Background-Long-term patency of conventional synthetic grafts is unsatisfactory below a 6-mm internal diameter.Poly(⑀-caprolactone) (PCL) is a promising biodegradable polymer with a longer degradation time. We aimed to evaluate in vivo healing and degradation characteristics of small-diameter vascular grafts made of PCL nanofibers compared with expanded polytetrafluoroethylene (ePTFE) grafts. Methods and Results-We prepared 2-mm-internal diameter grafts by electrospinning using PCL (M n ϭ80 000 g/mol).Either PCL (nϭ15) or ePTFE (nϭ15) grafts were implanted into 30 rats. Rats were followed up for 24 weeks. At the conclusion of the follow-up period, patency and structural integrity were evaluated by digital subtraction angiography. The abdominal aorta, including the graft, was harvested and investigated under light microscopy. Endothelial coverage, neointima formation, and transmural cellular ingrowth were measured by computed histomorphometry. All animals survived until the end of follow-up, and all grafts were patent in both groups. Digital subtraction angiography revealed no stenosis in the PCL group but stenotic lesions in 1 graft at 18 weeks (40%) and in another graft at 24 weeks (50%) in the ePTFE group. None of the grafts showed aneurysmal dilatation. Endothelial coverage was significantly better in the PCL group. Neointimal formation was comparable between the 2 groups. Macrophage and fibroblast ingrowth with extracellular matrix formation and neoangiogenesis were better in the PCL group. After 12 weeks, foci of chondroid metaplasia located in the neointima of PCL grafts were observed in all samples. Conclusions-Small-diameter PCL grafts represent a promising alternative for the future because of their better healing characteristics compared with ePTFE grafts. Faster endothelialization and extracellular matrix formation, accompanied by degradation of graft fibers, seem to be the major advantages. Further evaluation of degradation and graft healing characteristics may potentially lead to the clinical use of such grafts for revascularization procedures.

While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks. A remaining drawback of deep learning approaches is their requirement for an expensive retraining whenever the specific problem, the noise level, noise type, or desired measure of fidelity changes. On the contrary, variational methods have a plug-and-play nature as they usually consist of separate data fidelity and regularization terms.In this paper we study the possibility of replacing the proximal operator of the regularization used in many convex energy minimization algorithms by a denoising neural network. The latter therefore serves as an implicit natural image prior, while the data term can still be chosen independently. Using a fixed denoising neural network in exemplary problems of image deconvolution with different blur kernels and image demosaicking, we obtain state-of-the-art reconstruction results. These indicate the high generalizability of our approach and a reduction of the need for problemspecific training. Additionally, we discuss novel results on the analysis of possible optimization algorithms to incorporate the network into, as well as the choices of algorithm parameters and their relation to the noise level the neural network is trained on.

Abstract-The goal of pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchromatic image to obtain an image with high spectral and spatial resolution. The Intensity-Hue-Saturation (IHS) method is a popular pan-sharpening method used for its efficiency and high spatial resolution. However, the final image produced experiences spectral distortion. In this letter, we introduce two new modifications to improve the spectral quality of the image. First, we propose imageadaptive coefficients for IHS to obtain more accurate spectral resolution. Second, an edge-adaptive IHS method was proposed to enforce spectral fidelity away from the edges. Experimental results show that these two modifications improve spectral resolution compared to the original IHS and we propose an adaptive IHS that incorporates these two techniques. The adaptive IHS method produces images with higher spectral resolution while maintaining the high-quality spatial resolution of the original IHS.

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