1
|
Zheng L, Jiang P, Lin D, Chen X, Zhong T, Zhang R, Chen J, Song Y, Xue Y, Lin L. Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas. Cancer Imaging 2023; 23:117. [PMID: 38053183 DOI: 10.1186/s40644-023-00633-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 11/03/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. METHODS Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. RESULTS DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). CONCLUSION Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.
Collapse
Affiliation(s)
- Lingmin Zheng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Peirong Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Danjie Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaodan Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Tianjin Zhong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Rufei Zhang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jing Chen
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yang Song
- MR Scientific Marketing, Healthineers Ltd, Siemens, Shanghai, China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350004, China.
| |
Collapse
|
2
|
Supsupin EP, Gonzales NS, Debnam JM. Anatomy and Pathology of the Skull Base: Malignant and Nonmalignant Lesions. Oral Maxillofac Surg Clin North Am 2023:S1042-3699(23)00025-0. [PMID: 37142448 DOI: 10.1016/j.coms.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The skull base (SB) is the osseous foundation of the cranial vault. It contains many openings that allow communication between the extracranial and intracranial structures. This communication is crucial in normal physiologic processes yet may also arrow spread of disease. This article provides a comprehensive review of SB anatomy including important landmarks and anatomic variants relevant to SB surgery. We also illustrate the diverse pathologies affecting the SB.
Collapse
Affiliation(s)
- Emilio P Supsupin
- Radiology/Neuroradiology, Radiology Residency Program, University of Florida College of Medicine, 655 West 8th. Street, Jacksonville, FL 32209, USA.
| | - Noelani S Gonzales
- Nova Southeastern University, Dr. Kiran C. Patel College of Osteopathic Medicine, Fort Lauderdale, FL 33314, USA
| | - James Matthew Debnam
- Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
3
|
Jiang YY, Zhong ZL, Zuo M. Three-dimensional arterial spin labeling and diffusion kurtosis imaging in evaluating perfusion and infarct area size in acute cerebral ischemia. World J Clin Cases 2022; 10:5586-5594. [PMID: 35979093 PMCID: PMC9258361 DOI: 10.12998/wjcc.v10.i17.5586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 03/01/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Early thrombolytic therapy is crucial to treat acute cerebral infarction, especially since the onset of thrombolytic therapy takes 1-6 h. Therefore, early diagnosis and evaluation of cerebral infarction is important.
AIM To investigate the diagnostic value of magnetic resonance multi-delay three-dimensional arterial spin labeling (3DASL) and diffusion kurtosis imaging (DKI) in evaluating the perfusion and infarct area size in patients with acute cerebral ischemia.
METHODS Eighty-four patients who experienced acute cerebral ischemia from March 2019 to February 2021 were included. All patients in the acute stage underwent magnetic resonance-based examination, and the data were processed by the system’s own software. The apparent diffusion coefficient (ADC), average diffusion coefficient (MD), axial diffusion (AD), radial diffusion (RD), average kurtosis (MK), radial kurtosis (fairly RK), axial kurtosis (AK), and perfusion parameters post-labeling delays (PLD) in the focal area and its corresponding area were compared. The correlation between the lesion area of cerebral infarction under MK and MD and T2-weighted imaging (T2WI) was analyzed.
RESULTS The DKI parameters of focal and control areas in the study subjects were compared. The ADC, MD, AD, and RD values in the lesion area were significantly lower than those in the control area. The MK, RK, and AK values in the lesion area were significantly higher than those in the control area. The MK/MD value in the infarct lesions was used to determine the matching situation. MK/MD < 5 mm was considered matching and MK/MD ≥ 5 mm was considered mismatching. PLD1.5s and PLD2.5s perfusion parameters in the central, peripheral, and control areas of the infarct lesions in MK/MD-matched and -unmatched patients were not significantly different. PLD1.5s and PLD2.5s perfusion parameter values in the central area of the infarct lesions in MK/MD-matched and -unmatched patients were significantly lower than those in peripheral and control areas. The MK and MD maps showed a lesion area of 20.08 ± 5.74 cm2 and 22.09 ± 5.58 cm2, respectively. T2WI showed a lesion area of 19.76 ± 5.02 cm2. There were no significant differences in the cerebral infarction lesion areas measured using the three methods. MK, MD, and T2WI showed a good correlation.
CONCLUSION DKI parameters showed significant difference between the focal and control areas in patients with acute ischemic cerebral infarction. 3DASL can effectively determine the changes in perfusion levels in the lesion area. There was a high correlation between the area of the infarct lesions diagnosed by DKI and T2WI.
Collapse
Affiliation(s)
- Yan-Yan Jiang
- Department of Magnetic Resonance, Wuhan Asia General Hospital, Wuhan 430056, Hubei Province, China
| | - Zhi-Lin Zhong
- Department of Radiology, Wuhan Yaxin General Hospital, Wuhan 430056, Hubei Province, China
| | - Min Zuo
- Department of Radiology, Wuhan Hanyang Hospital, Wuhan University of Science and Technology, Wuhan 430050, Hubei Province, China
| |
Collapse
|
4
|
Wang DJJ, Le Bihan D, Krishnamurthy R, Smith M, Ho ML. Noncontrast Pediatric Brain Perfusion: Arterial Spin Labeling and Intravoxel Incoherent Motion. Magn Reson Imaging Clin N Am 2021; 29:493-513. [PMID: 34717841 DOI: 10.1016/j.mric.2021.06.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Noncontrast magnetic resonance imaging techniques for measuring brain perfusion include arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM). These techniques provide noninvasive and repeatable assessment of cerebral blood flow or cerebral blood volume without the need for intravenous contrast. This article discusses the technical aspects of ASL and IVIM with a focus on normal physiologic variations, technical parameters, and artifacts. Multiple pediatric clinical applications are presented, including tumors, stroke, vasculopathy, vascular malformations, epilepsy, migraine, trauma, and inflammation.
Collapse
Affiliation(s)
- Danny J J Wang
- USC Institute for Neuroimaging and Informatics, SHN, 2025 Zonal Avenue, Health Sciences Campus, Los Angeles, CA 90033, USA
| | - Denis Le Bihan
- NeuroSpin, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Ram Krishnamurthy
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive - ED4, Columbus, OH 43205, USA
| | - Mark Smith
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive - ED4, Columbus, OH 43205, USA
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive - ED4, Columbus, OH 43205, USA.
| |
Collapse
|
5
|
Abstract
: Malignant triton tumor (MTT) is a rare, aggressive type of tumor that commonly originates from mesenchymal tissue and is mainly found in adults. Herein, we report a single case of a 12-year-old boy diagnosed with intracranial MTT. This report presents the clinical features and imaging findings of MTT. The 12-year-old patient consulted the hospital due to intermittent dizziness and headache. Computed tomography (CT) showed low-density space-occupying lesion in the left parietal fossa. Magnetic resonance imaging (MRI) showed a mass shadow with slightly long T1 and long T2 signal intensity in the same area. Contrast enhanced MRI (CE-MRI) showed obvious enhancement of the lesion. He was diagnosed with meningioma and underwent surgery. Postoperative histopathological examination diagnosed the lesion as MTT. Two months after the operation, CT examination showed tumor recurrence. He then underwent local radiotherapy and chemotherapy, and unfortunately, died six months later. Intracranial MTT should be considered as one of the differential diagnoses of intracranial meningiomas. CT and MRI are of great significance in the identification of lesion location, invasion range, and degree of malignancy. Final diagnosis of intracranial MTT requires histopathological examination. MTT has a poor prognosis, and surgical resection of the tumor is the preferred treatment. Intervention after early diagnosis is the key to improve the outcome of patients.
Collapse
|
6
|
Sun Z, Hu S, Ge Y, Jin L, Huang J, Dou W. Can Arterial Spin Labeling Perfusion Imaging be Used to Differentiate Nasopharyngeal Carcinoma From Nasopharyngeal Lymphoma? J Magn Reson Imaging 2020; 53:1140-1148. [PMID: 33225524 DOI: 10.1002/jmri.27451] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Differentiating nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoma (NPL) is useful for deciding the appropriate treatment. However, the diagnostic accuracy of current imaging methods is low. PURPOSE To explore the feasibility of arterial spin labeling (ASL) perfusion imaging in the qualitative and quantitative differentiation between NPC and NPL to improve the diagnosis of malignancies in the nasopharynx. STUDY TYPE Retrospective. POPULATION Ninety seven patients: NPC (65 cases) and NPL (32 cases), histologically confirmed. FIELD STRENGTH/SEQUENCE 3T/3D fast spin echo pseudo-continuous ASL imaging with spiral readout scheme, 3D inverse recovery- fast spoiled gradient recalled echo brain volume (BRAVO) imaging. ASSESSMENT Cerebral blood flow (CBF) images from ASL perfusion imaging were assessed by three radiologists. Each tumor was visually scored based on CBF images. Intratumoral CBF and intramuscular CBF values were obtained from intratumoral and lateral pterygoid muscle areas, respectively. Through dividing intratumoral CBF by intramuscular CBF, normalized CBF (nCBF) was further calculated. STATISTICAL TESTS Fleiss's kappa and intraclass correlation coefficients (ICCs) were used to assess interobserver agreement among the three readers. The Mann-Whitney U-test was used to compare visual scoring, and an unpaired t-test was performed to compare CBF value between the NPC and NPL groups. The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter. RESULTS Good interobserver agreements were validated by high Fleiss's kappa and ICC values (all >0.80). NPCs showed significantly higher visual scores than NPLs (P < 0.05). Both intratumoral CBF and nCBF in NPC were significantly higher than those in NPL (both P < 0.05). Intratumoral CBF showed the highest AUC of 0.861 (P < 0.05) in differentiating NPC (n = 65) from NPL (n = 32), while the AUCs of nCBF and visual scoring were 0.847 and 0.753, respectively. DATA CONCLUSION For the diagnosis of distinguishing NPC from NPL, ASL perfusion imaging demonstrated high diagnostic efficiency. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
Collapse
Affiliation(s)
- Zongqiong Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, China
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, China
| | - Linfang Jin
- Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi City, China
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi City, China
| | | |
Collapse
|
7
|
Cebeci H, Kilincer A, Duran Hİ, Seher N, Şahinoğlu M, Karabağlı H, Karabağlı P, Paksoy Y. Precise discrimination between meningiomas and schwannomas using time-to-signal intensity curves and percentage signal recoveries obtained from dynamic susceptibility perfusion imaging. J Neuroradiol 2020; 48:157-163. [PMID: 33065198 DOI: 10.1016/j.neurad.2020.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/22/2020] [Accepted: 09/29/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND AND PURPOSE Meningiomas and schwannomas are common extra-axial brain tumors. Discrimination is challenging in some locations when characteristic imaging features are absent. This study investigated the accuracy of percentage signal recoveries obtained from dynamic susceptibility contrast perfusion imaging (DSC-PI) in discriminating meningiomas and schwannomas. MATERIAL AND METHODS Retrospective database research was conducted. Sixty nine meningioma and 15 schwannoma having DSC-PI between January 2016 and February 2020 were included. Time to signal intensity curves (TSIC) were analyzed and grouped as T1-dominant leakage, T2*-dominant leakage and return to baseline. Relative cerebral blood volume (rCBV), relative mean transit time (rMTT), percentage signal recovery 1 (PSR 1) and PSR 2 values were calculated. The differences between the groups were investigated. Receiver operating characteristic curves were operated. RESULTS rCBV, rMTT, PSR 1 and PSR 2 values were statistically different between meningiomas and schwannomas. PSR 2 provided the best discrimination. With the cut off value of 1.08 for PSR 2, meningiomas and schwannomas were differentiated with 95.7% sensitivity and 93.3% specificity. TSICs were also different between two groups. Most of meningiomas showed T2*-dominant leakage (78.2%), whereas most of shwannomas showed T1-dominant leakage (93.3%). CONCLUSION DSC-PI is a useful imaging tool for non-invasive discrimination of meningiomas and schwannomas. Particularly, percentage signal recoveries discriminates meningiomas and schwannomas with high sensitivity and specificity.
Collapse
Affiliation(s)
- Hakan Cebeci
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey.
| | - Abidin Kilincer
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Halil İbrahim Duran
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Nusret Seher
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Mert Şahinoğlu
- Department of Neurosurgery, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Hakan Karabağlı
- Department of Neurosurgery, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Pınar Karabağlı
- Department of Pathology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Yahya Paksoy
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey; Department of Neuroradiology, Hamad Medical Corporation Neuroscience Institute, Doha, Qatar
| |
Collapse
|
8
|
Neromyliotis E, Kalamatianos T, Paschalis A, Komaitis S, Fountas KN, Kapsalaki EZ, Stranjalis G, Tsougos I. Machine Learning in Meningioma MRI: Past to Present. A Narrative Review. J Magn Reson Imaging 2020; 55:48-60. [PMID: 33006425 DOI: 10.1002/jmri.27378] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/10/2020] [Accepted: 09/10/2020] [Indexed: 12/28/2022] Open
Abstract
Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
Collapse
Affiliation(s)
- Eleftherios Neromyliotis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Theodosis Kalamatianos
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Paschalis
- Department of Neurosurgery, School of Medicine, University of Thessaly, Larisa, Greece
| | - Spyridon Komaitis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos N Fountas
- Department of Clinical and Laboratory Research, School of Medicine, University of Thessaly, Larisa, Greece
| | - Eftychia Z Kapsalaki
- Department of Clinical and Laboratory Research, School of Medicine, University of Thessaly, Larisa, Greece
| | - George Stranjalis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Tsougos
- Department of Medical Physics, School of Medicine, University of Thessaly, Larisa, Greece
| |
Collapse
|
9
|
Chen X, Lin L, Wu J, Yang G, Zhong T, Du X, Chen Z, Xu G, Song Y, Xue Y, Duan Q. Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging. Acta Radiol 2020; 61:1228-1239. [PMID: 31986895 DOI: 10.1177/0284185119898656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. PURPOSE To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. MATERIAL AND METHODS A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann-Whitney U test, Kruskal-Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. RESULTS Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index (P < 0.05). CONCLUSION The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.
Collapse
Affiliation(s)
- Xiaodan Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, PR China
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Jie Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Tianjin Zhong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Xiaoqiang Du
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Zhiyong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Ganggang Xu
- Department of Management Science, University of Miami, Coral Gables, FL, USA
| | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Qing Duan
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| |
Collapse
|