Application of Image Reconstruction Based on Inverse Radon Transform in CT System Parameter Calibration and Imaging

According to the requirements of CT system parameter calibration and imaging, using OpenCV and MATLAB software, the reverse Radon transform and the Canny edge detection algorithm in the projection edge methods can determine the position and geometry of two different media in the square tray based on given data. Besides, it can apply and analyze the shape and the absorption rate of the specified point, the accuracy, and stability of the template calibration parameters by this way, which enables designing a new template and calibrating the new parameters.

Experimental Comparison of Radon Domain Approaches for Resident Space Object’s Parameter Estimation

The fast and uncontrolled rise of the space objects population is threatening the safety of space assets. At the moment, space awareness solutions are among the most calling research topic. In fact, it is vital to persistently observe and characterize resident space objects. Instrumental highlights for their characterization are doubtlessly their size and rotational period. The Inverse Radon Transform (IRT) has been demonstrated to be an effective method for this task. The analysis presented in this paper has the aim to compare various approaches relying on IRT for the estimation of the object’s rotation period. Specifically, the comparison is made on the basis of simulated and experimental data.

Reference Breast Phantoms for Low-Cost Microwave Imaging

Microwave imaging provides an alternative method for breast cancer screening and the diagnosis of cerebrovascular accidents. Before a surgical operation, the performance of microwave imaging systems should be evaluated on anatomically detailed anthropomorphic phantoms. This paper puts forward the advances in the development of breast phantoms based on 3D printing structures filled with liquid solutions that mimic biological tissues in terms of complex permittivity in a wide microwave frequency band. In this paper; four different experimental scenarios were created, and measurements were performed, and although there are many vector network analyzers on the market, the miniVNA used in this study has been shown to have potential in many biomedical applications such as portable computer-based breast cancer detection studies. We especially investigated the reproducibility of a particular mixture and the ability of some mixes to mimic various breast tissues. Afterwards, the images similar to the experimentally created scenarios were obtained by implementing the inverse radon transform to the obtained data.

3D infrared thermospectroscopic imaging

This work reports a multispectral tomography technique in transmission mode (called 3DITI for 3D Infrared Thermospectroscopic Imaging) based on a middle wavelength infrared (MWIR) focal plane array. This technique relies on an MWIR camera (1.5 to 5.5 μm) used in combination with a multispectral IR monochromator (400 nm to 20 μm), and a sample mounted on a rotary stage for the measurement of its transmittance at several angular positions. Based on the projections expressed in terms of a sinogram, spatial three-dimensional (3D) cubes (proper emission and absorptivity) are reconstructed using a back-projection method based on inverse Radon transform. As a validation case, IR absorptivity tomography of a reflective metallic screw is performed within a very short time, i.e., shorter than 1 min, to monitor 72 angular positions of the sample. Then, the absorptivity and proper emission tomographies of a butane-propane-air burner flame and microfluidic perfluoroalkoxy (PFA) tubing filled with water and ethanol are obtained. These unique data evidence that 3D thermo-chemical information in complex semi-transparent media can be obtained using the proposed 3DITI method. Moreover, this measurement technique presents new problems in the acquisition, storage and processing of big data. In fact, the quantity of reconstructed data can reach several TB (a tomographic sample cube of 1.5 × 1.5 × 3 cm3 is composed of more than 1 million pixels per wavelength).