Yeonok Park

Industrial x-ray inspection system with improved image characterization using blind deblurring based on compressed-sensing scheme

ABSTRACT An industrial x-ray inspection system has recently established by our group to examine large and dense objects available in industry. It consists of an industrial x-ray generator having a tube voltage of 450 kV and a focal spot size of 1 mm, a flat-panel detector having a pixel size of 200 µm and a pixel dimension of 2048 × 2048, and a mechanical support for object’s installation. For improving the image characteristics of the system, an effective blind deblurring method based on compressed-sensing scheme is reported. Blind deblurring is the image restoration by estimating the original image and the degradation mechanism using partial information on both. Compressed-sensing is a relatively new mathematical theory for solving the inverse problems. Systematic measurements were performed and the image characteristics of the restored images were quantitatively evaluated using several image-quality indicators. The results demonstrate that the deblurring method is effective for industrial x-ray inspection systems.

Feasibility Study for Improving the Image Characteristics in Digital Tomosynthesis (DTS) Using a Compressed-Sensing (Cs)-Based Pre-Deblurring Scheme

ABSTRACT Digital tomosynthesis (DTS) has been widely used in both industrial nondestructive testing and medical x-ray imaging as a popular multiplanar imaging modality. However, although it provides some of the tomographic benefits of computed tomography (CT) at reduced dose and imaging time, the image characteristics are relatively poor due to blur artifacts originated from incomplete data sampling for a limited angular range and also aspects inherent to imaging system, including finite focal spot size of the x-ray source, detector resolution, etc. In this work, in order to overcome these difficulties, we propose an intuitive method in which a compressed-sensing (CS)-based deblurring scheme is applied to the projection images before common DTS reconstruction. We implemented the proposed deblurring algorithm and performed a systematic experiment to demonstrate its viability for improving the image characteristics in DTS. According to our results, the proposed method appears to be effective for the blurring problems in DTS and seems to be promising to our ongoing application to x-ray nondestructive testing.

3D reconstruction based on compressed-sensing (CS)-based framework by using a dental panoramic detector

In this work, we propose a practical method that can combine the two functionalities of dental panoramic and cone-beam CT (CBCT) features in one by using a single panoramic detector. We implemented a CS-based reconstruction algorithm for the proposed method and performed a systematic simulation to demonstrate its viability for 3D dental X-ray imaging. We successfully reconstructed volumetric images of considerably high accuracy by using a panoramic detector having an active area of 198.4 mm × 6.4 mm and evaluated the reconstruction quality as a function of the pitch (p) and the angle step (Δθ). Our simulation results indicate that the CS-based reconstruction almost completely recovered the phantom structures, as in CBCT, for p≤2.0 and θ≤6°, indicating that it seems very promising for accurate image reconstruction even for large-pitch and few-view data. We expect the proposed method to be applicable to developing a cost-effective, volumetric dental X-ray imaging system.

Investigation of reconstruction quality in digital breast tomosynthesis (DBT) based on compressed-sensing algorithm and synthesized 2D breast image

Digital breast tomosynthesis (DBT) is most commonly used in three-dimensional (3D) mammography because it provides a 3D view, so suspected tumors and massed in the breast can be detected with a higher degree of accuracy. Conventional DBT reconstruction methods are based on the filtered-backprojection (FBP) with an additional deblurring filter. However, this approach usually requires dense projection data with low noise levels for acceptable reconstruction quality. In this work, instead, we investigated a state-of-the-art image reconstruction based on the compressed-sensing (CS) theory for potential application to accurate, low-dose DBT. We implemented a CS-based algorithm as well as a FBP-based algorithm for DBT reconstruction and performed a systematic experiment to verify the usefulness of the algorithm by comparing its reconstruction quality to the FBP-based one. We successfully obtained DBT images of substantially high accuracy by using the CS-based algorithm and synthesized a 2D breast image from the CS-reconstructed DBT images, which showed heightened details retained from DBT images, indicating superior performance compared to traditional 2D breast image alone.

Development of an automated gamma-imaging system for nondestructive testing of welded pipes and its performance evaluation

As a continuation of our radiographic testing (RT) R&D, we have developed an automated gamma-imaging system to be suitable for nondestructive testing of welded pipes. The system consists of an imaging detector having a CdTe/CMOS-based pixel array (AJAT, SCAN1000), a collimated 75Se gamma source (MDS, GammaMat SE) having about 78.7 Ci activities, a gear track mounted around test pipes for image scan, and a computer-controlled microstep motor for precise control of the scan speed. All the components of the imaging system were assembled tightly by a chain belt. From the imaging system, we obtained useful gamma images of a stainless steel pipe (30 cm in internal diameter and 1 cm in thickness) having a welding line. By using standard techniques for evaluation of the radiography quality, we assessed the imaging characteristics of the detector in terms of the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE). We also evaluated the gamma imaging characteristics of the system by using standard wire specimens (ASTM-1B and 1C), typically used in the field of industrial RT. In this paper, we present a detailed description of the automated gamma-imaging system and the experimental results we obtained.