Biomedical Physics & Engineering Express最新文献

{"title":"Quantifying radiotherapy beam quality: an analysis using gamma passing rates.","authors":"Xiang Gao, Yipeng He, Yanjuan Yu, Sijia Chen, Guanglu Gao, Lirong Fu, Liwan Shi, Zheng Kang","doi":"10.1088/2057-1976/adb291","DOIUrl":"10.1088/2057-1976/adb291","url":null,"abstract":"<p><p><i>Purpose</i>. PDD and profile curves play a crucial role in analyzing the beam quality and energy stability of accelerators. The aim of this study was to assess the efficacy of GPR in machine QA and compare it with traditional methods for analyzing dose outputs.<i>Methods</i>. GPRs were employed to assess the quality of radiation beams by comparing 1D and 2D Profile metrics and PDD data against commissioning data. The data used were obtained from the ASCII data files derived from the water tank. GPRs were calculated for all plots with a lower percentage dose cutoff of 10%. The local GPRs and dose influence for the 2D PDD metrics and dose influence were calculated for an open field 10 × 10 cm²photon beam at SSD = 100 cm. In both 1D and 2D GPRs analyses, criterion of 1%/1 mm was adopted, as this approach allows for the capture of more subtle variations in the data. To substantiate the viability of the study, a comparative analysis was conducted by comparing the outcomes of the gamma analysis with those derived from traditional methods, such as manual machine quality assurance checks.<i>Results</i>. GPRs demonstrated a superior capability for comprehensive data analysis compared to traditional methods. For the 1D curves, the passing rates (<i>γ</i>≤ 1) are 96.19%, 100%, and 93.46%, respectively. With respect to the 2D dose influence, the PDD image passing rate was 99.57%, and significant dose differences were observed at the four corners of the open field, indicating areas that require further investigation.<i>Conclusions</i>. Compared to traditional methods, GPRs are more sensitive to subtle changes in the data, providing valuable insights into the accelerator beam status.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a 3D printed bolus for radiotherapy: a proof-of-concept study.","authors":"A C Ciobanu, L C Petcu, F Járai-Szabó, Z Bálint","doi":"10.1088/2057-1976/adb15d","DOIUrl":"10.1088/2057-1976/adb15d","url":null,"abstract":"<p><p>3D-printed boluses in radiation therapy are of increasing interest for enhancing treatment precision and patient comfort. A comprehensive clinical validation of these boluses remains to be established. This study aims to confirm the effectiveness of a 3D-printed bolus through a proof-of-concept comparative validation, by implementing in a clinical setting a bolus made of PLA and designed to ensure uniform dose coverage for a case in the eye region. In this study the 3D-printed bolus was compared to two commercially available boluses (one thermoplastic and one skin type) by using a refecence where no bolus was present (with the optimal dose distribution scenario). All boluses were placed on an anthropomorphic head phantom and BeOSL detectors were used to measure dose values to determine the level of their effectiveness on delivery. During the scanning process, a thermoplastic mask was used to prevent bolus movement and to accurately reproduce clinical scenarios. Differences in dose values at D_maxand D_50%revealed the performance of each bolus. The treatment planning system (TPS) and BeOSL readings for the 3D printed bolus were within 2% (the clinical tolerance), with 0.66% dose difference for the customized 3D-printed bolus. Although the thermoplastic bolus had the closest value to the detector reading, with a score of 0.30%, this result was influenced by improper shaping of the bolus on the phantom and the presence of a wide air gap, which caused lack of eye covering. Whereas, the skin bolus, due to higher volume of air between phantom surface and bolus, showed a 1.29% dose difference between the TPS values and the OSL detector readings. We provide a comparative validation for the use of 3D printed boluses and highlight that proper bolus fitting is essential in clinical settings to avoid air gaps and to maintain dose distribution over multiple treatment sessions.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monolithic U-shaped crystal design for TOF-DOI detectors: a flat top versus a tapered top.","authors":"Miho Kiyokawa, Han Gyu Kang, Taiga Yamaya","doi":"10.1088/2057-1976/adaced","DOIUrl":"10.1088/2057-1976/adaced","url":null,"abstract":"<p><p>For brain-dedicated positron emission tomography (PET) scanners, depth-of-interaction (DOI) information is essential to achieve uniform spatial resolution across the field-of-view (FOV) by minimizing parallax error. Time-of-flight (TOF) information can enhance the image quality. In this study, we proposed a novel monolithic U-shaped crystal design that had a tapered geometry to achieve good coincidence timing resolution (CTR) and DOI resolution simultaneously. We compared a novel tapered U-shaped crystal design with a conventional flat-top geometry for PET detectors. Each crystal had outer dimensions of 5.85 × 2.75 × 15 mm³, with a 0.2 mm central gap forming physically isolated bottom surfaces (2.85 × 2.75 mm²). The novel U-shape crystal design with a tapered top roof resulted in the best CTR of 201 ± 3 ps, and DOI resolution of 3.1 ± 0.6 mm, which were better than flat top geometry. In the next study, we plan to optimize the crystal surface treatment and reflector to further improve the CTR and DOI resolution.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143021724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of attenuation characteristics for novel neonatal head phantom in diagnostic radiology using Monte Carlo simulations and experiments.","authors":"Hamza Sekkat, Khallouqi Abdellah, Omar El Rhazouani, Youssef Madkouri, Abdellah Halimi","doi":"10.1088/2057-1976/adb15c","DOIUrl":"10.1088/2057-1976/adb15c","url":null,"abstract":"<p><p>This study presents the design and validation of a neonatal head phantom using innovative heterogeneous composite materials customized to replicate the x-ray attenuation properties of neonatal cranial structures. Analysis of Hounsfield Unit (HU) data from 338 neonatal head CT scans informed the design of epoxy resin-based composites with additives such as sodium bicarbonate, fumed silica, and acetone to simulate bone, brain matter, cerebrospinal fluid (CSF) and hyperdense abnormalities. The cranial bone substitute (60% epoxy resin, 40% sodium bicarbonate) achieved a density of 1.60 g cm^-3, with HU values (574.67-608.04) closely matching clinical ranges. Brain matter (95% epoxy resin, 5% acetone) achieved HU values (35.27-43.61), aligning with clinical means, while the CSF-equivalent material (80% epoxy resin, 15% fumed silica, 5% acetone) matched neonatal CSF HU values (14.53-17.02). A mass substitute for hyperdense abnormalities exhibited HU values (56.16-61.07), enabling differentiation from normal brain. Validation included Monte Carlo simulations and experimental CT imaging, showing close agreement in linear attenuation coefficients, with deviations below 11% across energy levels. Mass attenuation coefficients from simulations and XCOM software were consistent, with deviations under 0.7%, confirming the materials dosimetric reliability. The phantom, with a cylindrical geometry (9 cm diameter, 10 cm length), provides accurate attenuation properties across 80-120 kVp energy levels, with deviations below 5% between experimental CT numbers and simulation data. This phantom offers a robust platform for neonatal imaging research, enabling impactful dose optimization and imaging protocol adjustment and supports improved diagnostic accuracy in pediatric imaging.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring spatial dose information in the parotid gland for xerostomia prediction and local dose patterns in head and neck cancer radiotherapy.","authors":"Ming Chao, Lewis Tomalin, Jie Wei, Tian Liu, Jiahan Zhang, Jerry Liu, José A Peñagarícano","doi":"10.1088/2057-1976/adb15e","DOIUrl":"10.1088/2057-1976/adb15e","url":null,"abstract":"<p><p><i>Purpose</i>. To investigate the relationship between spatial parotid dose and the risk of xerostomia in patients undergoing head-and-neck cancer radiotherapy, using machine learning (ML) methods.<i>Methods</i>. Prior to conducting voxel-based ML analysis of the spatial dose, two steps were taken: (1) The parotid dose was standardized through deformable image registration to a reference patient; (2) Bilateral parotid doses were regrouped into contralateral and ipsilateral portions depending on their proximity to the gross tumor target. Individual dose voxels were input into six commonly used ML models, which were tuned with ten-fold cross validation: random forest (RF), ridge regression (RR), support vector machine (SVM), extra trees (ET), k-nearest neighbor (kNN), and naïve Bayes (NB). Binary endpoints from 240 patients were used for model training and validation: 0 (N = 119) for xerostomia grades 0 or 1, and 1 (N = 121) for grades 2 or higher. Model performance was evaluated using multiple metrics, including accuracy, F₁score, areas under the receiver operating characteristics curves (auROC), and area under the precision-recall curves (auPRC). Dose voxel importance was assessed to identify local dose patterns associated with xerostomia risk.<i>Results</i>. Four models, including RF, SVM, ET, and NB, yielded average auROCs and auPRCs greater than 0.60 from ten-fold cross-validation on the training data, except for a lower auROC from NB. The first three models, along with kNN, demonstrated higher accuracy and F₁scores. A bootstrapping analysis confirmed test uncertainty. Voxel importance analysis from kNN indicated that the posterior portion of the ipsilateral gland was more predictive of xerostomia, but no clear patterns were identified from the other models.<i>Conclusion</i>. Voxel doses as predictors of xerostomia were confirmed with some ML classifiers, but no clear regional patterns could be established among these classifiers, except kNN. Further research with a larger patient dataset is needed to identify conclusive patterns.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalogram features reflect effort corresponding to graded finger extension: implications for hemiparetic stroke.","authors":"Chase Haddix, Madison Bates, Sarah Garcia-Pava, Elizabeth Salmon Powell, Lumy Sawaki, Sridhar Sunderam","doi":"10.1088/2057-1976/adabeb","DOIUrl":"10.1088/2057-1976/adabeb","url":null,"abstract":"<p><p>Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or 'no-go' (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on EEG features, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n = 11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n = 3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning aided determination of the optimal number of detectors for photoacoustic tomography.","authors":"Sudeep Mondal, Subhadip Paul, Navjot Singh, Pankaj Warbal, Zartab Khanam, Ratan K Saha","doi":"10.1088/2057-1976/adaf29","DOIUrl":"10.1088/2057-1976/adaf29","url":null,"abstract":"<p><p>Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology suffers from significant streak artifacts and low-quality images. The integration of deep learning (DL), specifically convolutional neural networks (CNNs), has recently demonstrated powerful performance in various fields of PAT. This work introduces a post-processing-based CNN architecture named residual-dense UNet (RDUNet) to address the stride artifacts in reconstructed PA images. The framework adopts the benefits of residual and dense blocks to form high-resolution reconstructed images. The network is trained with two different types of datasets to learn the relationship between the reconstructed images and their corresponding ground truths (GTs). In the first protocol, RDUNet (identified as RDUNet I) underwent training on heterogeneous simulated images featuring three distinct phantom types. Subsequently, in the second protocol, RDUNet (referred to as RDUNet II) was trained on a heterogeneous composition of 81% simulated data and 19% experimental data. The motivation behind this is to allow the network to adapt to diverse experimental challenges. The RDUNet algorithm was validated by performing numerical and experimental studies involving single-disk, T-shape, and vasculature phantoms. The performance of this protocol was compared with the famous backprojection (BP) and the traditional UNet algorithms. This study shows that RDUNet can substantially reduce the number of detectors from 100 to 25 for simulated testing images and 30 for experimental scenarios.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143057812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated detection of traumatic bleeding in CT images using 3D U-Net# and multi-organ segmentation.","authors":"Rizki Nurfauzi, Ayaka Baba, Taka-Aki Nakada, Toshiya Nakaguchi, Yukihiro Nomura","doi":"10.1088/2057-1976/adae14","DOIUrl":"10.1088/2057-1976/adae14","url":null,"abstract":"<p><p>Traumatic injury remains a leading cause of death worldwide, with traumatic bleeding being one of its most critical and fatal consequences. The use of whole-body computed tomography (WBCT) in trauma management has rapidly expanded. However, interpreting WBCT images within the limited time available before treatment is particularly challenging for acute care physicians. Our group has previously developed an automated bleeding detection method in WBCT images. However, further reduction of false positives (FPs) is necessary for clinical application. To address this issue, we propose a novel automated detection for traumatic bleeding in CT images using deep learning and multi-organ segmentation; Methods: The proposed method integrates a three-dimensional U-Net# model for bleeding detection with an FP reduction approach based on multi-organ segmentation. The multi-organ segmentation method targets the bone, kidney, and vascular regions, where FPs are primarily found during the bleeding detection process. We evaluated the proposed method using a dataset of delayed-phase contrast-enhanced trauma CT images collected from four institutions; Results: Our method detected 70.0% of bleedings with 76.2 FPs/case. The processing time for our method was 6.3 ± 1.4 min. Compared with our previous ap-proach, the proposed method significantly reduced the number of FPs while maintaining detection sensitivity.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143032209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of different machine learning classifiers in predicting xerostomia and sticky saliva due to head and neck radiotherapy using a multi-objective, multimodal radiomics model.","authors":"Benyamin Khajetash, Ghasem Hajianfar, Amin Talebi, Beth Ghavidel, Seied Rabi Mahdavi, Yang Lei, Meysam Tavakoli","doi":"10.1088/2057-1976/adafac","DOIUrl":"10.1088/2057-1976/adafac","url":null,"abstract":"<p><p><i>Background and Purpose</i>. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stages HNC patients using CT and MRI image features along with demographics and dosimetric information.<i>Materials and Methods.</i>A cohort of 85 HNC patients who underwent radiation treatment was evaluated. We built different ML-based classifiers to build a multi-objective, multimodal radiomics model by extracting 346 different features from patient data. The models were trained and tested for prediction, utilizing Relief feature selection method and eight classifiers consisting eXtreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Logistic Regression (LR), and Decision Tree (DT). The performance of the models was evaluated using sensitivity, specificity, area under the curve (AUC), and accuracy metrics.<i>Results.</i>Using a combination of demographics, dosimetric, and image features, the SVM model obtained the best performance with AUC of 0.77 and 0.81 for predicting early sticky saliva and xerostomia, respectively. Also, SVM and MLP classifiers achieved a noteworthy AUC of 0.85 and 0.64 for predicting late sticky saliva and xerostomia, respectively.<i>Conclusion</i>. This study highlights the potential of baseline CT and MRI image features, combined with dosimetric data and patient demographics, to predict radiation-induced xerostomia and sticky saliva. The use of ML techniques provides valuable insights for personalized treatment planning to mitigate toxicity effects during radiation therapy for HNC patients.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Full fine-tuning strategy for endoscopic foundation models with expanded learnable offset parameters.","authors":"Minghan Dong, Xiangwei Zheng, Xia Zhang, Xingyu Zhang, Mingzhe Zhang","doi":"10.1088/2057-1976/adaec3","DOIUrl":"10.1088/2057-1976/adaec3","url":null,"abstract":"<p><p>In the medical field, endoscopic video analysis is crucial for disease diagnosis and minimally invasive surgery. The Endoscopic Foundation Models (Endo-FM) utilize large-scale self-supervised pre-training on endoscopic video data and leverage video transformer models to capture long-range spatiotemporal dependencies. However, detecting complex lesions such as gastrointestinal metaplasia (GIM) in endoscopic videos remains challenging due to unclear boundaries and indistinct features, and Endo-FM has not demonstrated good performance. To this end, we propose a fully fine-tuning strategy with an Extended Learnable Offset Parameter (ELOP), which improves model performance by introducing learnable offset parameters in the input space. Specifically, we propose a novel loss function that combines cross-entropy loss and focal loss through a weighted sum, enabling the model to better focus on hard-to-classify samples during training. We validated ELOP on a private GIM dataset from a local grade-A tertiary hospital and a public polyp detection dataset. Experimental results show that ELOP significantly improves the detection accuracy, achieving accuracy improvements of 6.25 % and 3.75%respectively compared to the original Endo-FM. In summary, ELOP provides an excellent solution for detecting complex lesions in endoscopic videos, achieving more precise diagnoses.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}