Seung Wook Lee

Distinguishing differences between authentic artifacts and replicas is a significant challenge in the field of cultural heritage. In this study, we explore the application of neutron grating interferometry and tomography techniques to identify Korean copper coins in the nineteenth century of Joseon period by investigating structural differences between genuine objects and replicas. Neutron grating interferometry provides the microstructural information of coins, including features such as pores and precipitates, through a dark field image derived from small-angle neutron scattering. Additionally, neutron transmission tomography examines the three-dimensional internal structures and potentially hidden features of coins. Both neutron imaging techniques highlight regions that contain lead precipitates in the copper alloy, showing consistent agreement with optical imaging and with the quantitative lead content measured by energy dispersive X-ray spectroscopy. The distinct corrosion patterns observed in the authentic coin and replica provide empirical explanations for the general corrosion mechanism of copper alloy. This interpretation finds support in the moderate contribution of dark field contrast from cuprite, which underlies the signal of lead precipitates.

In this paper, we propose a method for reducing artifacts in X-ray and neutron grating interferometer imaging by optimizing the grating position and dose fluctuation. Grating interferometer can obtain additional information about the object, such as phase-contrast image and dark-field image, compared to traditional X-ray imaging. However, several errors such as inaccurate grating positions and non-uniform X-ray intensity can cause artifacts in the image and degrade image quality. The proposed method can reduce the artifacts by minimizing the value in the sample-free region in the transmission and dark-field images. We conducted experiments on both simulated and X-ray and neutron experimental data and demonstrated the effectiveness of our method.

본 연구는 분석 대상 구간인 조천의 생태시설물에 대한 1차원과 2차원 수리 특성 분석을 실시하고자한다. 이를 위한 하천의 종·횡단면 자료, 단면 조도계수 등과 같은 수리 특성 자료를 위해 2차례의 현장조사가 진행되었으며, 1차원 수치 모의 수행을 위한 흐름 자료 구축 및 기점 수위를 산정하여 경계조건을 설정하였다. 이에 ｢조천 하천기본계획｣에 의한 유량, 조도계수와 1차원 수리 특성 분석 결과로 도출된 기점 수위를 RMA-2모형의 입력자료로 이용하여 전체구간과 시설물 설치구간의 유속을 모의하였다. 마지막으로 산정된 유속을 활용한 SED2D 모의로 대상 하천의 전체구간 및 시설물 설치구간의 하상변화를 확인하고 분석하였다. 그 결과, 하천의 하폭이 유속에 많은 영향을 주며, 상류로 유입되는 부유사농도가 하상 변화에 가장 크게 작용하는 것으로 분석되었다. 이러한 결과는 향후 생태시설물 설치로인한 하상변화와 이에 따른 하천 활용을 위한 기초자료로 활용될 것이다.This study intends to conduct a one-dimensional and two-dimensional hydraulic characteristic analysis of the ecological facilities of Jocheon. For this purpose, two field surveys were conducted on hydraulic characteristic data such as longitudinal and cross-sectional data of rivers and roughness coefficient, and boundary conditions were set by establishing flow data for one-dimensional numerical simulation and calculating the starting point of water level. The water level was derived from the results of one-dimensional hydraulic characteristic analysis according to the ｢Jocheon Basic River Plan｣ as input data for the RMA-2 model. Lastly, the riverbed changes in the entire section of the target river and the facility installation section were confirmed and analyzed through SED-2D simulation using the calculated flow velocity. As a result, it was analyzed that the width of the river has a great influence on the flow rate. In additon to this, the concentration of suspended sediment flowing into the upstream has the most influence on the change of the river bed.

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noise/clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise/noise image pairs for training. We obtained many DPCIs through combination of phase stepping images, and these were used as input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.

We describe an inverse Talbot–Lau neutron grating interferometer that provides an extended autocorrelation length range for quantitative dark-field imaging. To our knowledge, this is the first report of a Talbot–Lau neutron grating interferometer (nTLI) with inverse geometry. We demonstrate a range of autocorrelation lengths (ACL) starting at low tens of nanometers, which is significantly extended compared to the ranges of conventional and symmetric setups. ACLs from a minimum of 44 nm to the maximum of 3.5 μm were presented for the designed wavelength of 4.4 Å in experiments. Additionally, the inverse nTLI has neutron-absorbing gratings with an optically thick gadolinium oxysulfide (Gadox) structure, allowing it to provide a visibility of up to 52% while maintaining a large field of view of approximately 100 mm × 100 mm. We demonstrate the application of our interferometer to quantitative dark-field imaging by using diluted polystyrene particles in an aqueous solution and silicon comb structures. We obtain quantitative structural information of the sphere size and concentration of diluted polystyrene particles and the period, height, and duty cycle of the silicon comb structures. The optically thick Gadox structure of the analyzer grating also provides improved characteristics for the correction of incoherent neutron scattering in an aqueous solution compared to the symmetric nTLI.

Scattering studies of milk and milk products, which are highly relevant food products on the global market, are often utilized and reported in literature to investigate and understand the subtle microscopic structural differences between dairy samples. These structural features determine the physical properties and ultimately the texture of milk products and, thus, also influence the consumer’s experience. Small-angle neutron scattering is a prominent example, which enables observations of length scales, which convey proteins and fat globules in food-grade milk. In addition, deuteration enables contrast variations between the constituents of dairy products. In this study, we investigate the potential of probing small-angle neutron scattering from milk samples through quantitative neutron dark-field imaging using grating interferometry, to establish the feasibility of studying, in particular, fat globules and milk gel structures with this spatially resolved scattering technique.

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noisy-clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise-noise image pairs for training. We obtained many differential phase contrast images through combination of phase stepping images, and these were used as noise input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.