High-resolution Imaging Research Articles

Retinal fundus image super-resolution based on generative adversarial network guided with vascular structure prior

Many ophthalmic and systemic diseases can be screened by analyzing retinal fundus images. The clarity and resolution of retinal fundus images directly determine the effectiveness of clinical diagnosis. Deep learning methods based on generative adversarial networks are used in various research fields due to their powerful generative capabilities, especially image super-resolution. Although Real-ESRGAN is a recently proposed method that excels in processing real-world degraded images, it suffers from structural distortions when super-resolving retinal fundus images are rich in structural information. To address this shortcoming, we first process the input image using a pre-trained U-Net model to obtain a structural segmentation map of the retinal vessels and use the segmentation map as the structural prior. The spatial feature transform layer is then used to better integrate the structural prior into the generation process of the generator. In addition, we introduce channel and spatial attention modules into the skip connections of the discriminator to emphasize meaningful features and accordingly enhance the discriminative power of the discriminator. Based on the original loss functions, we introduce the L1 loss function to measure the pixel-level differences between the segmentation maps of retinal vascular structures in the high-resolution images and the super-resolution images to further constrain the super-resolution images. Simulation results on retinal image datasets show that our improved algorithm results have a better visual performance by suppressing structural distortions in the super-resolution images.

An Improved Hyper Spectral Imaging for Accurate Disease Diagnosis in Sustainable Medical Environments

Hyper spectral imaging (HSI) has emerged as a powerful technique for accurate disease diagnosis in medical environments. It provides high-resolution images with detailed spectral information, making it possible to identify subtle differences between healthy and diseased tissues. However, current HSI systems face challenges in terms of accuracy and efficiency, limiting their widespread application in sustainable medical environments. To overcome these challenges, our team has developed an improved HSI system that utilizes state-of-the-art spectral imaging technology and machine learning algorithms. This system is capable of capturing and analyzing a wider range of spectral data, enabling more precise identification of disease-specific spectral signatures. Furthermore, our system has been optimized to operate with minimal power consumption, making it environmentally friendly and suitable for sustainable medical environments. The improved HSI system has been successfully tested in clinical settings and has shown promising results in accurately diagnosing various diseases such as cancer, dermatitis, and cardiovascular conditions. Its high accuracy and fast processing time make it a valuable tool for early disease detection and treatment planning. Moreover, the ability to operate with low energy consumption makes it a sustainable solution for medical facilities in resourcelimited areas. In addition to its accuracy and efficiency, our improved HSI system is also user-friendly and can be easily integrated into existing medical imaging systems.

Urban surface-emitted longwave radiation estimation from high spatial resolution thermal infrared images using a hybrid method

Accurate estimation of the surface-emitted longwave radiation (SELR) has important scientific value in understanding its spatiotemporal dynamics and surface thermal environment. Thermal infrared (TIR) images with high spatial resolution offer enhanced data support for studying SELR of complex surfaces, such as urban surface. This study proposes a new urban-oriented hybrid (UoHy) method, which considers multiple scattering and adjacent effects of urban pixel using the term sky view factor, to estimate urban SELR from the top-of-atmosphere radiance of TIR images with high spatial resolution, and performs sensitivity analysis and application. The experimental results for the thermal images of GF-5/VIMS as an example showed that the UoHy method has relatively high accuracy, and obtains SELR errors of less than 12.0 W/m2 under low atmospheric water vapor conditions and less than 17.0 W/m2 under high atmospheric water vapor conditions. The application of the method during daytime and nighttime also demonstrated the method's validity and effectiveness. Compared with the natural surface-oriented hybrid method, the UoHy method obtains an improvement of about 7.0–10.0 W/m2 in SELR estimation, which indicates that it has the potential for practical SELR estimation over urban surface with TIR images of high spatial resolution.

Influence of urban functional zone change on land surface temperature using multi-source geospatial data: A case study in Nanjing City, China

The urban heat island effect is a prevalent urban concern, and studies on its influencing factors are essential to mitigate its negative effects and promote sustainable urban development. Although many studies have revealed the impact of land cover change on land surface temperature (LST) during urbanization, most have focused only on built-up areas without considering the relationship between urban internal functional structure and LST. To clarify the influence of changes in urban functional zones (UFZs) on LST, focusing on the downtown area of Nanjing City, China, this study proposed an effective framework using multi-source geospatial data for qualitative and quantitative analysis. Specifically, very high spatial resolution images and point of interest data were jointly used to generate UFZ maps using an advanced deep learning approach, and LST distribution maps were retrieved from Landsat 8 images using the radiative transfer equation. The UFZ and LST maps were then overlaid and analyzed to reveal the impact of UFZ change on LST. The results showed that in 2013, 2018, and 2022, the residential region constituted the largest proportion of area, comprising 23.52%, 24.52%, and 27.28%, respectively. The areas of unused region and transport region experienced the most rapid decrease and increase of 6.08% and 4.05% from 2013 to 2022, respectively. Furthermore, the area of sub-high temperature zone decreased the most, by 3.47%, whereas that of the high-temperature zone increased the most, by 6.05%. In the study area, water and green space maintained relatively low LSTs, whereas residential region and unused region exhibited higher LSTs. From 2013 to 2022, UFZ changes contributed to a 0.71°C increase in LST. Transfers from green space, water, commercial region, transport region, and industrial region mostly resulted in a higher LST, which was associated with a decrease in fractional vegetation cover (FVC). Transfers from unused region, residential region, and public service region mostly contributed to reducing LST, which was caused by the increase in FVC. The findings and the proposed framework can provide reliable references for urban managers to optimize urban spatial layout and promote sustainable urban development.

On the Origin of a Broad Quasiperiodic Fast-propagating Wave Train: Unwinding Jet as the Driver

Large-scale extreme-ultraviolet waves commonly exhibit as single wave front and are believed to be caused by coronal mass ejections. Utilizing high spatiotemporal resolution imaging observations from the Solar Dynamics Observatory, we present two sequentially generated wave trains originating from the same active region: a narrow quasiperiodic fast-propagating (QFP) wave train that propagates along the coronal loop system above the jet and a broad QFP wave train that travels along the solar surface beneath the jet. The measurements indicate that the narrow QFP wave train and the accompanying flare’s quasiperiodic pulsations (QPPs) have nearly identical onsets and periods. This result suggests that the accompanying flare process excites the observed narrow QFP wave train. However, the broad QFP wave train starts approximately 2 minutes before the QPPs of the flare, but it is consistent with the interaction between the unwinding jet and the solar surface. Moreover, we find that the period of the broad QFP wave train, approximately 130 s, closely matches that of the unwinding jet. This period is significantly longer than the 30 s period of the accompanying flare’s QPPs. Based on these findings, we propose that the intermittent energy release of the accompanying flare excited the narrow QFP wave train confined propagating in the coronal loop system. The unwinding jet, rather than the intermittent energy release in the accompanying flare, triggered the broad QFP wave train propagating along the solar surface.

Long-term trends in total administered radiation dose from brain [18F]FDG-PET in children with drug-resistant epilepsy.

To assess the trends in administered 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) doses, computed tomography (CT) radiation doses, and image quality over the last 15years in children with drug-resistant epilepsy (DRE) undergoing hybrid positron emission tomography (PET) brain scans. We retrospectively analyzed data from children with DRE who had [18F]FDG-PET/CT or magnetic resonance scans for presurgical evaluation between 2005 and 2021. We evaluated changes in injected [18F]FDG doses, administered activity per body weight, CT dose index volume (CTDIvol), and dose length product (DLP). PET image quality was assessed visually by four trained raters. Conversely, CT image quality was measured using region-of-interest analysis, normalized by signal-to-noise (SNR) and contrast-to-noise ratio (CNR). We included 55 children (30 male, mean age: 9 ± 6years) who underwent 61 [18F]FDG-PET scans (71% as PET/CT). Annually, the injected [18F]FDG dose decreased by ~ 1% (95% CI: 0.92%-0.98%, p < 0.001), with no significant changes in administered activity per body weight (p = 0.51). CTDIvol and DLP decreased annually by 16% (95% CI: 9%-23%) and 15% (95% CI: 8%-21%, both p < 0.001), respectively. PET image quality improved by 9% year-over-year (95% CI: 6%-13%, p < 0.001), while CT-associated SNR and CNR decreased annually by 7% (95% CI: 3%-11%, p = 0.001) and 6% (95% CI: 2%-10%, p = 0.008), respectively. Our findings indicate stability in [18F]FDG administered activity per body weight alongside improvements in PET image quality. Conversely, CT-associated radiation doses reduced. These results reaffirm [18F]FDG-PET as an increasingly safer and higher-resolution auxiliary imaging modality for children with DRE. These improvements, driven by technological advancements, may enhance the diagnostic precision and patient outcomes in pediatric epilepsy surgery.

Role of thermospheric winds on the generation of F-region irregularities over Indonesian sector during the solar minimum year 2020

Analysis of ionosonde observations over Chumpon (10.7°N, 99.4°E, 3.3° Mag. Lat.) and Kototabang (0.2° S, 100.3° E, 10° S Mag. Lat.) during the low solar activity year 2020 reveals that the occurrence of post-sunset spread-F maximises during winter (November-February), whereas that of post-midnight spread-F maximises during summer (May-August). The thermospheric winds observed by the Michelson Interferometer for Global High-Resolution Thermospheric Imaging (MIGHTI) on board the Ionospheric Connection Explorer (ICON) satellite over the Indonesian longitudes (95°E-115°E) are investigated to determine their impact on the generation of spread-F. The thermospheric winds at equatorial and low latitudes (10°N) are strongly eastward (> 50 m/s) around post-sunset hours (∼19:00 LST) in winter and equinox. The strong eastward wind is present only around midnight hours (after 22:00 LST) in summer. However, the present study reveals that the zonal wind does not contribute to the generation of plasma bubbles. Besides, the ICON-MIGHTI meridional wind at low latitude (10°N) is observed to be equatorward throughout the day during summer. Compared to the post-sunset hours, the intensity of trans-equatorial wind is stronger on the windward side (60-70 m/s) than on the leeward side (10-20 m/s) during midnight hours. The component of the equatorward meridional wind along the magnetic field lines can lift the F-layer to higher heights leading to a net decrease in the field line-integrated Pedersen conductivity thereby increasing the growth rate of the Rayleigh Taylor instability that can result in the generation of post-midnight spread F.

Enhancing the strength-ductility synergy of dual-phase Al0.3CoCrFeNiTi0.3 high-entropy alloys through the regulation of B2 phase content

In this study, Al0.3CoCrFeNiTi0.3 high-entropy alloys (HEAs) with different phase contents and microstructures were prepared through cold rolling and recrystallization annealing. This method achieved a synergistic combination of strength and ductility, with the CR1000-1 HEA attaining an ultimate tensile strength of 1175 MPa and a uniform elongation of 21.5%. In Al0.3CoCrFeNiTi0.3, there exist two distinct phase morphologies: dispersed structure and clustered structure. Using in-situ high-resolution digital image correlation at the microscale, it was observed that local strain in the dispersed structure tends to concentrate along the phase boundaries when a small strain is applied. As the applied strain increases, strain rapidly concentrates in the FCC phase and subsequently in the B2 phase. For the clustered structure, the strain initially concentrates rapidly within the B2 phase and then gradually diffuses into the FCC phase. The low degree of inhomogeneity of the dispersed structure reduces the likelihood of stress concentration and the risk of damage initiation. Furthermore, we observed the synergistic strain hardening effect produced by various deformation mechanisms, such as dislocation pile-ups, dislocation networks, interacting stacking faults, and deformation twins. This study provides valuable insights for the fabrication of dual-phase HEAs with enhanced strength-ductility synergy, applicable to a wide range of engineering applications.

Design of a polarization-insensitive broadband achromatic metalens for mid-wave infrared detector.

Metalenses, boasting outstanding focusing efficiency and high-resolution imaging capabilities, have generated widespread usage in fields such as integrated optics, achromatic imaging, and optical holography. In this study, we have developed a broadband achromatic metalens within the detection range from 3 to 5 µm, and it has a numerical aperture (NA) of 0.71 with a remarkable maximum focusing efficiency of 63.8% at the focal plane within the specified bandwidth. We have further delved into the dispersion control mechanism that combines the geometric and transmission phases and optimized the constructed phase response simulation database using the particle swarm optimization (PSO) algorithm, ensuring a precise phase matching between the actual wavefront and the ideal focusing wavefront. This metalens with its ability to expand the array size has the potential to create a compact infrared imager, which holds significant importance in achieving efficient detection and integration within infrared detectors.

Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography.

Organoids, which are miniature in vitro versions of organs, possess significant potential for studying human diseases and elucidating their underlying mechanisms. Live imaging techniques play a crucial role in organoid research and contribute to elucidating the complex structure and dynamic biological phenomena of organoids. However, live, unlabeled high-resolution imaging of native organoids is challenging, primarily owing to the complexities of sample handling and optical scattering inherent in three-dimensional (3D) structures. Additionally, conventional imaging methods fail to capture the real-time dynamic processes of growing organoids. In this study, we introduce low-coherence holotomography as an advanced, label-free, quantitative imaging modality designed to overcome several technical obstacles for long-term live imaging of 3D organoids. We demonstrate the efficacy of low-coherence holotomography by capturing high-resolution morphological details and dynamic activities within mouse small intestinal organoids at subcellular resolution. Moreover, our approach facilitates the distinction between viable and nonviable organoids, significantly enhancing its utility in organoid-based research. This advancement underscores the critical role of live imaging in organoid studies, offering a more comprehensive understanding of these complex systems.

KMnO4/Pb staining allows uranium free imaging of tissue architectures in low vacuum scanning electron microscopy

Scanning electron microscopy under low-vacuum conditions allows high-resolution imaging of complex cell/tissue architectures in nonconductive specimens. However, the conventional methods for metal staining of biological specimens require harmful uranium compounds, which hampers the applications of electron microscopy. Here, we introduce a uranium-free KMnO4/Pb metal staining protocol that allows multiscale imaging of extensive cell/tissue architectures to intensive subcellular ultrastructures. The obtained image contrast was equivalent to that of Ur/Pb staining and sufficient for ultrastructural observation, showing the fine processes of podocytes in the glomerulus, which were invisible by light microscopy. The stainability in the elastic tissue indicated that the distinct histochemical properties of KMnO4 oxidation led to Pb deposition and BSE signal enhancement superior to Ur staining. Elemental analysis clarified that the determinant of the backscattered electron signal intensity was the amount of Pb deposition enhanced by KMnO4 oxidation. This user-friendly method is anticipated to create a new approach for biomedical electron microscopy.

Developing flaw sizing methodology in Total Focusing Method (TFM) by EDM calibration blocks

The Total Focusing Method (TFM) represents a significant advancement in ultrasonic inspection, delivering high-resolution imaging by leveraging phased array technology combined with sophisticated data processing algorithms. This synergy enables detailed visualization of flaws in various materials, thereby improving flaw detection and characterization. Despite TFM's capabilities, the lack of a standardized methodology for flaw sizing limits its potential for flaw evaluation. This paper seeks to establish a new paradigm in flaw sizing by introducing a custom methodology specifically designed for TFM, using electrical discharge machined (EDM) calibration blocks that reflect a range of flaw shapes. The research highlights the limitations of conventional side-drilled holes (SDH) for capturing realistic flaw nuances and emphasizes the superior ability of EDM notches to simulate the complex geometries inherent in typical flaws. By investigating the influence of different TFM modes, the study provides insight into their effectiveness in improving the accuracy of flaw characterization. Our approach addresses the challenges of existing TFM practices, with EDM notches serving as an essential tool in methodological advancement. This work contributes to the continued development of best practices in TFM application, paving the way for more accurate, reliable, and versatile nondestructive testing.

Rapid full-color serial sectioning tomography with speckle illumination and ultraviolet excitation

Rapid full-color serial sectioning tomography with speckle illumination and ultraviolet excitation

Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.

Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson's r; analysis of covariance; Bland-Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90min to 20s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/Rwithout/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

Rectifying Discrepancies Between GB-SAR Images and Terrain Model Caused by Measurement Deviations in Open-Pit Mines

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720[Formula: see text]m (@700[Formula: see text]m average monitoring range) when the average density of the terrain point cloud is 0.5[Formula: see text]m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications.

Wavefront shaping and imaging through a multimode hollow-core fiber

Multimode fibers recently emerged as compact minimally-invasive probes for high-resolution deep-tissue imaging. However, the commonly used silica fibers have a relatively low numerical aperture (NA) limiting the spatial resolution of a probe. On top of that, light propagation within the solid core generates auto-fluorescence and Raman background, which interferes with imaging. Here we propose to use a hollow-core fiber to solve these problems. We experimentally demonstrate spatial wavefront shaping at the multimode hollow-core fiber output with tunable high-NA. We demonstrate raster-scan and speckle-based compressive imaging through a multimode hollow-core fiber.

Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications.

Customizing subwavelength stochastic light fields via structured spatial coherence in a high-NA resonator

Interaction of light in the high-numerical aperture (high-NA) systems is crucial for theoretical advances and applications such as superresolution imaging and optical nanofabrication. High coherence is demanded at this scale for intensity and spin profile sculpturing, since the underlying physics being wave interference. Here we report that, even for low-coherence light, 3D light features in a nanometer range can be generated by employing structured coherence states of the light beam in a high-NA resonator system. The generated structures, e.g., 3D helix intensity and transverse spin texture, can survive in rather incoherent optical fields at nanoscale. We also found that, counterintuitively, despite the substantial decrease in spatial coherence of the light field, the longitudinal electric field component and transverse spin density are instead enhanced. The applications of the observed twisted spectral density and spin structures may range from high-resolution imaging to metrology.

In situ imaging of a kleptoplastidic ciliate thin layer indicates traditional sampling underestimates oceanic mixotroph biomass

Planktonic mixotrophs, including ciliates, are abundant, yet understudied, mediators of primary and secondary production in aquatic food webs. Here, we report high-resolution in situ observations of a thin layer—a vertically limited patch of high organism abundance—of the ciliate, Laboea strobila. Within the thin layer, peak L. strobila abundances were as high as 62,000 cells/L, six times higher than previously reported, and were strongly positively correlated with co-measured chlorophyll a concentrations, indicating that the organism indulged in kleptoplasty, retaining plastids from ingested algae. Estimates indicated L. strobila contributed to ~63–78% of chlorophyll a within the layer, in sharp contrast to conventional expectations that phytoplankton dominate this signature, suggesting that mixotroph biomass, and consequently, their contribution to marine ecosystems is widely underestimated. These observations also highlight the importance of pairing traditional approaches such as chlorophyll a measurements with high-resolution imaging for proper attribution of biomass proxies.

Iterative Mamba Diffusion Change-Detection Model for Remote Sensing

In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing the quality of images shot over various geographical areas, particularly for high-resolution images. However, there are some shortcomings of the widely used Convolutional Neural Networks (CNNs) and Transformers-based CD methods. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Additionally, the commonly used information-fusion methods for pre- and post-change images often lead to information loss or redundancy, resulting in inaccurate edge detection. To address these issues, we propose an Iterative Mamba Diffusion Change Detection (IMDCD) approach to iteratively integrate various pieces of information and efficiently produce fine-grained CD maps. Specifically, the Swin-Mamba-Encoder (SME) within Mamba-CD (MCD) is employed as a semantic feature extractor, capable of modeling long-range relationships with linear computability. Moreover, we introduce the Variable State Space CD (VSS-CD) module, which extracts abundant CD features by training the matrix parameters within the designed State Space Change Detection (SS-CD). The computed high-dimensional CD feature is integrated into the noise predictor using a novel Global Hybrid Attention Transformer (GHAT) while low-dimensional CD features are utilized to calibrate prior CD results at each iterative step, progressively refining the generated outcomes. IMDCD exhibits a high performance across multiple datasets such as the CDD, WHU, LEVIR, and OSCD, marking a significant advancement in the methodologies within the CD field of RS. The code for this work is available on GitHub.