Imaging Mapping Research Articles

Anomaly classification based on self-supervised learning and its application

BackgroundAnomaly classification remains a challenging task in computer vision applications across diverse practical fields such as industrial detection and security check. The purpose of this study is to develop a new anomaly classification method based on self-supervised learning (Self-ACM), which is expected to enhance anomaly classification accuracy significantly. MethodsThe feature maps of images were abstracted using VGG16 model pre-trained on ImageNet, which were then fed into the innovative model to capture the normality distribution within the dataset domain. Then, a self-supervised adversarial anomaly classification learning framework was proposed to facilitate the acquisition of a higher-level semantic representations for improved anomaly detection. Thirdly, we collected and constructed a novel terahertz (THZ) dataset, which serves as a pioneering resource for benchmarking anomaly classification tasks in the field. ResultsThrough a series of rigorous experiments, our findings unequivocally demonstrate the following key insights: Firstly, harnessing feature maps as input data yielded a significant enhancement in anomaly detection performance, underscoring the effectiveness of this approach. Secondly, the integration of self-supervision enriched the dataset with invaluable information, empowering both the discriminator and generator to acquire superior feature representations. The culmination of these advancements is our novel method achieving unparalleled state-of-the-art performance across multiple benchmark datasets. This breakthrough underscores the transformative impact of our approach on anomaly detection methodologies, solidifying its position as a pioneering solution in the field. ConclusionThe Self-ACM strategy not only advances anomaly detection methodologies but also offers a remarkable contribution to dataset creation, setting a new standard for anomaly classification research.

Multistate Dynamics and Kinetics of CO2 Activation by Ta+ in the Gas Phase: Insights into Single-Atom Catalysis.

The activation of carbon dioxide (CO2) by a transition-metal cation in the gas phase is a unique model system for understanding single-atom catalysis. The mechanism of such reactions is often attributed to a "two-state reactivity" model in which the high-energy barrier of a spin state correlating with ground-state reactants is avoided by intersystem crossing (ISC) to a different spin state with a lower barrier. However, such a "spin-forbidden" mechanism, along with the corresponding dynamics, has seldom been rigorously examined theoretically, due to the lack of global potential energy surfaces (PESs). In this work, we report full-dimensional PESs of the lowest-lying quintet, triplet, and singlet states of the TaCO2+ system, machine-learned from first-principles data. These PESs and the corresponding spin-orbit couplings enable us to provide an extensive theoretical characterization of the dynamics and kinetics of the reaction between the tantalum cation (Ta+) and CO2, which have recently been investigated experimentally at high collision energies using crossed beams and velocity map imaging, as well as at thermal energies using a selected-ion flow tube apparatus. The multistate quasi-classical trajectory simulations with surface hopping reproduce most of the measured product translational and angular distributions, shedding valuable light on the nonadiabatic reaction dynamics. The calculated rate coefficients from 200 to 600 K are also in good agreement with the latest experimental measurements. More importantly, these calculations revealed that the reaction is controlled by intersystem crossing, rather than potential barriers.

Application of GIS & RS in Rainwater Harvesting for an Arid Region

In dry areas with variable rainfall, water shortages severely affect the livelihoods of local populations. Rainwater harvesting from land sites and roof-tops is a readily available solution to this problem. This study presents a GIS & RS-based methodology to identify potential land sites and roof-top rainwater harvesting potential using easily obtainable and freely available data. Slope, land cover, soil, and drainage maps were used to identify suitable rainwater harvesting land sites, which were further analyzed for surface runoff using the curve number method. This research also incorporated obtainable socio-economic factors in identifying the suitable locations of dams considering the implementation stage. Based on the proposed criteria, 44 suitable locations for dams were identified and validated using a validation map and Google Earth images. CROPWAT model analysis revealed that paddy water demand in Batticaloa would increase threefold if all available land for paddy cultivation was utilized. Therefore, abandoned lands can be utilized by erecting dams at those identified locations. Analysis of Google Earth images also revealed that roof rainwater harvesting would be sufficient to meet the basic water demand in the area. This study demonstrates that rainwater harvesting can be an effective strategy for addressing water scarcity in dry areas.

MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics

Abstract Background and objective Treatment planning through the diagnostic dimension of theranostics provides insights into predicting the absorbed dose of RPT, with the potential to individualize radiation doses for enhancing treatment efficacy. However, existing studies focusing on dose prediction from diagnostic data often rely on organ-level estimations, overlooking intra-organ variations. This study aims to characterize the intra-organ theranostic heterogeneity and utilize artificial intelligence techniques to localize them, i.e. to predict voxel-wise absorbed dose map based on pre-therapy PET. Methods 23 patients with metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA I&T RPT were retrospectively included. 48 treatment cycles with pre-treatment PET imaging and at least 3 post-therapeutic SPECT/CT imaging were selected. The distribution of PET tracer and RPT dose was compared for kidney, liver and spleen, characterizing intra-organ heterogeneity differences. Pharmacokinetic simulations were performed to enhance the understanding of the correlation. Two strategies were explored for pre-therapy voxel-wise dosimetry prediction: (1) organ-dose guided direct projection; (2) deep learning (DL)-based distribution prediction. Physical metrics, dose volume histogram (DVH) analysis, and identity plots were applied to investigate the predicted absorbed dose map. Results Inconsistent intra-organ patterns emerged between PET imaging and dose map, with moderate correlations existing in the kidney (r = 0.77), liver (r = 0.5), and spleen (r = 0.58) (P < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R2 = 0.92 for kidney). The DL model improved the mean slope of fitting lines in identity plots (199% for liver), when compared to the theoretical optimal results of the organ-dose approach. Conclusion Our results demonstrated the intra-organ heterogeneity of pharmacokinetics may complicate pre-therapy dosimetry prediction. DL has the potential to bridge this gap for pre-therapy prediction of voxel-wise heterogeneous dose map.

Automated detection and tracking of photovoltaic modules from 3D remote sensing data

This study addresses the growing demand for increased performance and reliability of photovoltaic (PV) installations by developing innovative monitoring technologies. The strategy consists of flying an unmanned aerial vehicle (UAV) equipped with a dual camera (RGB and thermal) over the PV plant of interest, followed by the generation of photogrammetric 3D models derived from the overlapped aerial images. The resulting datasets involve orthoimages and point clouds by processing RGB and thermal imagery. The key contribution of this study is twofold: (1) the thermal image mapping on dense and high-resolution point clouds that represent the status and geometry of PV solar modules, and (2) the automatic identification of individual solar panels in 3D space and their thermal characterization along their oriented surface. Then, the vector layer of each PV panel is projected onto the 3D thermal point cloud to extract the thermal values associated with each panel. To evaluate the capability of the proposed method, it was replicated in different scenarios, considering rural and urban environments with different light conditions and PV structures. The results demonstrate the robustness of our method, which achieves a remarkably high detection rate, around 99.12% of true positives, and a low false positive rate, close to 0.88%. Consequently, this method means an advance over previous work by proposing a comprehensive and automated solution for individual and highly detailed monitoring of each solar panel from 3D remotely sensed data. This study opens up new frontier research related to real-time monitoring of photovoltaic modules, an inspection of solar photovoltaic cells, the simulation of solar resources and forecasting, the development of digital twins, solar radiation modelling, and analysis of modular floating solar farms under wave motion.

A 5K Efficient Low-Light Enhancement Model by Estimating Increment between Dark Image and Transmission Map Based on Local Maximum Color Value Prior

Low-light enhancement (LLE) has seen significant advancements over decades, leading to substantial improvements in image quality that even surpass ground truth. However, these advancements have come with a downside as the models grew in size and complexity, losing their lightweight and real-time capabilities crucial for applications like surveillance, autonomous driving, smartphones, and unmanned aerial vehicles (UAVs). To address this challenge, we propose an exceptionally lightweight model with just around 5K parameters, which is capable of delivering high-quality LLE results. Our method focuses on estimating the incremental changes from dark images to transmission maps based on the low maximum color value prior, and we introduce a novel three-channel transmission map to capture more details and information compared to the traditional one-channel transmission map. This innovative design allows for more effective matching of incremental estimation results, enabling distinct transmission adjustments to be applied to the R, G, and B channels of the image. This streamlined approach ensures that our model remains lightweight, making it suitable for deployment on low-performance devices without compromising real-time performance. Our experiments confirm the effectiveness of our model, achieving high-quality LLE comparable to the IAT (local) model. Impressively, our model achieves this level of performance while utilizing only 0.512 GFLOPs and 4.7K parameters, representing just 39.1% of the GFLOPs and 23.5% of the parameters used by the IAT (local) model.

Imaging and ferroelectric orientation mapping of photostriction in a single Bismuth Ferrite nanocrystal

The exploration of multiferroic materials and their interaction with light at the nanoscale presents a captivating frontier in materials science. Bismuth Ferrite (BiFeO3, BFO), a standout among these materials, exhibits room-temperature ferroelectric and antiferromagnetic behaviour and magnetoelectric coupling. Of particular interest is the phenomenon of photostriction, the light-induced deformation of crystal structures, which enhances the prospect for device functionality based on these materials. Understanding and harnessing multiferroic phenomena holds significant promise in various technological applications, from optoelectronics to energy storage. The orientation of the ferroelectric axis is an important design parameter for devices formed from multiferroic materials. Determining its orientation in the laboratory frame of reference usually requires knowing multiple wavevector transfer (Q-Vector) directions, which can be challenging to establish due to the need for extensive reciprocal-space searches. Our study demonstrates a method to identify the ferroelectric axis orientation using Bragg Coherent X-ray Diffraction Imaging (BCDI) measurements at a single Q-vector direction. This method involves applying photostriction-inducing laser illumination across various laser polarisations. Our findings reveal that photostriction primarily occurs as a surface phenomenon at the nanoscale. Moreover, a photo-induced crystal length change ranging from 30 to 60 nm was observed, consistent with earlier findings on bulk material.

Ion Velocity Map Imaging Study of the Reactive Collisions between Carbon Dioxide and Helium Ion.

The reactive collision between He+ and CO2 plays an important role in substance evolutions of the planetary CO2-rich atmosphere. Using a three-dimensional ion velocity map imaging technique, we investigate the low-energy ion-molecule reactions He+ + CO2 → He + CO2+/He + CO+ + O/He + CO + O+. The velocity images of the products CO+ and O+ of dissociative charge-exchange reactions are distinctly different from those of charge-exchange product CO2+. The remarkable features of stereodynamics are observed in the dissociative charge-exchange reaction and are attributed to the spatial alignment of the initially random target CO2 during the He+ approach. Branching ratios of different channels of dissociative charge exchange are further obtained with the Doppler kinematics model, indicating a high preference for the energy-resonant channel.

High-Entropy Lithium Niobate Nanocubes for Photocatalytic Water Splitting under Visible Light.

The vast compositional space available in high-entropy oxide semiconductors offers unique opportunities for electronic band structure engineering in an unprecedented large room. In this work, with wide band gap semiconductor lithium niobate (LiNbO3) as a model system, we show that the substitutional addition of high-entropy metal cation mixtures within the Nb sublattice can lead to the formation of a single-phase solid solution featuring a substantially narrowed band gap and intense broadband visible light absorption. The resulting high-entropy LiNbO3 [denoted as Li(HE)O3] crystallizes as well-faceted nanocubes; atomic-resolution imaging and elemental mapping via transmission electron microscopy unveil a distinct local chemical complexity and lattice distortion, characteristics of high-entropy stabilized solid solution phases. Because of the presence of high-entropy stabilized Co2+ dopants that serve as active catalytic sites, Li(HE)O3 nanocubes can accomplish the visible light-driven photocatalytic water splitting in an aqueous solution containing methanol as a sacrificial electron donor without the need of any additional co-catalysts.

Localized surface plasmon resonance (LSPR) excitation on single silver nanoring with nanoscale surface roughness

With the expansion of the application of high-sensitivity Surface-enhanced Raman scattering (SERS) technique, micro SERS-active substrates with rich optical properties and high-level functions are desired. In this study, silver nanorings with nanoscale surface roughness were fabricated as a new type of enclosed quasi-2D micro-SERS-active substrate. Highly-crystalline spherical and hemispherical silver nanoprotrusions were densely and uniformly distributed over the entire surface of the nanorings. The SERS signals were significantly enhanced on the roughened silver nanorings which were mainly derived from the maximal localized surface plasmon resonance (LSPR) points at the junctions between adjacent coupled nanoprotrusions on the roughened nanorings. The mapping image shows a uniform and intense LSPR enhancement over the nanorings, owing to the uniform and dense distribution of silver nanoprotrusions and the resulting uniform distribution of maximal LSPR points on the roughened nanorings. The dark-field spectra further indicated that the single roughened silver nanoring had significant LSPR enhancement, a wide LSPR frequency-range response, and adaptability for SERS enhancement. Notably, both the measured and simulated results demonstrate that the maximal LSPR enhancement at the junctions between the nanoprotrusions, which are distributed on the inner surface of the silver nanoring, is higher than that on the outer surface because of the plasmon-focusing effect of the enclosed silver nanoring, which leads to the lateral asymmetrical distribution of LSPR intensity, indicating more LSPR and SERS features. These results indicate that single roughened silver nanorings exhibit excellent performance as a new type of enclosed quasi-2D silver nanoring micro-SERS-active substrate, microzone LSPR catalysis, and micro/nanodevices.

Instantaneous thermometry imaging using two-photon laser-induced fluorescence.

Measuring temperature in complex two-phase flows is crucial for understanding the dynamics of heat and mass transfer. In this Letter, we introduce a novel, to the best of our knowledge, optical approach based on the combination of two-photon laser-induced fluorescence (2p-LIF) imaging and two-color laser-induced fluorescence (2CLIF) for instantaneous temperature mapping of complex liquid media. Using Kiton Red (KR) and Rhodamine 560 (R560), a temperature sensitivity of 1.54%/∘C has been achieved over a range of 17-60°C. The monitoring of two-dimensional transient temperature dynamics in the heating and degassing of water shows the efficiency of the 2p-2CLIF. This new approach contributes to the toolkit of optical temperature measurement techniques, providing a robust solution for studying transient scattering media and high-speed two-phase flows.

Imaging Rovibrational Excitation of Scattered YO Molecules in Inelastic Collisions with Kr and Ne.

Energy transfer between atoms and molecules is fundamental to many physical and chemical processes, and understanding the mechanisms and outcomes of energy transfer is crucial for various applications in physics and chemistry. Here, the rovibrational excitation of YO(X 2Σ+) molecules with the collision of Kr and Ne has been studied in the laser-ablation crossed beam and time-sliced ion velocity map imaging setup in combination with the resonance enhanced multiphoton ionization scheme. Significant changes in the angular distribution for different rovibrational excitations of YO molecules are observed with the collision of Kr. The sharp forward distribution for low rovibrational excitation of YO(v' = 0, 1) molecules suggest that the weak attractive potential between Kr and YO is dominant at large impact parameters. Comparatively, the strong sideway distribution for highly rovibrationally excited YO(v' = 1, 2, 3, and 5) is due to rainbow scattering from the stronger attractive potential of Kr···YO at relatively small impact parameters. The more isotropic angular distribution in the highly rovibrationally excited YO(v' = 11) indicates the formation of a short-lived complex. A change in the angular distribution of scattered YO with different rovibrational excitations was also observed in the collisions of Ne. For YO as a heteronuclear diatomic molecule, collisions of the Y- and the O-end of YO with rare gases would affect the contribution of inelastic processes at different impact parameters.

Facile synthesis of silver nanoparticles loaded spent cathodic Lithium-Ion battery components for enhanced bifunctional photocatalysis: Investigating the influence of magnetic properties

Due to resource depletion and environmental concerns regarding battery disposal, it is becoming more essential to recycle valuable metals from wasted lithium-ion batteries (LIBs). A novel approach is proposed in this study for the efficient bifunctional photocatalysis of LIBs components by integrating AgNPs in an environmentally sustainable manner via a sustainable hydrothermal process. The HRTEM and image mapping reveal a carbon matrix (C/Li3Fe5O8/Ag) with a dispersed arrangement of AgNPs (5–10 wt%). By acting as a reducing agent, graphite plays a crucial role in the transformation of Ag+ ions into AgNPs. The homogenous distribution of Ag NPs observed in the high-resolution TEM images further supports the role of graphite in the modification and decoration of Ag NPs onto the surface of the extracted spent cathode sample. The detection of a spot matching the (200) plane of Ag in the selected area electron diffraction (SAED) measurements confirms the successful formation of AgNPs. Additionally, the presence of a ring corresponding to the (110) plane of graphite in the SAED measurements suggests the coexistence of graphite with AgNPs in the composite heterostructures. The nitrogen adsorption isotherm analysis was conducted to explore the physical properties of the AgNPs@LIBs nanocomposite, providing valuable insights into its pore size distribution and Brunauer–Emmett–Teller (BET) surface area. The nanocomposites exhibited paramagnetic behavior, as revealed by vibrational sample magnetometer (VSM) measurements, with saturation magnetization values ranging from 76.3 × 10−3 to 36.13 × 10−3 emu/g. The study involved the photodegradation of oxytetracycline (OTC), a representative pharmaceutical pollutant, along with an investigation into the potential of Cr(VI) reduction. Under optimal component ratios, a significant improvement in pollutant removal was achieved under UV radiation, reaching up to 75 % for OTC and 80 % for Cr(VI), highlighting the efficiency of the process.

Accuracy of Mirror Image Mapping of Lymphatic Tract for High-stage and Reoperative Lymphaticovenular Anastomosis: Intraoperative Analysis and Early Clinical Outcome.

Indocyanine green lymphography (ICGL) generally has a nonlinear pattern in advanced-stage lymphedema. Despite the lack of a linear pattern ICGL, lymphatic vessels have been discovered in several studies. The purpose of this work was to establish lymphatic mapping utilizing information from the contralateral limb and to illustrate the symmetry of lymphatic systems. Data were retrospectively collected from 81 patients who underwent lymphaticovenular anastomosis (LVA) using the contralateral mapping technique during 2018 to 2022. The sensitivity, specificity, accuracy, and negative and positive predictive values of this technique were calculated and analyzed. Lymphatic vessels were identified in 85.2% of the upper and 82.3% of the lower limb presumed sites using the contralateral mapping technique. The positive predictive value for successful LVA anastomosis was 93.8% for upper limb and 92.3% for lower limb cases. This mirror image technique's accuracy was 91.7% and 91.1%, for the upper limb and lower limb group, respectively. Between reoperative and new LVA cases, there was no statistically significant difference in the number, type, or diameter of lymphatic vessels or number of anastomoses. LVA with contralateral mapping technique is an effective method for patients with high-stage lymphedema with a nonlinear pattern on ICGL.

Segregation effects of doped VN and ZrN on magnetic and microstructural characteristics of FePt-based films

Segregants are essential materials for forming the complexation or granular microstructure in thin films for magnetic recording media. One such film, FePt-BN, has a metallic L10 FePt main phase as its matrix, in addition to having BN and C segregants at the grain boundaries. Amorphous BN has sufficient mechanical strength to support the columnar growth of FePt grains, however, it has a lower deposition rate resulting in columnar grains with a nonuniform aspect ratio. Moreover, some B diffuses in the interstitial site of FePt lattices. To overcome these limitations, this study added the transition nitride VN and ZrN with equal volume percentages to replace some BN segregants in FePt(BN, Ag, C) films: substantially different segregation behaviors were observed. Microstructural imaging mapping revealed that although VN segregated the FePt grains boundary, ZrN was diffused in the lattice. The FePt-BN-ZrN film thus had more disordered grains contributing to a higher in-plane magnetization and wider hard axis magnetic hysteresis loops than did FePt-BN-VN. The FePt-BN-VN film has the highest perpendicular magnetic anisotropy constant (Ku= 2.17 × 106 J/m3) and coercivity (Hc= 4 T); moreover, parallel remanence magnetization was minimal with a linear in-plane magnetization loop. The FePt-BN-VN film was sputtered directly on MgO(100) but depositing the FePt-BN-ZrN film required a 2-nm-thick FePt nucleation layer. The high atomic number of Zr and high sputter yield ratio (YN/Ymetal) of ZrN may increase the sites occupied by N, potentially affecting ZrN segregation.

NASICON-structure Na3+xMnVxTi1-x(PO4)3/C cathode for sodium-ion battery with excellent cycling performance and high specific capacity

The recently discovered Na3MnTi(PO4)3 cathode material has an extremely high theoretical capacity, but its poor cycling performance makes it challenging to apply. In this work, a series of NASICON-type Na3+xMnVxTi1-x(PO4)3/C (x = 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1) cathode materials were prepared by sol-gel method for sodium-ion batteries. X-ray diffraction (XRD) indicates that vanadium doping has increased the cell spacing within the material and the cell volume. From the results of X-ray photoelectron spectroscopy (XPS) and Energy dispersive X-ray spectroscopy (EDS) mapping images, it is shown that vanadium has been successfully doped into the material. Among them, the Na3·3MnV0·3Ti0·7(PO4)3/C delivers an outstanding specific capacity (121.7 mAh g−1 at 0.1C, C = 135 mA g−1) and Na3·4MnV0·4Ti0·6(PO4)3/C shows an excellent capacity retention (80.88 % after 1500 cycles at 1C). Notably, the coulombic efficiency of both materials during cycling is close to 100 %, indicating that these materials have great practical application value.

MRI based evaluation of carcinoma gallbladder using apparent diffusion coefficient (ADC) and diffusion weighted imaging (DWI)

Background Carcinoma of the gallbladder is a malignant neoplasm, which is associated with a poor prognosis due to its often-asymptomatic nature and late-stage diagnosis. Non-invasive imaging techniques play a key role in the early detection and characterization of gallbladder carcinoma. This thesis presents a comprehensive analysis of the utility of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) mapping in the magnetic resonance imaging (MRI) evaluation of carcinoma of the gallbladder. The primary objective of this research was to investigate the exact diagnostic accuracy and potential clinical applications of DWI and ADC in assessing carcinoma of the gallbladder and related tumour aggressiveness. A systematic review of existing literature was conducted to establish a foundation for this study with efforts to acquire relevant imaging data. The results of this study demonstrate the efficacy of DWI and ADC in the detection of carcinoma of the gallbladder when compared to conventional MRI sequences. These advanced imaging techniques hold promise in increasing the accuracy of diagnosis, ultimately leading to improved patient outcomes and more informed clinical decision making. The potential of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) mapping in the evaluation of gallbladder cancer using magnetic resonance imaging (MRI) is thoroughly analysed in this thesis. Methods A descriptive prospective study will be done at Acharya Vinoba Bhave Rural Hospital, Sawangi, involving 20 patients with clinically suspected carcinoma gallbladder, who are referred to the department of Radiodiagnosis. These patients will be subjected to study-purposive sampling. Conclusion After an appropriate statistical analysis, we expect to assess the role of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) mapping in the magnetic resonance imaging (MRI) evaluation of carcinoma of the gallbladder. CTRI registration This trial has been submitted to Clinical Trials Registry-India (CTRI) for Review. Registration Number: REF/2023/09/072780 Registration date:01/05/2023 URL of trial: https://www.ctri.nic.in/

Performance test of clean-coastal-water composite sentinel 2A image for shallow water bathymetry mapping

The “clean-coastal-water time-series composite” image preprocessing algorithm is one of the alternatives for obtaining minimal atmospheric disturbance satellite images for data inputs in Satellite-Derived Bathymetry (SDB). However, the performance of time-series composite images for shallow water bathymetric mapping needs to be assessed, considering the presence of pixel and band dispersion during the composite image construction process. This research aims to study the result of a clean-coastal-water time-series composite Sentinel-2 by using cloud computing preprocessing as input data for SDB in shallow waters, compare the results of SDB from a Sentinel-2 single date image and from the clean-coastal-water time-series composite image using a random forest regressor (RFR) algorithm in terms of accuracy and spatial distribution of the bathymetry, and evaluate the performance and quality of bathymetric maps obtained from a single date image and clean-coastal-water time-series composite Sentinel-2 images based on International Hydrographic Organization standards (IHO). The results show that not all time-series composite processes can produce clean images of interference from clouds, sunglints, bad waters, and pixel wave breaks. The acquisition time range affects the quality of the time-series composite image. The SDB RFR model can handle the complex relationship between the in situ water depth variable and the surface reflectance value of a clean-coastal-water time-series composite image that has pixel and band dispersion. Overall, the SDB RFR model was successfully used to predict bathymetry in shallow waters with accuracy that meets the IHO standard CATZOC requirements, with RMSE and MAE values of less than 0.5 m as well as TVU confidence level values of more than 95% at a depth of 0–5 m resulting from each - respectively clean-coastal-water time-series composite images in shallow waters around Morotai Island.

Combined diffusion tensor imaging and quantitative susceptibility mapping to characterize normal-appearing white matter in self-limited epilepsy with centrotemporal spikes.

In brain development, Myelination is the characteristic feature of white matter maturation, which plays an important role in efficient information transmitting. The white matter abnormality has been reported to be associated with self-limited epilepsy with centrotemporal spikes (SeLECTS). This study aimed to detect the altered white matter region in the SeLECTS patients by the combination of diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM) technique. 27 children with SeLECTS and 23 age- and gender-matched healthy children were enrolled. All participants were scanned with 3.0-T MRI to acquire the structure, diffusion and susceptibility-weighted data. The susceptibility and diffusion weighted data were processed to obtain quantitative susceptibility map and fraction anisotropy (FA) map. Then voxel-wise tract-based spatial statistics (TBSS) were used to analyze quantitative susceptibility and FA data. Both DTI and QSM revealed extensive white matter alterations in the frontal, parietal, and temporal lobes in SeLECTS patients. The overlapped region of DTI and QSM analyses was located in the fiber tracts of the corona radiata. The FA values in this overlapped region were negatively correlated with the magnetic susceptibility values. Our results suggest that TBSS-based QSM can be employed as a novel approach for characterizing alterations in white matter in SeLECTS. And the combination of QSM and DTI can provide a more comprehensive evaluation of white matter integrity by utilizing different biophysical features.