Biomedical Physics & Engineering Express最新文献

{"title":"Portal dose image prediction using Monte Carlo generated transmission energy fluence maps of dynamic radiotherapy treatment plans: a deep learning approach.","authors":"Peter Andersson, Magnus Båth, Åsa Palm, Roumiana Chakarova","doi":"10.1088/2057-1976/adc73f","DOIUrl":"10.1088/2057-1976/adc73f","url":null,"abstract":"<p><p><i>Aims.</i>This work aims to develop and investigate the feasibility of a hybrid model combining Monte Carlo (MC) simulations and deep learning (DL) to predict electronic portal imaging device (EPID) images based on MC-generated exit phase space energy fluence maps from dynamic radiotherapy treatment plans. Such predicted images can be used as reference images during<i>in vivo</i>dosimetry.<i>Materials and methods</i>. MC simulations involving a Varian True Beam linear accelerator model were performed using the EGSnrc code package. Two custom variants of the U-Net architecture were employed. The MLC dynamic chair sequence and 17 clinical treatment plans, spanning various cancer types and delivery methods, were used to acquire experimental data, and in the MC simulations. The proposed method was tested through 2D gamma index analysis, comparing predicted and measured EPID images.<i>Results</i>. Results showed gamma passing rates of 38.65%, 74.16% and 96.17% (minimum, median, maximum) for a simpler model variant and 52.72%, 80.61% and 96.80% for the more complex model variant.<i>Conclusion</i>. The study highlights the feasibility of integrating MC and DL methodologies for<i>in vivo</i>dosimetry quality assurance in complex radiotherapy delivery.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751006","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":"Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.","authors":"Shengxiu Jiao, Honghao Xu, Jia Luo, Lin Lei, Peng Zhou","doi":"10.1088/2057-1976/adc697","DOIUrl":"10.1088/2057-1976/adc697","url":null,"abstract":"<p><p><i>Purpose</i>. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that integrates CK beam dose characteristics into CK planning dose calculations.<i>Methods</i>. The inputs utilized for the geometry and dosimetry method (GDM) include the patient's CT, the PTV structure, and multiple CK noncoplanar beam dose deposition features. The dose distributions were calculated using the Monte Carlo (MC) algorithm provided with the CK system and served as the ground truth dose label. Additionally, dose prediction was conducted through the geometry method (GM) for comparative analysis. The gamma pass rate<i>γ</i>(1 mm,1%),<i>γ</i>(2 mm,2%) and<i>γ</i>(3 mm,3%) were calculated between the predicted model and the MC method.<i>Results</i>. Compared to the GDM, the GM shows a significant dose difference from the MC approach in the low-dose region (<5 Gy) outside the target created by the various CK noncoplanar beams. The GDM increased the<i>γ</i>(1 mm, 1%) from 49.55% to 81.69%,<i>γ</i>(2 mm, 2%) from 73.24% to 98.11% and the<i>γ</i>(3 mm, 3%) from 81.69% to 99.37% when compared with the GM's results.<i>Conclusions</i>. This work proposed a deep learning dose calculation method by using patient geometry and dosimetry features in CK plans. The proposed method extends the geometric and dosimetric feature-driven deep learning dose calculation method to CK application scenarios, which has a great potential to accelerate the CK planning dose calculation and improve the planning efficiency.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742101","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":"The Utilization of the k-means clustering for Cancer Cell Detection and Classification with Serous Effusion.","authors":"Safaa Al-Qaysi","doi":"10.1088/2057-1976/adca3e","DOIUrl":"https://doi.org/10.1088/2057-1976/adca3e","url":null,"abstract":"<p><p>Cytological analysis of serous effusion specimens is essential for cancer diagnosis. In this work, we analyzed three-dimensional (3D) morphologic features by clustering to discriminate between malignant and nonmalignant cells in serous effusion specimens collected from 10 patients with pleural and peritoneal effusion accumulation symptoms. After the nuclei and mitochondria were fluorescently labeled, we obtained confocal image stack data and conducted 3D reconstruction and morphological feature parameter computation. Confocal images were segmented, interpolated, and reconstructed. Quantitative comparison across cell types has been made by 27 morphological features of volume and surface linked to the cell, nucleus, and mitochondria. We used an unsupervised machine learning method of k-means clustering to separate the cell distribution objectively and effectively in the 3D parameter space of the cell morphology features. The statistical significance of the differences was examined on morphological features among the three cell clusters. The clustering results were also analyzed against those of cytopathological examinations performed by collaborative pathologist on specimens collected from the same patients. These results showed that 3D morphologic features allow clustering of the effusion cells in the space of these parameters and may help produce new ways to quickly profile cells for cancer diagnosis in clinical settings. By incorporating these techniques into clinical practice, healthcare professionals may be able to more effectively detect and treat cancers in patients.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810105","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":"Timing performance evaluation of a dual-Axis rotational PET system according to NEMA NU 4-2008 standards: A simulation study.","authors":"P M C C Encarnação, P M M Correia, F M Ribeiro, J F C A Veloso","doi":"10.1088/2057-1976/adc5f5","DOIUrl":"10.1088/2057-1976/adc5f5","url":null,"abstract":"<p><p><i>Introduction:</i>Positron Emission Tomography (PET) imaging's diagnostic accuracy is dependent on the scanner design and image quality, which is affected by several factors including the coincidence timing window (CTW). NEMA NU 4-2008 procedures are commonly used to assess and compare PET systems performance, including dual rotation technologies like easyPET.3D, known for high-spatial resolution and reduced parallax contribution.<i>Aim:</i>This study aims to identify easyPET.3D's optimal performance based on NEMA standards. In addition, explores the impact of different CTWs on PET image quality by comparing simulated electronics capable of a 300 ps CTW with a 40 ns CTW.<i>Results:</i>When the data is filtered by a 40 ns CTW, a sub-millimetre resolution at the field-of-view (FoV) centre and a constant behaviour in the radial direction are achieved. The absolute sensitivity was 0.18% with a maximum value of 0.31%, for a 15 mm transverse FoV. The noise equivalent count rate peaked at 18 MBq with 249 cps. Recovery coefficients ranged from 17% to 90%, and spilled-over ratios were 0.32 (water) and 0.41 (air).<i>Conclusions:</i>A shorter 300 ps CTW primarily impacted PET dynamic range, allowing higher activity acquisitions, with no significant changes in resolution and sensitivity under NEMA test conditions. As for the image quality test, the 300 ps CTW images have less background, better SOR values, and similar RC values when comparing the 40 ns CTW.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717891","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":"Optimized Glaucoma Detection Using HCCNN with PSO-Driven Hyperparameter Tuning.","authors":"Latha G, Aruna Priya P","doi":"10.1088/2057-1976/adc9b7","DOIUrl":"https://doi.org/10.1088/2057-1976/adc9b7","url":null,"abstract":"<p><strong>Purpose: </strong>&#xD;This study is focused on creating an effective glaucoma detection system employing a Hybrid Centric Convolutional Neural Network (HCCNN) model. By using Particle Swarm Optimization (PSO), classification accuracy is increased and computing complexity is reduced. Modified U-Net is also used to segment the optic disc (OD) and optic cup (OC) regions of classified glaucoma images in order to determine the severity of glaucoma.&#xD;Methods:&#xD;The proposed HCCNN model can extract features from fundus images that show signs of glaucoma. To improve the model performance, hyperparameters like dropout rate, learning rate, and the number of units in dense layer are optimized using the PSO method. The PSO algorithm iteratively assesses and modifies these parameters to minimise classification error.The classified glaucoma image is subjected to channel separation to enhance the visibility of relevant features. This channel separated image is segmented using the modified U-Net to delineate the OC and OD regions.&#xD;Results:&#xD;Experimental findings indicate that the PSO-HCCNN model attains classification accuracy of 94% and 97% on DRISHTI-GS and RIM-ONE datasets. Performance criteria including accuracy, sensitivity, specificity, and AUC are employed to assess the system's efficacy, demonstrating a notable enhancement in the early detection rates of glaucoma. To evaluate the segmentation performance, parameters such as Dice coefficient, and Jaccard index are computed.&#xD;Conclusion:&#xD;The integration of PSO with the HCCNN model considerably enhances glaucoma detection from fundus images by optimising essential parameters and accurate OD and OC segmentation, resulting in a robust and precise classification model. This method has potential uses in ophthalmology and may help physicians detect glaucoma early and accurately.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802265","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 deep learning approach for quantifying CT perfusion parameters in stroke.","authors":"Wanning Zeng, Yang Li, Jeff Lei Zhang, Tong Chen, Ke Wu, Xiaopeng Zong","doi":"10.1088/2057-1976/adc9b6","DOIUrl":"https://doi.org/10.1088/2057-1976/adc9b6","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residue function and underestimation of tissue perfusion. In addition, the use of global arterial input function (AIF) potentially leads to erroneous parameter estimates. We aim to develop a method for accurately estimating physiological parameters from CTP images.&#xD;Approach:&#xD;We introduced a Transformer-based network to learn voxel-wise temporal features of CTP images. With global AIF and concentration time curve (CTC) of brain tissue as inputs, the network estimated local AIF and flow-scaled residue function. The derived parameters, including cerebral blood flow (CBF) and bolus arrival delay (BAD), were validated on both simulated and patient data (ISLES18 dataset), and were compared with multiple SVD-based methods, including standard SVD (sSVD), block-circulant SVD (cSVD) and oscillation-index SVD (oSVD).&#xD;Main results:&#xD;On data simulating multiple scenarios, local AIF estimated by the proposed method correlated with true AIF with a coefficient of 0.97±0.04 (P<0.001), estimated CBF with a mean error of 4.95 ml/100 g/min, and estimated BAD with a mean error of 0.51 s; the latter two errors were significantly lower than those of the SVD-based methods (P<0.001). The CBF estimated by the SVD-based methods were underestimated by 10%~15%. For patient data, the CBF estimates of the proposed method were significantly higher than those of the sSVD method in both normally perfused and ischemic tissues, by 13.83 ml/100 g/min or 39.33% and 8.55 ml/100 g/min or 57.73% (P<0.001), respectively, which was in agreement with the simulation results.&#xD;Significance:&#xD;The proposed method is capable of estimating local AIF and perfusion parameters from CTP images with high accuracy, potentially improving CTP's performance and efficiency in diagnosing and treating ischemic stroke.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802262","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":"Reparameterization Lightweight Residual Network for Super-Resolution of Brain MR Images.","authors":"Yang Geng, Pingping Wang, Jinyu Cong, Xiang Li, Kunmeng Liu, Benzheng Wei","doi":"10.1088/2057-1976/adc935","DOIUrl":"https://doi.org/10.1088/2057-1976/adc935","url":null,"abstract":"<p><p>As the demand for high-resolution medical images increases, super-resolution (SR) technology becomes particularly important. In recent years, SR technology based on deep learning has achieved remarkable achievements, and its application in medical images is also growing. Since brain MRI is prone to artifacts during long-term scanning, SR technology has become an effective means to improve image clarity. However, traditional SR methods are usually computationally complex and time-consuming, making them unsuitable for real-time applications. To solve this problem, this paper proposes a lightweight SR model with BSRN as the backbone network and combined with structural re-parameterization to achieve lightweight and efficient SR. The model uses a multi-branch structure during training and integrates the multiple branches into a 3×3 convolution during inference, effectively retaining important feature information. At the same time, the computational complexity and storage requirements are significantly reduced. Through experimental verification on the IXI dataset, this method shows excellent super-resolution reconstruction effects, especially when processing noisy and blurred images, and can effectively improve image clarity and details. Research results show that this method improves model performance and has good application potential, providing new ideas for future medical image processing technology development.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787604","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":"Impact of asymmetric stenosis and heart rate on the left coronary artery hemodynamics in elderly patients: a 3D computational study.","authors":"Asif Equbal, Paragmoni Kalita, Farhin Iqbal","doi":"10.1088/2057-1976/adc45f","DOIUrl":"10.1088/2057-1976/adc45f","url":null,"abstract":"<p><p>The left coronary main (LCM) artery and its branches, particularly the left anterior descending (LAD) artery, are highly prone to atherosclerosis, especially as arterial stiffness increases with age. Irregularities in arterial geometry further contribute to the development of asymmetric plaques, underscoring the importance of three-dimensional (3D) hemodynamic studies, which remain limited in literature. Moreover, no existing research explores how hemodynamic variables change with different heart rates in the presence of asymmetric plaque in LAD, which is essential for assessing the disease severity and progression. To address this gap, our study conducts a 3D numerical analysis of the hemodynamic effects of heart rate (HR) and degree of stenosis (DOS) with asymmetric plaques in the LAD branch. The hemodynamic parameters - primary velocityVp, time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT) - are analyzed to correlate HR and DOS with disease progression and severity. Analysis based on all these hemodynamic variables reveals that the atheroprone regions on the outer lateral walls expand as the DOS increases for a given HR. Conversely, such regions shrink in size as the HR increases for fixed DOS. While the inner lateral walls are safe in terms of OSI and RRT, they remain atheroprone due to alarmingly low TAWSS, especially at 75% DOS. At 45% DOS, TAWSS exceeds the upper-critical limit of 15 Pa at 120 beats per minute (bpm), making the branch thrombosis-prone. At 60% and 75% DOS, the thrombosis threshold is crossed at 100 bpm and at 75 bpm, respectively. Based on the threshold values, TAWSS is found to be a more conservative marker for assessing cardiovascular risks associated with these plaques compared to OSI and RRT.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699227","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":"Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT generation model for liver radiotherapy.","authors":"Lamyaa Aljaafari, Richard Speight, David L Buckley, Bashar Al-Qaisieh, Sebastian Andersson, David Bird","doi":"10.1088/2057-1976/adc818","DOIUrl":"https://doi.org/10.1088/2057-1976/adc818","url":null,"abstract":"<p><strong>Background and purpose: </strong>&#xD;An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.&#xD;Methods and materials:&#xD;sCT images were generated for eleven patients using a CycleGAN algorithm. Clinical volumetric modulated arc treatment plans (VMAT) were calculated on CT and recalculated on sCT, and dose differences were assessed using dose volume histogram (DVH). For position verification, the sCT images were validated as reference images to 4D cone beam computed tomography (4D CBCT) by calculating the translational and rotational differences between sCT and CT registrations to 4D CBCT.&#xD;Results:&#xD;The mean dose differences for the planning target volume (PTV) and organs at risk (OAR) between the CT and sCT plans were 0.0% and < 0.5% respectively. For positioning verification, the systematic translational and rotational differences were < 0.5 mm and < 0.5° respectively in all directions&#xD;Conclusion:&#xD;This is the first study to validate a sCT model for liver cancer in terms of both dosimetry and patient positioning, marking a significant step in demonstrating the feasibility of an MRI-only workflow for treating liver cancer. The generated sCT showed dosimetric differences within clinically acceptable levels and were successfully used as reference images for treatment position verification. This CycleGAN model is accessible through the research version of a commercial vendor, with potential for development as a clinical solution.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771277","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":"Fabrication of micro-wire stent electrode as a minimally invasive endovascular neural interface for vascular electrocorticography using laser ablation method.","authors":"Bo Wen, Lu Su, Yuan Zhang, Aiping Wang, Hongchen Zhao, Jianjun Wu, Zhongxue Gan, Lihua Zhang, Xiaoyang Kang","doi":"10.1088/2057-1976/adc266","DOIUrl":"10.1088/2057-1976/adc266","url":null,"abstract":"<p><p><i>Objective</i>. Minimally invasive endovascular stent electrode is a currently emerging technology in neural engineering with minimal damage to the neural tissue. However, the typical stent electrode still requires resistive welding and is relatively large, limiting its application mainly on the large animal or thick vessels. In this study, we investigated the feasibility of laser ablation of micro-wire stent electrode with a diameter as small as 25μmand verified it in the superior sagittal sinus (SSS) of a rat.<i>Approach</i>. We have developed a laser ablation technology to expose the electrode sites of the micro-wire on both sides without damaging the wire itself. During laser ablation, we applied a new method to fix and realign the micro-wires. The micro-wire stent electrode was fabricated by carefully assemble the micro-wire and stent. We tested the electrochemical performances of the electrodes as a neural interface. Finally, we deployed the stent electrode in a rat to verified the feasibility.<i>Main result</i>. Based on the proposed micro-wire stent electrode, we demonstrated that the stent electrode could be successfully deployed in a rat. With the benefit of the smaller design and laser fabrication technology, it can be fitted into a catheter with an inner diameter of 0.6mm. The vascular electrocorticography can be detected during the acute recording, making it promising in the application of small animals and thin vessels.<i>Significance</i>. The method we proposed combines the advantages of endovascular micro-wire electrode and stent, helping make the electrodes smaller. This study provided an alternative method for deploying micro-wire electrodes into thinner vessels as an endovascular neural interface.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662345","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}