used to boost its catalytic property and electrochemical performances in supercapacitor application. Herein, crystalline reduced graphene oxide (rGO) wrapped ternary metal sulfide nanorod composites with different rGO ratios are synthesized using hydrothermal technique and are compared for their physical, chemical, and electrochemical performances. It is found that their properties are tuned by the weight ratios of rGO.