Laser surface treatment was carried out on AISI 416 machinable martensitic stainless steel containing 0.225 wt.% sulfur. Nd:YAG laser with a 2.2-KW continuous wave was used. The aim was to compare the physical and chemical properties achieved by this type of selective surface treatment with those achieved by the conventional treatment. Laser power of different values (700 and 1000 W) with four corresponding different laser scanning speeds (0.5, 1, 2, and 3 m·min−1) was adopted to reach the optimum conditions for impact toughness, wear, and corrosion resistance for laser heat treated (LHT) samples. The 0 °C impact energy of LHT samples indicated higher values compared to the conventionally heat treated (CHT) samples. This was accompanied by the formation of a hard surface layer and a soft interior base metal. Microhardness was studied to determine the variation of hardness values with respect to the depth under the treated surface. The wear resistance at the surface was enhanced considerably. Microstructure examination was characterized using optical and scanning electron microscopes. The corrosion behavior of the LHT samples was also studied and its correlation with the microstructures was determined. The corrosion data was obtained in 3.5% NaCl solution at room temperature by means of a potentiodynamic polarization technique.

The application of He-Ne laser technologies for description of articular cartilage degeneration, one of the most common diseases worldwide, is an innovative usage of these technologies used primarily in material engineering. Plain radiography and magnetic resonance imaging are insufficient to allow the early assessment of the disease. As surface roughness of articular cartilage is an important indicator of articular cartilage degeneration progress, a safe and noncontact technique based on laser speckle image to estimate the surface roughness is provided. This speckle image from the articular cartilage surface, when illuminated by laser beam, gives very important information about the physical properties of the surface. An experimental setup using a low power He-Ne laser and a high-resolution digital camera was implemented to obtain speckle images of ten bovine articular cartilage specimens prepared for different average roughness values. Texture analysis method based on gray-level co-occurrence matrix (GLCM) analyzed on the captured speckle images is used to characterize the surface roughness of the specimens depending on the computation of Haralick’s texture features. In conclusion, this promising method can accurately estimate the surface roughness of articular cartilage even for early signs of degeneration. The method is effective for estimation of average surface roughness values ranging from 0.09 µm to 2.51 µm with an accuracy of 0.03 µm.

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We propose a new optical method based on comparative holographic projection for visual comparison between two abnormal follow-up magnetic resonance (MR) exams of glioblastoma patients to effectively visualize and assess tumor progression. First, the brain tissue and tumor areas are segmented from the MR exams using the fast marching method (FMM). The FMM approach is implemented on a computed pixel weight matrix based on an automated selection of a set of initialized target points. Thereafter, the associated phase holograms are calculated for the segmented structures based on an adaptive iterative Fourier transform algorithm (AIFTA). Within this approach, a spatial multiplexing is applied to reduce the speckle noise. Furthermore, hologram modulation is performed to represent two different reconstruction schemes. In both schemes, all calculated holograms are superimposed into a single two-dimensional (2D) hologram which is then displayed on a reflective phase-only spatial light modulator (SLM) for optical reconstruction. The optical reconstruction of the first scheme displays a 3D map of the tumor allowing to visualize the volume of the tumor after treatment and at the progression. Whereas, the second scheme displays the follow-up exams in a side-by-side mode highlighting tumor areas, so the assessment of each case can be fast achieved. The proposed system can be used as a valuable tool for interpretation and assessment of the tumor progression with respect to the treatment method providing an improvement in diagnosis and treatment planning.

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