Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel automated framework that improves the standard U-Net based methods on these CMR multi-channel data (magnitude and phase) by cross-channel fusion with attention module and shape information based post-processing to achieve accurate delineation of both epicardium and endocardium contours. To evaluate the results, we employ the widely used Dice scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows enhanced performance compared to standard UNet based networks trained with single-channel data. Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.
Over the last couple of decades, there has been an intense research on strain balanced semiconductor quantum wells (QW) to increase the efficiency of multi-junction solar (MJ) solar cells grown monolithically on germanium. So far, the most successful application of QWs have required just to tailor a few tens of nanometers the absorption edge of a given subcell in order to reach the optimum spectral position. However, the demand for higher efficiency devices requiring 3, 4 or more junctions, represents a major difference in the challenges QWs must face: tailoring the absorption edge of a host material is not enough, but a complete new device, absorbing light in a different spectral region, must be designed. Among the most important issues to solve is the need for an optically thick structure to absorb enough light while keeping excellent carrier extraction using highly strained materials. Improvement of the growth techniques, smarter device designs - involving superlattices and shifted QWs, for example - or the use of quantum wires rather than QWs, have proven to be very effective steps towards high efficient MJ solar cells based on nanostructures in the last couple of years. But more is to be done to reach the target performances. This work discusses all these challenges, the limitations they represent and the different approaches that are being used to overcome them.
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