![]() In Magnetic Resonance Imaging (MRI), the information about the image is collected in the form of Using the single acquisition method and visual aiding tool, SNR comparison of images from two different reconstruction methods can be easily evaluated. This mechanism is supported by a visual aiding tool to carefully select signal and noise regions and avoid the errors introduced in blind selection of ROI. It has been observed that, by careful selection of noise region, the drastic changes in values of SNR can be avoided with single acquisition techniques. The single image method does not produce consistent results as the SNR values change drastically based on the background noise. This paper focuses on the implementation of an image quality assessment mechanism using the single image method by specifying Region of Interest (ROI) from object and background as suggested by National Electrical Manufacturers Association (NEMA). The main goal of the proposed work is to provide research groups and MR algorithm developers with protocols and tools necessary to conveniently assess the quality of MR images and to be able to compare the reconstruction methods among each other. The implementation of the pipeline requires evaluation of large number of filters and reconstruction algorithms, which demands a simple and effective mechanism to assess the quality of output at each stage. The MR reconstruction pipeline includes the algorithms for reconstruction, quality enhancement, artifact reduction, and so on. The raw data acquired from MR scanner is processed through a series of components in the MR reconstruction pipeline to generate an image. Image quality assessment becomes critical for measuring the performance of Magnetic Resonance (MR) image reconstruction algorithms. There is no prior detection approach based on compressed imaging to enhance the performance of a FOD radar, as we present in this study. Nevertheless, we highlight some previously unrealized benefits of the FOD radar system application. It validates the improved efficiency in at least two conditions, a small interval between multiple targets and strong scattering coverage of small targets. Furthermore, we demonstrate the concept of the MMW radar detection approach based on compressed imaging with actual data. The long stripes scattering phenomenon can be reduced, which is beneficial to further detection processing. The compressed imaging is introduced to retrieve the image scene with higher resolution, and a small detectable object, such as 2cm diameter metal ball that equals -35dBsm RCS. The compressed sensing (CS) approach used for detection achieves efficiency by suppressing sidelobes and preventing small targets from being submerged by larger targets. The conventional matched filter process along the range would encounter frequency leakage when strong scattering targets generate high sidelobe and mutual shield interference to object detection. Small target detection is a puzzling challenge that requires a breakthrough for practical application. Scan-mode millimeter-wave radar with a fixed installation position is widely used as a leading solution. We also discuss the relationship between the spatial resolution and pixel size and correct some misconceptions on these issues.Foreign object debris (FOD) radar systems are often used to detect foreign materials that appear on the pavement that can pose as a threat to aircraft and personnel. Different filter functions have been investigated. The methods are intuitive without any assumption and approximation. We develop a 4-step procedure progressively prove the spatially asymptotic independence of pixel intensities and introduce a delta-train method to derive the spatial resolution in the image, for both the basic and the filtered FT imaging. Our study reported in this paper shows that the truncation and apodization introduce/change the spatial correlation and the spatial resolution of pixel intensities in FT imaging. ![]() Although by properly choosing the filter functions, the apodization approach is effective in removing the overshoot and ringing, it is at the price of both spatial correlation and spatial resolution of pixel intensities in the reconstructed image. A common practice is to multiply k-space data with a filter function prior to FT reconstruction for softening the amount of ringing and accepting the concomitant blur. Gibbs ringing is an inevitable artifact in MR Fourier Transform (FT) imaging caused by truncating k-space data via a rectangular window.
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