Journal of Imaging最新文献

{"title":"Enhanced CATBraTS for Brain Tumour Semantic Segmentation.","authors":"Rim El Badaoui, Ester Bonmati Coll, Alexandra Psarrou, Hykoush A Asaturyan, Barbara Villarini","doi":"10.3390/jimaging11010008","DOIUrl":"10.3390/jimaging11010008","url":null,"abstract":"<p><p>The early and precise identification of a brain tumour is imperative for enhancing a patient's life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11766851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.","authors":"Ioannis Stathopoulos, Roman Stoklasa, Maria Anthi Kouri, Georgios Velonakis, Efstratios Karavasilis, Efstathios Efstathopoulos, Luigi Serio","doi":"10.3390/jimaging11010006","DOIUrl":"10.3390/jimaging11010006","url":null,"abstract":"<p><p>Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLAIR, T2, and T1ce) and their volume.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11766070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data.","authors":"Md Rejaul Karim, Shahriar Ahmed, Md Nasim Reza, Kyu-Ho Lee, Joonjea Sung, Sun-Ok Chung","doi":"10.3390/jimaging11010005","DOIUrl":"10.3390/jimaging11010005","url":null,"abstract":"<p><p>The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r²) of 0.98, a confidence interval (CI) of -0.14 to -0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m³ and 14.09 ± 2.10 m³, respectively, with an MAE of 0.57 m³, an RMSE of 0.61 m³, an r² value of 0.97, and a CI of -0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r² values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and -0.18 for tree spacing and 0.01, -0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11766997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Protocol for Color Difference Evaluation Under Stabilized LED Light.","authors":"Sofiane Vernet, Eric Dinet, Alain Trémeau, Philippe Colantoni","doi":"10.3390/jimaging11010004","DOIUrl":"10.3390/jimaging11010004","url":null,"abstract":"<p><p>There are two key factors to consider before implementing a color discrimination experiment. First, a set of color patches should be selected or designed for the specific purpose of the experiment to be carried out. Second, the lighting conditions should be controlled to eliminate the impact of lighting instability on the experiment. This paper addresses both of these challenges. It proposes a method to print pairs of color patches with non-noticeable color differences. It also proposes a method to stabilize the Spectral Power Distributions (SPDs) of a Light-Emitting Diode (LED) lighting system. Finally, it introduces an experimental protocol for a color discrimination study that will be performed thanks to the contributions presented in this paper.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11765870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified Center-Edge Angle in Children with Developmental Dysplasia of the Hip.","authors":"Katharina S Gather, Fabian Sporer, Christos Tsagkaris, Marco Götze, Simone Gantz, Sebastien Hagmann, Thomas Dreher","doi":"10.3390/jimaging11010003","DOIUrl":"10.3390/jimaging11010003","url":null,"abstract":"<p><p>Developmental dysplasia of the hip (DDH) is a prevalent developmental condition that necessitates early detection and treatment. Follow-up, as well as therapeutic decision-making in children younger than four years, is challenging because the center-edge (CE) angle of Wiberg is not reliable in this age group. The authors propose a modification of the CE angle (MCE) to achieve comparable reliability with the CE among children younger than four and set diagnostic thresholds for the diagnosis of DDH. 952 anteroposterior pelvic radiographs were retrospectively reviewed. The MCE is defined on X-ray pelvic overview images as the angle between the line connecting the epiphyseal joint center and the outer edge of the acetabulum, and perpendicular to the Hilgenreiner line. The MCE angle exhibited high sensitivity and specificity, as well as intrarater variability comparable to the CE among children younger and older than four years. The authors recommend cut-off values for the MCE angle; for children under four years old, the angle should be equal to or greater than 15 degrees; for those under eight years old, it should be equal to or greater than 20 degrees; and for those eight years old and older, it should be equal to or greater than 25 degrees. However, the MCE angle's reliability diminishes around the age of nine due to the curvature of the growth plate, which complicates accurate measurement. This study showed that the MCE angle can be used adequately in children under four years and could be used as a progression parameter to diagnose DDH.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11765889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.","authors":"Tarek Berghout","doi":"10.3390/jimaging11010002","DOIUrl":"10.3390/jimaging11010002","url":null,"abstract":"<p><p>Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019-2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11766058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking of Multispectral Pansharpening: Reproducibility, Assessment, and Meta-Analysis.","authors":"Luciano Alparone, Andrea Garzelli","doi":"10.3390/jimaging11010001","DOIUrl":"10.3390/jimaging11010001","url":null,"abstract":"<p><p>The term pansharpening denotes the process by which the geometric resolution of a multiband image is increased by means of a co-registered broadband panchromatic observation of the same scene having greater spatial resolution. Over time, the benchmarking of pansharpening methods has revealed itself to be more challenging than the development of new methods. Their recent proliferation in the literature is mostly due to the lack of a standardized assessment. In this paper, we draw guidelines for correct and fair comparative evaluation of pansharpening methods, focusing on the reproducibility of results and resorting to concepts of meta-analysis. As a major outcome of this study, an improved version of the additive wavelet luminance proportional (AWLP) pansharpening algorithm offers all of the favorable characteristics of an ideal benchmark, namely, performance, speed, absence of adjustable running parameters, reproducibility of results with varying datasets and landscapes, and automatic correction of the path radiance term introduced by the atmosphere. The proposed benchmarking protocol employs the haze-corrected AWLP-H and exploits meta-analysis for cross-comparisons among different experiments. After assessment on five different datasets, it was found to provide reliable and consistent results in ranking different fusion methods.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11766025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volumetric Humeral Canal Fill Ratio Effects Primary Stability and Cortical Bone Loading in Short and Standard Stem Reverse Shoulder Arthroplasty: A Biomechanical and Computational Study.","authors":"Daniel Ritter, Patric Raiss, Patrick J Denard, Brian C Werner, Peter E Müller, Matthias Woiczinski, Coen A Wijdicks, Samuel Bachmaier","doi":"10.3390/jimaging10120334","DOIUrl":"10.3390/jimaging10120334","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the effect of three-dimensional (3D) volumetric humeral canal fill ratios (VFR) of reverse shoulder arthroplasty (RSA) short and standard stems on biomechanical stability and bone deformations in the proximal humerus.</p><p><strong>Methods: </strong>Forty cadaveric shoulder specimens were analyzed in a clinical computed tomography (CT) scanner allowing for segmentation of the humeral canal to calculate volumetric measures which were verified postoperatively with plain radiographs. Virtual implant positioning allowed for group assignment (VFR < 0.72): Standard stem with low (<i>n</i> = 10) and high (<i>n</i> = 10) filling ratios, a short stem with low (<i>n</i> = 10) and high filling ratios (<i>n</i> = 10). Biomechanical testing included cyclic loading of the native bone and the implanted humeral component. Optical recording allowed for spatial implant tracking and the quantification of cortical bone deformations in the proximal humerus.</p><p><strong>Results: </strong>Planned filling ratios based on 3D volumetric measures had a good-to-excellent correlation (ICC = 0.835; <i>p</i> < 0.001) with implanted filling ratios. Lower canal fill ratios resulted in significantly higher variability between short and standard stems regarding implant tilt (820 N: <i>p</i> = 0.030) and subsidence (220 N: <i>p</i> = 0.046, 520 N: <i>p</i> = 0.007 and 820 N: <i>p</i> = 0.005). Higher filling ratios resulted in significantly lower bone deformations in the medial calcar area compared to the native bone, while the bone deformations in lower filling ratios did not differ significantly (<i>p</i> > 0.177).</p><p><strong>Conclusions: </strong>Lower canal filling ratios maintain dynamic bone loading in the medial calcar of the humerus similar to the native situation in this biomechanical loading setup. Short stems implanted with a low filling ratio have an increased risk for implant tilt and subsidence compared to high filling ratios or standard stems.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"10 12","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11727762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142898991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Diagnosis Algorithm for Alzheimer's Disease.","authors":"Zhenhao Jin, Junjie Gong, Minghui Deng, Piaoyi Zheng, Guiping Li","doi":"10.3390/jimaging10120333","DOIUrl":"10.3390/jimaging10120333","url":null,"abstract":"<p><p>Alzheimer's disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images. In the segmentation network, the backbone network was simplified, the activation function and loss function were replaced, and the 3D GAM attention mechanism was introduced. In the classification network, firstly, the CA attention mechanism was added to enhance the model's ability to capture positional information of disease features; secondly, dilated convolutions were introduced to extract richer features from the input feature maps; and finally, the fully connected layer of MobileNetV3 was modified and the idea of transfer learning was adopted to improve the model's feature extraction capability. The results of the study showed that the proposed approach achieved classification accuracies of 97.85% for AD/NC, 95.31% for MCI/NC, 93.96% for AD/MCI, and 92.63% for AD/MCI/NC, respectively, which were 3.1, 2.8, 2.6, and 2.8 percentage points higher than before the improvement. Comparative and ablation experiments have validated the proposed classification performance of this method, demonstrating its capability to facilitate an accurate and efficient automated auxiliary diagnosis of AD, offering a deep learning-based solution for it.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"10 12","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11728444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images.","authors":"Parvaneh Aliniya, Mircea Nicolescu, Monica Nicolescu, George Bebis","doi":"10.3390/jimaging10120331","DOIUrl":"10.3390/jimaging10120331","url":null,"abstract":"<p><p>Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the development of supervised methods and the utilization of deep learning. Training deep learning-based models using segmentation masks will also be a powerful tool for removing pectoral muscle for unseen data. To test the validity of this idea, we trained AU-Net separately on the INbreast and CBIS-DDSM for the segmentation of the pectoral muscle. We used cross-dataset testing to evaluate the performance of the models on an unseen dataset. In addition, the models were tested on all of the images in the MIAS dataset. The experimental results show that cross-dataset testing achieves a comparable performance to the same-dataset experiments.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"10 12","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11727873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142898888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}