Spatial Resolution Characteristics Research Articles

Classification and extraction method of hidden dangers along railway lines based on semantic segmentation network

ABSTRACT Foreign objects invading high-speed railway lines can cause danger. One existing solution is to use remote sensing images to analyse the dangerous areas along the railway line, thereby providing a certain amount of investigation time. Considering the spatial and temporal resolution characteristics of existing remote sensing technologies in identifying floating objects and the reality of rapid land use changes, this paper identifies areas on the ground where floating objects may be generated by using semantic segmentation techniques oriented to remotely sensed imagery and provides early warnings to staff along the route. However, these regions that need to be analysed have different semantics and scales. To address these challenges, this paper proposes a Dual-branch Parallel Fusion Network (DPFNet) based on Transformer, aimed at enhancing multi-class semantic segmentation in remote sensing images. To leverage global contextual information, we introduce a Swin Transformer-based backbone network, which employs self-attention to capture a comprehensive scene context, facilitating better segmentation by considering the entire scene’s context. For multi-scale semantic features, we propose one approach that involves independent branching feature expression and a Multi-scale Feature Space Fusion Module (MFSFM). The former enriches multi-scale information, while the latter fuses features across different levels to capture diverse semantic features. Experimental results demonstrate that DPFNet can effectively identify the hidden danger area, and the fusion of multi-scale features makes the network more accurately identify and segment the risk area of different sizes, improving the segmentation accuracy and robustness, and is of great significance to the formation of the ‘prevention’ as the core of the railway safety operation.

UAV Quantitative Remote Sensing of Riparian Zone Vegetation for River and Lake Health Assessment: A Review

River and lake health assessment (RLHA) is an important approach to alleviating the conflict between protecting river and lake ecosystems and fostering socioeconomic development, aiming for comprehensive protection, governance, and management. Vegetation, a key component of the riparian zone, supports and maintains river and lake health (RLH) by providing a range of ecological functions. While research on riparian zone vegetation is ongoing, these studies have not yet been synthesized from the perspective of integrating RLHA with the ecological functions of riparian zone vegetation. In this paper, based on the bibliometric method, the relevant literature studies on the topics of RLHA and unmanned aerial vehicle (UAV) remote sensing of vegetation were screened and counted, and the keywords were highlighted, respectively. Based on the connotation of RLH, this paper categorizes the indicators of RLHA into five aspects: water space: the critical area from the river and lake water body to the land in the riparian zone; water resources: the amount of water in the river and lake; water environment: the quality of water in the river and lake; water ecology:aquatic organisms in the river and lake; and water services:the function of ecosystem services in the river and lake. Based on these five aspects, this paper analyzes the key role of riparian zone vegetation in RLHA. In this paper, the key roles of riparian zone vegetation in RLHA are summarized as follows: stabilizing riverbanks, purifying water quality, regulating water temperature, providing food, replenishing groundwater, providing biological habitats, and beautifying human habitats. This paper analyzes the application of riparian zone vegetation ecological functions in RLH, summarizing the correlation between RLHA indicators and these ecological functions. Moreover, this paper analyzes the advantages of UAV remote sensing technology in the quantitative monitoring of riparian zone vegetation. This analysis is based on the high spatial and temporal resolution characteristics of UAV remote sensing technology and focuses on monitoring the ecological functions of riparian zone vegetation. On this basis, this paper summarizes the content and indicators of UAV quantitative remote sensing monitoring of riparian zone vegetation for RLHA. It covers several aspects: delineation of riparian zone extent, identification of vegetation types and distribution, the influence of vegetation on changes in the river floodplain, vegetation cover, plant diversity, and the impact of vegetation distribution on biological habitat. This paper summarizes the monitoring objects involved in monitoring riparian zones, riparian zone vegetation, river floodplains, and biological habitats, and summarizes the monitoring indicators for each category. Finally, this paper analyzes the challenges of UAV quantitative remote sensing for riparian zone vegetation at the current stage, including the limitations of UAV platforms and sensors, and the complexity of UAV remote sensing data information. This paper envisages the future application prospects of UAV quantitative remote sensing for riparian zone vegetation, including the development of hardware and software such as UAV platforms, sensors, and data technologies, as well as the development of integrated air-to-ground monitoring systems and the construction of UAV quantitative remote sensing platforms tailored to actual management applications.

Spatial Feature Enhancement and Attention-Guided Bidirectional Sequential Spectral Feature Extraction for Hyperspectral Image Classification

Hyperspectral images have the characteristics of high spectral resolution and low spatial resolution, which will make the extracted features insufficient and lack detailed information about ground objects, thus affecting the accuracy of classification. The numerous spectral bands of hyperspectral images contain rich spectral features but also bring issues of noise and redundancy. To improve the spatial resolution and fully extract spatial and spectral features, this article proposes an improved feature enhancement and extraction model (IFEE) using spatial feature enhancement and attention-guided bidirectional sequential spectral feature extraction for hyperspectral image classification. The adaptive guided filtering is introduced to highlight details and edge features in hyperspectral images. Then, an image enhancement module composed of two-dimensional convolutional neural networks is used to improve the resolution of the image after adaptive guidance filtering and provide a high-resolution image with key features emphasized for the subsequent feature extraction module. The proposed spectral attention mechanism helps to extract more representative spectral features, emphasizing useful information while suppressing the interference of noise. Experimental results show that our method outperforms other comparative methods even with very few training samples.

High-Dynamic-Range Integrated NV Magnetometers

High-dynamic-range integrated magnetometers demonstrate extensive potential applications in fields involving complex and changing magnetic fields. Among them, Diamond Nitrogen Vacancy Color Core Magnetometer has outstanding performance in wide-range and high-precision magnetic field measurement based on its inherent high spatial resolution, high sensitivity and other characteristics. Therefore, an innovative frequency-tracking scheme is proposed in this study, which continuously monitors the resonant frequency shift of the NV color center induced by a time-varying magnetic field and feeds it back to the microwave source. This scheme successfully expands the dynamic range to 6.4 mT, approximately 34 times the intrinsic dynamic range of the diamond nitrogen-vacancy (NV) center. Additionally, it achieves efficient detection of rapidly changing magnetic field signals at a rate of 0.038 T/s.

Detailed Land Use Classification in a Rare Earth Mining Area Using Hyperspectral Remote Sensing Data for Sustainable Agricultural Development

In China, ion-adsorbing rare earth minerals are mainly located in the southern hilly areas and are important strategic resources. Extensive long-term mining has severely damaged the land cover in mining areas, caused soil pollution and terrain fragmentation, disrupted the balance between mining and agriculture, severely restricted agricultural development, and affected ecological development. Precise and detailed classification of land use within mining areas is crucial for monitoring the sustainable development of agricultural ecology in these areas. In this study, we leverage the high spatial and high spectral resolution characteristics of the Zhuhai-1 (OHS) hyperspectral image datasets. We create four types of datasets based on spectral, vegetation, red edge, and texture characteristics. These datasets are optimized for multifaceted features, considering the complex land use scenario in rare earth mining areas. Additionally, we design seven optimal combination schemes for features. This is performed to examine the impact of different schemes on land use classification in rare earth mining areas and the accuracy of identifying agricultural land classes from broken blocks. The results show that (1) the inclusion of texture features has the most obvious effect on the overall classification accuracy; (2) the red edge feature has the worst effect on improving the overall accuracy of the surface classification; however, it has a prominent effect on the identification of agricultural lands such as farmland, orchards, and reclaimed vegetation; and (3), following the combination of various optimization features, the land use classification yielded the highest overall accuracy, at 88.16%. Furthermore, the comprehensive identification of various agricultural land classes, including farmland, orchards, and greenhouse vegetables, yielded the most desirable outcomes. The research results not only highlight the advantages of hyperspectral images for complex terrain classification and recognition but also address the previous limitations in the application of hyperspectral datasets over wide mining areas. Additionally, the results underscore the reliability of feature selection methods in reducing information redundancy and improving classification accuracy. The proposed feature selection combination, based on OHS hyperspectral datasets, offers technical support and guidance for the detailed classification of complex land use in mining areas and the accurate monitoring of agroecological environments.

Neural rehabilitation based on brain computer interface

BCI is a newly developed technology that can be used for neurological rehabilitation of brain injuries and paralysis. This article first reviews the history of BCI, and then introduces two major neurorehabilitation BCI technologies for neurorecording and neuromodulation, invasive and non-invasive. For each technology, we describe the challenges of each technology, analyzing the current state and future development trends. In terms of non-invasive technology, the most mature and extensive EEG brain signal analysis methods and two brain regulation technologies, transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), as well as the application of brain signal analysis techniques such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy fNIRS) were also discussed. In terms of invasive technology, the implantable neural interface technology focuses on implantable neural interface technology, which collects cortical electrical brain signals (ECoG), which can obtain more original neural information and has high temporal and spatial resolution characteristics. In recent years, with the development of microelectronics technology and material technology, invasive BCI technology has made a lot of cutting-edge progress, which is an important future development direction of neurorehabilitation technology.

A novel two-dimensional phantom for electrical impedance tomography using 3D printing

Electrical impedance tomography (EIT) is an imaging method that can be used to image electrical impedance contrasts within various tissues of the body. To support development of EIT measurement systems, a phantom is required that represents the electrical characteristics of the imaging domain. No existing type of EIT phantom combines good performance in all three characteristics of resistivity resolution, spatial resolution, and stability. Here, a novel EIT phantom concept is proposed that uses 3D printed conductive material. Resistivity is controlled using the 3D printing infill percentage parameter, allowing arbitrary resistivity contrasts within the domain to be manufactured automatically. The concept of controlling resistivity through infill percentage is validated, and the manufacturing accuracy is quantified. A method for making electrical connections to the 3D printed material is developed. Finally, a prototype phantom is printed, and a sample EIT analysis is performed. The resulting phantom, printed with an Ultimaker S3, has high reported spatial resolution of 6.9 µm, 6.9 µm, and 2.5 µm for X, Y, and Z axis directions, respectively (X and Y being the horizontal axes, and Z the vertical). The number of resistivity levels that are manufacturable by varying infill percentage is 15 (calculated by dividing the available range of resistivities by two times the standard deviation of the manufacturing accuracy). This phantom construction technique will allow assessment of the performance of EIT devices under realistic physiological scenarios.

Experimental Study on Reducing Wavelength Dependency of Spatial Resolution Characteristics of a Digital Camera by MTF Correction

Experimental Study on Reducing Wavelength Dependency of Spatial Resolution Characteristics of a Digital Camera by MTF Correction

A bubble-enhanced lanthanide-doped up/down-conversion platform with tumor microenvironment response for dual-modal photoacoustic and near-infrared-II fluorescence imaging

As an important tumor diagnosis strategy in precision medicine, multimodal imaging has been widely studied. However, the weak imaging signal with low spatial resolution and the constant signal of lack of specific activation severely limit its disease diagnosis. Herein, a bubble-enhanced lanthanide-based up/down-conversion platform with tumor microenvironment response for dual-mode imaging, LDNP@DMSN-Au@CaCO3 nanoparticles (named as LDAC NPs) were successfully developed. Combining the advantages of photoacoustic imaging (PAI) and the second near-infrared window (NIR-II) fluorescence imaging (FI), significantly improved the accuracy of diseases diagnosis. LDAC NPs with flower-like structure were synthesized through the encapsulation of uniform lanthanide-doped nanoparticles (NaYbF4:Ce,Er@NaYF4 named LDNPs) with dendritic mesoporous silica (DMSN). The gold nanoparticles (Au NPs) were then in situ grown on the surface of DMSN and the surface were finally coated with a layer of calcium carbonate (CaCO3). Under the excitation of the 980 nm laser, LDNPs showed strong emission of NIR-II at 1550 nm due to the doping of Ce and Er ions, showcasing excellent spatial resolution and deep tissue penetration characteristics, while the resulting visible light emission (540 nm) enables Au NPs to generate PAI signals with the aid of LDNPs via the fluorescence resonance energy transfer effect. In acidic tumoral environment, CaCO3 layer could produce CO2 microbubbles, and the PAI signals of LDAC NPs could be further enhanced with the generation of CO2 bubbles due to the bubble cavitation effect. Simultaneously, the NIR-II FI of LDAC NPs was self-enhanced with the degradation of the CaCO3. This intelligent nanoparticle with stimulus-activated dual-mode imaging capability holds great promise in future precision diagnostics.

Impact of microwave observations on the estimation of Arctic sea surface temperatures

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (>58°N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers.

Ambient nitrogen dioxide in 47 187 neighbourhoods across 326 cities in eight Latin American countries: population exposures and associations with urban features

Health research on ambient nitrogen dioxide (NO2) is sparse in Latin America, despite the high prevalence of NO2-associated respiratory diseases in the region. This study describes within-city distributions of ambient NO2 concentrations at high spatial resolution and urban characteristics associated with neighbourhood ambient NO2 in 326 Latin American cities. We aggregated estimates of annual surface NO2 at 1 km2 spatial resolution for 2019, population counts, and urban characteristics compiled by the SALURBAL project to the neighbourhood level (ie, census tracts). We described the percentage of the urban population living with ambient NO2 concentrations exceeding WHO air quality guidelines. We used multilevel models to describe associations of neighbourhood ambient NO2 concentrations with population and urban characteristics at the neighbourhood and city levels. We examined 47 187 neighbourhoods in 326 cities from eight Latin American countries. Of the roughly 236 million urban residents observed, 85% lived in neighbourhoods with ambient annual NO2 above WHO guidelines. In adjusted models, higher neighbourhood-level educational attainment, closer proximity to the city centre, and lower neighbourhood-level greenness were associated with higher ambient NO2. At the city level, higher vehicle congestion, population size, and population density were associated with higher ambient NO2. Almost nine out of every ten residents of Latin American cities live with ambient NO2 concentrations above WHO guidelines. Increasing neighbourhood greenness and reducing reliance on fossil fuel-powered vehicles warrant further attention as potential actionable urban environmental interventions to reduce population exposure to ambient NO2. Wellcome Trust, National Institutes of Health, Cotswold Foundation.

Machine learning-coupled tactile recognition with high spatiotemporal resolution based on cross-striped nanocarbon piezoresistive sensor array

Flexible pressure sensor arrays have been playing important roles in various applications of human–machine interface, including robotic tactile sensing, electronic skin, prosthetics, and human–machine interaction. However, it remains challenging to simultaneously achieve high spatial and temporal resolution in developing pressure sensor arrays for tactile sensing with robust function to achieve precise signal recognition. This work presents the development of a flexible high spatiotemporal piezoresistive sensor array (PRSA) by coupling with machine learning algorithms to enhance tactile recognition. The sensor employs cross-striped nanocarbon-polymer composite as an active layer, though screen printing manufacture processes. A miniaturized signal readout circuit and transmission board is developed to achieve high-speed acquisition of distributed pressure signals from the PRSA. Test results indicate that the developed PRSA platform simultaneously possesses the characteristics of high spatial resolution up to 1.5 mm, fast temporal resolution of about 5 ms, and long-term durability with a variation of less than 2%. The PRSA platform also exhibits excellent performance in real-time visualization of multi-point touch, mapping embossed shapes, and tracking motion trajectory. To test the performance of PRSA in recognizing different shapes, we acquired pressure images by pressing the finger-type device coated with PRSA film on different embossed shapes and implementing the T-distributed Stochastic Neighbor Embedding model to visualize the distinction between images of different shapes. Then we adopted a one-layer neural network to quantify the discernibility between images of different shapes. The analysis results show that the PRSA could capture the embossed shapes clearly by one contact with high discernibility up to 98.9%. Collectively, the PRSA as a promising platform demonstrates its promising potential for robotic tactile sensing.

Second-order optimization for multiparameter reflection waveform inversion in acoustic VTI media

SUMMARY The seismic wave velocity in subsurface formations typically varies both spatially and directionally, resulting in a heterogeneous and anisotropic medium. To successfully image complex structures and potential reservoirs using multichannel seismic data, high-resolution interval velocity models are required. However, most controlled-source seismic recordings lack wide-angle signals, such as diving and refracted waves that propagate through deep formations. Consequently, geophysicists rely on the traveltimes and waveforms of reflections or back-scatterings in finite-frequency and finite-offset seismograms to estimate velocity and account for possible anisotropy. Recently, wave-equation based reflection waveform inversion (RWI) has emerged as an active research topic due to its ability to recover intermediate-wavelength model components that cannot be retrieved in reflection traveltime tomography. Nevertheless, the widely used gradient-type local optimization suffers from multiparameter trade-off and slow convergence of RWI in anisotropic media. Analysis of the Fréchet derivative reveals that the sensitivity of reflections to changes in long-to-intermediate wavelengths of the anisotropic velocities is jointly controlled by the specular reflection on the interface and the radiation pattern of scattered fields concerning the model parameters along the wave paths. Anatomy of the approximate Hessian reveals characteristics of parameter coupling and spatial resolution in the context of multiparameter RWI, inspiring the development of a matrix-free Gauss–Newton algorithm based on the second-order adjoint-state method for large-scale applications. Synthetic and real data examples demonstrate that the proposed approach can appropriately handle overburden heterogeneities and anisotropies, and thus improve imaging of underlying complex structures such as dipping and small-scale faults in the deep parts.

Nanoscale analysis of magnetic chains consisting of cobalt mesospheres by full‐field transmission x‐ray tomography

AbstractFull‐field transmission x‐ray tomography can obtain 3D morphology and configurations of nanoscale materials. In this study, we further demonstrate the feasibility of this technique using magnetic chains consisting of cobalt mesospheres. The preliminary images presented in this article clearly reveal that three types of cobalt nanoparticles, namely, hollow, core–shell, and solid were found for magnetic chain synthesis. Furthermore, the tomographic analyses revealed the growth mechanism of magnetic chains self‐assembled by cobalt nanoparticles. We may claim that the spatial resolution and contrast characteristics of the full‐field transmission x‐ray tomography can satisfy the research requirements of nanomaterial science. Hence, transmission x‐ray tomography could be a powerful complementary method for imaging and analysis of nanoscale materials.

Rectangular cross-sectional Nd3+/Yb3+ co-doped AlN nanorods with strong up-conversion emission for high-sensitivity optical thermometry

Due to the characteristics of non-contact, high sensitivity and spatial resolution, nanoscale optical thermometry is necessary in many applications such as micro/nano electronics, integrated optics and biomedicine. Herein, we report the synthesis of Nd3+/Yb3+ co-doped AlN (AlN:Nd3+/Yb3+) nanorods using an arc discharge method and investigate their phase, composition and morphology using XRD, Raman, XPS, EDS, ICP, SEM and TEM. The nanorods with rectangular cross-section are highly single crystalline and have a uniform smooth surface. Upon excitation at 980 nm, AlN:Nd3+/Yb3+ nanorods exhibit up-conversion (UC) visible emission bands (around 544, 595 and 667 nm) and near-infrared emission bands (around 755 and 806 nm), which correspond to intra-4f electron transitions of the Nd3+ ions. Later, the temperature dependence of UC luminescence behavior was systemically studied using luminescence intensity ratio (LIR) technique. It was verified that LIRs with non-thermally coupled levels (4G7/2/4F5/2 or 4G7/2/4F7/2) are suitable for temperature sensing. The maximum relative sensitivity based on 4G7/2/4F7/2 is 4.63% K−1 at 298 K, which is one of the highest values reported for optical thermometers. This work provides an efficient method for synthesizing wide-band gap semiconductors with doped size-mismatched functional atoms that can be used for high-sensitivity optical thermometry.

Upscaling peatland mapping with drone-derived imagery: impact of spatial resolution and vegetation characteristics

ABSTRACT Northern peatland functions are strongly associated with vegetation structure and composition. While large-scale monitoring of functions through remotely sensed mapping of vegetation patterns is therefore promising, knowledge on the interdependency between spatial resolution of acquired imagery, spatial vegetation characteristics, and resulting mapping accuracy needs to be improved. We evaluated how classification accuracy of commonly used vegetation mapping units (microforms and plant functional types) was affected by spatial resolution of acquired imagery and several spatial characteristics of the vegetation itself (size, shape, configuration, and diversity). To this end, we collected very high-resolution drone imagery (<0.05 m) from eight Irish peatlands differing in vegetation composition and pattern complexity in September 2021 and 2022. We then resampled this imagery from pixel sizes of 0.027–1 m and classified both mapping units at all unique spatial resolutions. Hereafter, we computed spatial vegetation characteristics for each of the eight classified images at 0.027 m spatial resolution to determine their role in defining minimum spatial resolution requirements for both microforms and plant functional types. We found that overall classification accuracy of microforms and plant functional types was consistently high (>90%) for all studied peatlands until average spatial resolutions were reached of 0.5 m ± 0.2 m and 0.25 m ± 0.1 m, respectively. However, variability within mapping units was considerable, with overall minimum spatial resolution ranging between 0.25 and 0.7 m for microforms and between 0.15 and 0.35 m for plant functional types. Individual classes even varied from 0.05 to 1 m. Furthermore, spatial vegetation characteristics were important drivers of minimum spatial resolution for microforms, but not for plant functional types. Particularly, peatlands with larger, compacter, and more clustered microform patches could be classified with coarser spatial resolution imagery (up to 0.7 m), while peatlands with small, complex, diverse, and more finely distributed patches required higher spatial resolutions (minimally 0.25 m). Based on these findings, we conclude that spatial vegetation characteristics strongly determine minimum required spatial resolution and thus affect upscaling of peatland vegetation mapping beyond the landscape scale by constraining the use of specific remote sensing platforms.

Enhancing Spatial Resolution of GNSS-R Soil Moisture Retrieval through XGBoost Algorithm-Based Downscaling Approach: A Case Study in the Southern United States

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique.

Development, validation, and simplification of a scanner-specific CT simulator.

Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2HU and the maximum was 22.5HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.

Hydrogel Micropillar Array for Temperature Sensing in Fluid.

Localized temperature sensing and control on a micron-scale have diverse applications in biological systems. We present a micron-sized hydrogel pillar array as potential temperature probes and actuators by exploiting sensitive temperature dependence of their volume change. Soft lithography-based molding processes were presented to fabricate poly N-isopropyl acrylamide (p-NIPAAm)-based hydrogel pillar array on a glass substrate. Au nanorods as light-induced heating elements were embedded within the hydrogel pillars, and near-infrared (NIR) light was used to modulate temperature in a local area. First, static responses of the micro-pillar array were characterized as a function of its temperature. It was shown that the hydrogel had a sensitive volume transition near its low critical solution temperature (LCST). Furthermore, we showed that LCST could be readily adjusted by utilizing copolymerizing with acrylamide (AAM). To demonstrate the feasibility of spatiotemporal temperature mapping and modulation using the presented pillar array, pulsed NIR light was illuminated on a local area of the hydrogel pillar array, and its responses were recorded. Dynamic temperature change in water was mapped based on the abrupt volume change characteristics of the hydrogel pillar, and its potential actuation using NIR light was successfully demonstrated. Considering that the structure can be arrayed in a two-dimensional pixel format with high spatial resolution and high sensitive temperature characteristics, the presented method and the device structure can have diverse applications to change and sense local temperatures in liquid. This is particularly useful in biological systems, where their physiological temperature can be modulated and mapped with high spatial resolution.

Development and validation of a 3D anthropomorphic phantom for dental CBCT imaging research.

Optimization of dental cone beam computed tomography (CBCT) imaging is still in a preliminary stage and should be addressed using task-based methods. Dedicated models containing relevant clinical tasks for image quality studies have yet to be developed. To present a methodology to develop and validate a virtual adult anthropomorphic voxel phantom for use in task-based image quality optimization studies in dental CBCT imaging research, focusing on root fracture (RF) detection tasks in the presence of metal artefacts. The phantom was developed from a CBCT scan with an isotropic voxel size of 0.2mm, from which the main dental structures, mandible and maxilla were segmented. The missing large anatomical structures, including the spine, skull and remaining soft tissues, were segmented from a lower resolution full skull scan. Anatomical abnormalities were absent in the areas of interest. Fine detailed dental structures, that could not be segmented due to the limited resolution and noise in the clinical data, were modelled using a-priori anatomical knowledge. Model resolution of the teeth was therefore increased to 0.05mm. Models of RFs as well as dental restorations to create the artefacts, were developed, and could be inserted in the phantom in any desired configuration. Simulated CBCT images of the models were generated using a newly developed multi-resolution simulation framework that incorporated the geometry, beam quality, noise and spatial resolution characteristics of a real dental CBCT scanner. Ray-tracing and Monte Carlo techniques were used to create the projection images, which were reconstructed using the classical FDK algorithm. Validation of the models was assessed by measurements of different tooth lengths, the pulp volume and the mandible, and comparison with reference values. Additionally, the simulated images were used in a reader study in which two oral radiologists had to score the realism level of the model's normal anatomy, as well as the modelled RFs and restorations. A model of an adult head, as well as models of RFs and different types of dental restorations were created. Anatomical measurements were consistent with ranges reported in literature. For the tooth length measurements, the deviations from the mean reference values were less than 20%. In 77% of all the measurements, the deviations were within 10.1%. The pulp volumes, and mandible measurements were within one standard deviation of the reference values. Regarding the normal anatomy, both readers considered the realism level of the dental structures to be good. Background structures received a lower realism score due to the lack of detailed enough trabecular bone structure, which was expected but not the focus of this study. All modelled RFs were scored at least adequate by at least one of the readers, both in appearance and position. The realism level of the modelled restorations was considered to be good. A methodology was proposed to develop and validate an anthropomorphic voxel phantom for image quality optimization studies in dental CBCT imaging, with a main focus on RF detection tasks. The methodology can be extended further to create more models representative of the clinical population.