Computational Imaging-Processing Comparison for Lumbar Spine Cadaveric Specimens with Clinical Medical Physics Applications

A series of improved imaging-computational and algorithmic methods for new/different lumbar cadaveric specimens was obtained. These are based on previous publications [3,3.1], with an improved-imaging research line. Results show a systematic study of each lumbar cadaveric specimen. Enhanced imaging findings and resolution for vertebral facets/positioning, contrast, anatomical parts separation and visualization of lumbar spines are demonstrated. Medical Physics and clinical bioengineering advances related to previous contributions are proven with imaging processing, programming codes/patterns, and computer vision tools. Findings constitute computational imaging methods which are appropriate for sharp and detailed anatomical-clinical analysis and comparisons among cadaveric specimens. These processing solutions are useful for lumbar spine computational study and anatomical dissection. Applications on Medical Physics, Biomedical Engineering, and Computational-Forensic Diagnosis are obtained from this cadaveric imaging systematic comparison and software methods.

Frequency-Diverse Holographic Metasurface Antenna for Near-Field Microwave Computational Imaging

This article presents a holographic metasurface antenna with stochastically distributed surface impedance, which produces randomly frequency-diverse radiation patterns. Low mutual coherence electric field patterns generated by the holographic metasurface antenna can cover the K-band from 18 to 26 GHz with 0.1 GHz intervals. By utilizing the frequency-diverse holographic metasurface (FDHM) antenna, we build a near-field microwave computational imaging system based on reflected signals in the frequency domain. A standard horn antenna is adopted to acquire frequency domain signals radiated from the proposed FDHM antenna. A detail imaging restoration process is presented, and the desired targets are correctly reconstructed using the 81 frequency-diverse patterns through full-wave simulation studies. Compressed sensing technique and iterative shrinkage/thresholding algorithms are applied for the imaging reconstruction. The achieved compressive ratio of this computational imaging system on the physical layer is 30:1.