De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR de-noising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.     

Exploring the Potential of Wire Fed Direct Energy Deposition of Aluminum–Boron Nitride Nanotube Composite: Microstructural Evolution and Mechanical Properties

Recent advancements have shown great promise in utilizing wire-fed direct energy deposition (DED) for building aluminum alloy structures. However, utilizing the wire-fed DED approach for fabricating metal matrix composite structures remains a significant challenge. Herein, a wire-based additive manufacturing process is used to successfully produce a 1D boron nitride nanotube (BNNT)-reinforced aluminum composite with high strength. Al-BNNT electrode is developed in house. The microstructural changes that occur during layer-by-layer deposition are investigated. The grain morphology changes from equiaxed grains in the bottom layer to columnar grains in the top layer. BNNTs act as nuclei to promote the formation of equiaxed grains and interfacial compounds (AlN and AlB₂) during solidification. This results in improved strength, with Al-BNNT composite exhibiting a tensile strength of 47 MPa, 2.3 times higher than its pure Al. Higher strength is attributed to the retention and uniform distribution of BNNT reinforcement in the melt pool, leading to effective load transfer. This study demonstrates the potential of additive manufacturing for producing high-performance metal matrix composites with novel 1D reinforcements and improved multifunctional properties.