{"title":"Editorial for \"Simultaneous Visualization of the Carotid and Subclavian Arteries Using Non-Contrast-Enhanced MR Angiography With a 3D Fast Field Echo Sequence and Time-Spatial Labeling Inversion Pulse: A Comparison With 3D Time-of-Flight MR Angiography\".","authors":"Mitsue Miyazaki","doi":"10.1002/jmri.70116","DOIUrl":"https://doi.org/10.1002/jmri.70116","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martín Martínez, Mikel Ariz, Ignacio Alvarez, Tomás Muñoz Santoro, Gabriel Castellanos, Carlos Ortiz de Solórzano, Pau Pastor, Maria A Pastor
{"title":"Editorial for \"Hemispheric Asymmetry of Neuromelanin-Iron Dysfunction in the Substantia Nigra: MRI-Based Evidence for Lateralized Motor Onset in Early-Stage Parkinson's Disease\".","authors":"Martín Martínez, Mikel Ariz, Ignacio Alvarez, Tomás Muñoz Santoro, Gabriel Castellanos, Carlos Ortiz de Solórzano, Pau Pastor, Maria A Pastor","doi":"10.1002/jmri.70121","DOIUrl":"https://doi.org/10.1002/jmri.70121","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial for \"A Deep Learning-Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients\".","authors":"Sarv Priya, Prashant Nagpal","doi":"10.1002/jmri.70114","DOIUrl":"https://doi.org/10.1002/jmri.70114","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial for \"MR Passive Elastography: From Seismic Noise Tomography to Brain Tumors Characterization\".","authors":"Jianfeng Bao, Xiaoyue Ma, Yong Zhang","doi":"10.1002/jmri.70081","DOIUrl":"https://doi.org/10.1002/jmri.70081","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Simultaneous Visualization of the Carotid and Subclavian Arteries Using Non-Contrast-Enhanced MR Angiography With a 3D Fast Field Echo Sequence and Time-Spatial Labeling Inversion Pulse: A Comparison With 3D Time-of-Flight MR Angiography.","authors":"Hitomi Numamoto, Koji Fujimoto, Sachi Okuchi, Kanae K Miyake, Yasutaka Fushimi, Takakuni Maki, Yuichiro Monzen, Rimika Imai, Nobuyasu Ichinose, Yuji Nakamoto","doi":"10.1002/jmri.70112","DOIUrl":"https://doi.org/10.1002/jmri.70112","url":null,"abstract":"<p><strong>Background: </strong>Carotid artery stenosis is a major cause of stroke. Non-contrast MR angiography (MRA) using time-spatial labeling inversion pulse (Time-SLIP) may offer potential advantages over 3D time-of-flight (TOF)-MRA for simultaneous visualization of carotid, vertebral, and subclavian arteries, but remains uninvestigated.</p><p><strong>Purpose: </strong>To determine optimal black blood inversion time (TI) for visualizing the carotid and subclavian arteries using three-dimensional (3D) fast field echo (FFE) Time-SLIP MRA, and to compare its image quality with 3D TOF-MRA.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Subjects: </strong>11 healthy adults (23-57 years, five females) and 4 patients (76-93 years, three females) with cervical vascular abnormalities. All patients had ICA stenosis. One patient exhibited ECA stenosis.</p><p><strong>Field strength/sequence: </strong>3-T, 3D FFE with Time-SLIP using four TIs (1200-1800 ms) for healthy, 3D balanced steady-state free precession (bSSFP) with Time-SLIP using TI = 1600 for patients, and 3D TOF-MRA for all subjects, covering the cervical and subclavian arteries.</p><p><strong>Assessment: </strong>For healthy subjects, relative signal intensity (SI) was measured using ROI for both sides of the carotid, vertebral, and subclavian arteries. For the same locations, vessel visibility was independently scored by three board-certified radiologists. Patient images were qualitatively assessed for vessel visibility, abnormality, and artifacts.</p><p><strong>Statistical tests: </strong>Friedman and Wilcoxon signed-rank tests for pairwise comparisons of vessel visibility. A Bonferroni-corrected significance threshold of p < 0.005 (= 0.05/10) was used.</p><p><strong>Results: </strong>In volunteer scans, relative SI for TI = 1600 was highest, while TI = 1800 showed the best visibility scores. 3D TOF-MRA showed limitations in subclavian and brachiocephalic arteries due to low vessel-to-background contrast. In patients, FFE-based Time-SLIP provided better subclavian artery depiction than bSSFP-based Time-SLIP.</p><p><strong>Data conclusion: </strong>3D FFE with Time-SLIP may enable high-quality simultaneous visualization of the carotid and subclavian arteries when compared to 3D TOF-MRA. In patients with vascular abnormalities, 3D FFE may provide superior subclavian artery depiction compared to 3D TOF-MRA and 3D bSSFP.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial for \"Development of an MRI-Based Comprehensive Model Fusing Clinical, Habitat Radiomics, and Deep Learning Models for Preoperative Identification of Tumor Deposits in Rectal Cancer\".","authors":"Numan C Balci, Ahmad Raza, Shafik Sidani","doi":"10.1002/jmri.70076","DOIUrl":"https://doi.org/10.1002/jmri.70076","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MRI Assessment of Radiation-Induced Delayed-Onset Microstructural Gray Matter Changes in Nasopharyngeal Carcinoma Patients.","authors":"Ziru Qiu, Gui Fu, Yuhao Lin, Haoran Xie, Jiahui Liang, Jie Pan, YunPeng Li, Yanqiu Feng, Xiaofei Lv, Xinyuan Zhang","doi":"10.1002/jmri.70119","DOIUrl":"https://doi.org/10.1002/jmri.70119","url":null,"abstract":"<p><strong>Background: </strong>The dynamic progression of gray matter (GM) microstructural alterations following radiotherapy (RT) in patients, and the relationship between these microstructural abnormalities and cortical morphometric changes remains unclear.</p><p><strong>Purpose: </strong>To longitudinally characterize RT-related GM microstructural changes and assess their potential causal links with classic morphometric alterations in patients with nasopharyngeal carcinoma (NPC).</p><p><strong>Study type: </strong>Prospective, longitudinal.</p><p><strong>Population: </strong>Forty treatment-naïve patients with NPC (40.78 ± 9.15 years; 14 female) and 20 healthy controls (40.65 ± 9.76 years; 7 female).</p><p><strong>Field strength/sequences: </strong>3 T MRI with 3D T1-weighted gradient echo and multishell diffusion-weighted single-shot echo planar imaging.</p><p><strong>Assessment: </strong>Multishell diffusion and structural MRIs were acquired from NPC patients at baseline, 0-3 months (acute), 6 months (early-delayed), and 12 months (late-delayed) post-RT, with healthy controls imaged at baseline. Temporal lobe (TL) radiation doses were extracted from dose-volume histograms. GM-based spatial statistics and surface-based morphometry analyses were used to quantify microstructural and macrostructural changes, respectively. TL subregions were extracted from the Desikan-Killiany atlas for the region-of-interest (ROI) analysis.</p><p><strong>Statistical tests: </strong>Chi-squared tests, t-tests, repeated measures analysis of variance (ANOVA), Spearman correlation (r), and mediation analyses (p < 0.01 for voxel-wise analyses and p < 0.05 for others were considered significant).</p><p><strong>Results: </strong>GM microstructural changes in TL regions were mainly observed to emerge at 6 months and to persist or first appear at 12 months post-RT, representing a delayed-onset pattern. ROI analyses showed dose-dependent alterations in diffusion metrics within the entorhinal cortex (EC) and temporal pole (TP) (|r| = 0.31-0.66). Morphometric analysis demonstrated widespread TL atrophy. Mediation analysis showed delayed-onset changes in EC and TP that mediated macrostructural abnormalities in multiple TL regions, including left middle and superior temporal gyri and right inferior temporal and parahippocampal gyri.</p><p><strong>Data conclusions: </strong>This study showed delayed-onset, dose-sensitive microstructural changes in EC and TP that contribute to broader TL atrophy in NPC patients.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>Stage 4.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
XueLing Liu, Na Wang, MinJia Xie, LiQin Yang, Pu-Yeh Wu, FengTao Liu, YuXin Li
{"title":"Hemispheric Asymmetry of Neuromelanin-Iron Dysfunction in the Substantia Nigra: MRI-Based Evidence for Lateralized Motor Onset in Early-Stage Parkinson's Disease.","authors":"XueLing Liu, Na Wang, MinJia Xie, LiQin Yang, Pu-Yeh Wu, FengTao Liu, YuXin Li","doi":"10.1002/jmri.70110","DOIUrl":"https://doi.org/10.1002/jmri.70110","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's disease (PD) often presents with lateralized motor symptoms at onset, reflecting asymmetric degeneration of the substantia nigra (SN). Neuromelanin (NM) loss and iron accumulation are hallmarks of SN pathology in PD, but their spatial distribution and interrelationship in PD patients with right-sided (PDR) or left-sided (PDL) motor symptom onset remain unclear.</p><p><strong>Purpose: </strong>To investigate the spatial vulnerability and interrelationship of NM and iron in the SN among PDR, PDL, and healthy controls (HCs) using MRI.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Population: </strong>56 early-stage PD patients (28 PDR: 55.93 ± 7.41 years, 16 female/12 male; 28 PDL: 59.04 ± 10.44 years, 13 female/15 male) and 30 age- and gender-matched HCs (57.47 ± 4.07 years, 17 female/13 male).</p><p><strong>Field strength/sequence: </strong>3-T, T1-weighted Fast Spoiled Gradient Recalled (FSPGR) and gradient recalled echo (GRE) for quantitative susceptibility mapping (QSM).</p><p><strong>Assessment: </strong>Voxel-wise group comparisons of NM and iron were performed, and overlapping clusters with co-localized NM loss and iron deposition were identified. Mean cluster values were extracted for subsequent group comparison and NM-iron relationship modeling.</p><p><strong>Statistical tests: </strong>Welch's analysis of variance (ANOVA), Chi-squared test, Mann-Whitney U, voxel-wise analysis of covariance (ANCOVA), bidirectional mediation, partial correlation.</p><p><strong>Results: </strong>Cluster 1 (PDR vs. HC) in left nigrosome-1 showed concurrent NM reduction (1.14 ± 0.09 vs. 1.30 ± 0.06) and iron increase (0.14 ± 0.06 vs. 0.08 ± 0.04 ppm); Cluster 2 (PDL vs. HC) in right nigrosome-1 also showed concurrent NM reduction (1.15 ± 0.06 vs. 1.28 ± 0.07) and iron increase (0.16 ± 0.05 vs. 0.10 ± 0.04 ppm). Bidirectional mediation revealed significant NM-iron direct and indirect effects in PDR and PDL (vs. HCs).</p><p><strong>Data conclusion: </strong>This QSM-based study demonstrates contralateral nigrosome-1 as the vulnerable region with concurrent NM loss and iron accumulation and exhibits reciprocal mediation effects between NM and iron in early-stage PD.</p><p><strong>Level of evidence: 2: </strong></p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of an MRI-Based Comprehensive Model Fusing Clinical, Habitat Radiomics, and Deep Learning Models for Preoperative Identification of Tumor Deposits in Rectal Cancer.","authors":"Xiang Li, Ying Zhu, Yaru Wei, Zhongwei Chen, Zhishan Wang, Yanyan Li, Xuebo Jin, Ziyi Chen, Jiashan Zhan, Xiaobo Chen, Meihao Wang","doi":"10.1002/jmri.70075","DOIUrl":"https://doi.org/10.1002/jmri.70075","url":null,"abstract":"<p><strong>Background: </strong>Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored.</p><p><strong>Purpose: </strong>To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Surgically diagnosed rectal cancer patients (n = 635): training (n = 259) and internal validation (n = 112) from center 1; center 2 (n = 264) for external validation.</p><p><strong>Field strength/sequence: </strong>1.5/3T, T2-weighted image (T2WI) using fast spin echo sequence.</p><p><strong>Assessment: </strong>Four models (clinical, habitat radiomics, DL, fusion) were developed for preoperative TDs diagnosis (184 TDs positive). T2WI was segmented using nnUNet, and habitat radiomics and DL features were extracted separately. Clinical parameters were analyzed independently. The fusion model integrated selected features from all three approaches through two-stage selection. Disease-free survival (DFS) analysis was used to assess the models' prognostic performance.</p><p><strong>Statistical tests: </strong>Intraclass correlation coefficient (ICC), logistic regression, Mann-Whitney U tests, Chi-squared tests, LASSO, area under the curve (AUC), decision curve analysis (DCA), calibration curves, Kaplan-Meier analysis.</p><p><strong>Results: </strong>The AUCs for the four models ranged from 0.778 to 0.930 in the training set. In the internal validation cohort, the AUCs of clinical, habitat radiomics, DL, and fusion models were 0.785 (95% CI 0.767-0.803), 0.827 (95% CI 0.809-0.845), 0.828 (95% CI 0.815-0.841), and 0.862 (95% CI 0.828-0.896), respectively. In the external validation cohort, the corresponding AUCs were 0.711 (95% CI 0.599-0.644), 0.817 (95% CI 0.801-0.833), 0.759 (95% CI 0.743-0.773), and 0.820 (95% CI 0.770-0.860), respectively. TDs-positive patients predicted by the fusion model had significantly poorer DFS (median: 30.7 months) than TDs-negative patients (median follow-up period: 39.9 months).</p><p><strong>Data conclusion: </strong>A fusion model may identify TDs in rectal cancer and could allow to stratify DFS risk.</p><p><strong>Level of evidence: 3: </strong></p><p><strong>Technical efficacy stage: </strong>3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Timothy B Weng, Gargi Porwal, Dhivya Srinivasan, Ben Inglis, Sabrina Rodriguez, David R Jacobs, Pamela J Schreiner, Farzaneh A Sorond, Stephen Sidney, Cora Lewis, Lenore Launer, Guray Erus, Ilya M Nasrallah, R Nick Bryan, Adrienne N Dula
{"title":"Predicting Breath Hold Task Compliance From Head Motion.","authors":"Timothy B Weng, Gargi Porwal, Dhivya Srinivasan, Ben Inglis, Sabrina Rodriguez, David R Jacobs, Pamela J Schreiner, Farzaneh A Sorond, Stephen Sidney, Cora Lewis, Lenore Launer, Guray Erus, Ilya M Nasrallah, R Nick Bryan, Adrienne N Dula","doi":"10.1002/jmri.70105","DOIUrl":"https://doi.org/10.1002/jmri.70105","url":null,"abstract":"<p><strong>Background: </strong>Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.</p><p><strong>Purpose: </strong>To develop a non-invasive and data-driven quality filter for breath-hold compliance using only measurements of head motion during imaging.</p><p><strong>Study type: </strong>Prospective cohort.</p><p><strong>Participants: </strong>Longitudinal data from healthy middle-aged subjects enrolled in the Coronary Artery Risk Development in Young Adults Brain MRI Study, N = 1141, 47.1% female.</p><p><strong>Field strength/sequence: </strong>3.0 Tesla gradient-echo MRI.</p><p><strong>Assessment: </strong>Manual labelling of respiratory belt monitored data was used to determine breath hold compliance during MRI scan. A model to estimate the probability of non-compliance with the breath hold task was developed using measures of head motion. The model's ability to identify scans in which the participant was not performing the breath hold were summarized using performance metrics including sensitivity, specificity, recall, and F1 score. The model was applied to additional unmarked data to assess effects on population measures of CVR.</p><p><strong>Statistical tests: </strong>Sensitivity analysis revealed exclusion of non-compliant scans using the developed model did not affect median cerebrovascular reactivity (Median [q1, q3] = 1.32 [0.96, 1.71]) compared to using manual review of respiratory belt data (1.33 [1.02, 1.74]) while reducing interquartile range.</p><p><strong>Results: </strong>The final model based on a multi-layer perceptron machine learning classifier estimated non-compliance with an accuracy of 76.9% and an F1 score of 69.5%, indicating a moderate balance between precision and recall for the identification of scans in which the participant was not compliant.</p><p><strong>Data conclusion: </strong>The developed model provides the probability of non-compliance with a breath-hold task, which could later be used as a quality filter or included in statistical analyses.</p><p><strong>Level of evidence: 1: </strong>TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}