1
|
Huang P, Li H, Lin F, Lei M, Zhang M, Liu J, JunChen, Hou J, Xiao M. Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01027-8. [PMID: 39020151 DOI: 10.1007/s10278-024-01027-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/04/2024] [Accepted: 01/15/2024] [Indexed: 07/19/2024]
Abstract
The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of rib fractures (DLADRF). A total of 158 patients undergoing forensic diagnosis for rib fractures were included in this study: 50 underwent SD-CT, and 108 were assessed using ULD-CT. Junior and senior radiologists independently evaluated the images to identify and characterize the rib fractures. The sensitivity of rib fracture diagnosis by radiologists and radiologist + DLADRF was better using SD-CT than ULD-CT. However, the diagnosis sensitivity of DLADRF using ULD-CT alone was slightly more than SD-CT. Nonetheless, no substantial differences were observed in specificity, positive predictive value, and negative predictive value between SD-CT and ULD-CT by the same radiologist, radiologist + DLADRF, and DLADRF (P > 0.05). The area under the curve (AUC) of receiver operating characteristic indicated that senior radiologist + DLADRF was significantly better than senior and junior radiologists, junior radiologists + DLADRF, and DLADRF alone using SD-CT or ULD-CT (all P < 0.05). Also, junior radiologists + DLADRF was better with ULD-CT than senior and junior radiologists (P < 0.05). The AUC of the rib fracture diagnosed by senior radiologists did not differ from DLADRF using ULD-CT. Also, no significant differences were observed between junior + AI and senior and between junior and DLADRF using SD-CT. DLADRF enhanced the diagnostic performance of radiologists in detecting recent rib fractures. The diagnostic outcomes between SD-CT and ULD-CT across radiologists' experience and DLADRF did not differ significantly.
Collapse
Affiliation(s)
- Peikai Huang
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - Hongyi Li
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - Fenghuan Lin
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - Ming Lei
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - Meng Zhang
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - Jingfeng Liu
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - JunChen
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China
| | - Junfei Hou
- Guangdong Provincial People's Hospital Zhuhai Hospital, 2 Hongyang Road, Golden Bay Area, Zhuhai City, Guangdong Province, China
| | - Mengqiang Xiao
- Department of Radiology, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, 53 Jingle Road, Zhuhai City, Guangdong Province, China.
| |
Collapse
|
2
|
Jin L, Youjun E, Ye Z, Gao P, Wei G, Zhang JQ, Li M. Feasibility of rib fracture detection in low-dose computed tomography images with a large, multicenter datasets-based model. Heliyon 2024; 10:e31010. [PMID: 38770294 PMCID: PMC11103521 DOI: 10.1016/j.heliyon.2024.e31010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/22/2024] Open
Abstract
Purpose To evaluate the feasibility of rib fracture detection in low-dose computed tomography (CT) images with a RetinaNet-based approach and to evaluate the potential of lowdose CT for rib fracture detection compared with regular-dose CT images. Materials and methods The RetinaNet-based deep learning model was trained using 7300 scans with 50,410 rib fractures that were used as internal training datasets from four multicenter. The external test datasets consisted of both regular-dose and low-dose chest-abdomen CT images of rib fractures; the MICCAI 2020 RibFrac Challenge Dataset was used as the public dataset. Radiologists' interpretations were used as reference standards. The performance of the model in rib fracture detection was compared with the radiologists' interpretation. Results In total, 728 traumatic rib fractures of 100 patients [60 men (60 %); mean age, 53.45 ± 11.19 (standard deviation (SD)); range, 18-77 years] were assessed in the external datasets. In these patients, the regular-dose group had a mean CT dose index volume (CTDIvol) of 7.18 mGy (SD: 2.22) and a mean dose length product (DLP) of 305.38 mGy cm (SD: 95.31); the low-dose group had a mean CTDIvol of 2.79 mGy (SD: 1.11) and a mean DLP of 131.52 mGy cm (SD: 55.58). The sensitivity of the RetinaNet-based model and that of the radiologists was 0.859 and 0.721 in the low-dose CT images and 0.886 and 0.794 in the regular-dose CT images, respectively. Conclusions These findings indicate that the RetinaNet-based model can detect rib fractures in low-dose CT images with a robust performance, indicating its feasibility in assisting radiologists with rib fracture diagnosis.
Collapse
Affiliation(s)
- Liang Jin
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
- Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, China
| | - E. Youjun
- Yizhun Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Zheng Ye
- Shanghai Changfeng Community Health Service Center, Shanghai, China
| | - Pan Gao
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
| | - Guoliang Wei
- Yizhun Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Jia qi Zhang
- Yizhun Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
- Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Shanghai, China
| |
Collapse
|
3
|
Wasfie T, Hardy R, Naisan M, Hella J, Barber K, Yapchai R, Memar S, Megaly M, Shapiro B. Value of Thoracic CT in Blunt Trauma Patients With High Glasgow Coma Scale and Low Injury Severity Scale. Am Surg 2023; 89:5678-5681. [PMID: 37118989 DOI: 10.1177/00031348231173423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
OBJECTIVE To determine the value of ordering a routine chest CT (CCT) in patients with blunt trauma presenting to the emergency department with a high GCS and low ISS, we retrospectively collected patient data including CT scan results, when physical examination and initial chest X-ray were normal in the trauma bay area. METHODS A retrospective data collection of 901 consecutive blunt trauma patients seen in the ED between 2017 and 2019 was analyzed. Data included physical examination, age, gender, current use of anticoagulation therapy, comorbid conditions, as well as the result of radiologic images, hospital length of stay, surgical intervention, and mortality. The patients were divided into two groups: group one (patients with negative physical examination; chest x-ray and CT) and group 2 (negative physical examination, positive or negative chest x-ray, and positive CT). Statistical analysis was performed using student's t-test and chi-square test. RESULTS Of the 901 patients there were 489 (54%) males and 412 (46%) females with a mean age of 56 years. There were 461 patients who had a physical examination, chest x-ray, abdominal and CCT done. Group one included 442 (96%) patients, with negative physical examination, negative chest X-ray and CT scan. In group 2, 19 (4%) patients who had positive CT and or chest x-ray. Both groups were similar in GCS and ISS. Of the 19 patients, sixteen patients had a positive CCT, and thirteen of those had a positive chest x-ray. In the three patients who had negative physical examination and chest x-ray, the CT findings included one with a nondisplaced 10th rib fracture and two patients with osteoporotic compression fractures of dorsal vertebrae. The rate of both chest x-ray and CCT being positive among a group of screened patients was 16% (3/19) and the rate of a negative chest x-ray but positive CT was 16% (3/19). The odds ratio between the two outcomes was one. CONCLUSION In blunt trauma patients presenting to the ED with a high GCS and low ISS score, when initial physical examination and chest x-ray are negative, routine CCT is of little value.
Collapse
Affiliation(s)
- Tarik Wasfie
- Department of Trauma and General Surgery, Ascension Genesys Hospital, Grand Blanc, MI, USA
| | - Rachel Hardy
- Department of Trauma and General Surgery, Ascension Genesys Hospital, Grand Blanc, MI, USA
| | | | - Jennifer Hella
- Department of Academic Research, Ascension Genesys Hospital, Grand Blanc, MI, USA
| | - Kimberly Barber
- Department of Academic Research, Ascension Genesys Hospital, Grand Blanc, MI, USA
| | - Raquel Yapchai
- Department of Trauma and General Surgery, Ascension Genesys Hospital, Grand Blanc, MI, USA
| | - Shayan Memar
- Kirkville College of Osteopathic Medicine, Kirksville, MO, USA
| | - Maher Megaly
- College of Medicine, Central Michigan University, Mount Pleasant, MI, USA
| | - Brian Shapiro
- Department of Trauma and General Surgery, Ascension Genesys Hospital, Grand Blanc, MI, USA
| |
Collapse
|
4
|
Tan H, Xu H, Yu N, Yu Y, Duan H, Fan Q, Zhanyu T. The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma. BMC Med Imaging 2023; 23:55. [PMID: 37055752 PMCID: PMC10099632 DOI: 10.1186/s12880-023-01012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/04/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma. MATERIALS AND METHODS CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensusdiagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared. RESULTS There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns weresignificantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of attendings aided by DL-CAD (94.56%, 95.67%) or not aided (86.47%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced, and diagnostic confidence was significantly enhanced. CONCLUSIONS DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity, and positive predictive value for radiologists. DL-CAD can advance the diagnostic consistency of radiologists with different experiences.
Collapse
Affiliation(s)
- Hui Tan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Hui Xu
- Peter Boris Centre for Addiction Research, McMaster University & St. Joseph's Health Care Hamilton, 100 West 5th Street, Hamilton, ON, L8P 3R2, Canada.
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Haifeng Duan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Qiuju Fan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China.
| | - Tian Zhanyu
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China
| |
Collapse
|
5
|
Liu C, Chen Z, Xu J, Wu G. Diagnostic value and limitations of CT in detecting rib fractures and analysis of missed rib fractures: a study based on early CT and follow-up CT as the reference standard. Clin Radiol 2022; 77:283-290. [DOI: 10.1016/j.crad.2022.01.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 01/06/2022] [Indexed: 11/17/2022]
|
6
|
Duarte ML, dos Santos LR, Oliveira ASB, Iared W, Peccin MS. Computed tomography with low-dose radiation versus standard-dose radiation for diagnosing fractures: systematic review and meta-analysis. SAO PAULO MED J 2021; 139:388-397. [PMID: 34346965 PMCID: PMC9615584 DOI: 10.1590/1516-3180.2020.0374.r3.1902021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 02/19/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Computed tomography (CT) accounts for 13% of all radiological examinations in the United States and 40-70% of the radiation that patients receive. Even with the advent of magnetic resonance imaging (MRI), CT continues to be the gold standard for diagnosing bone fractures. There is uncertainty as to whether CT with a low radiation dose has a fracture detection rate similar to that of standard-dose CT. OBJECTIVE To determine the detection rate of low-dose radiation CT and standard-dose radiation CT for fractures, in patients with suspected fractures. DESIGN AND SETTING Systematic review of comparative studies on diagnostic accuracy within the evidence-based health program at a federal university in São Paulo (SP), Brazil. METHODS We searched the electronic databases Cochrane Library, MEDLINE, EMBASE and LILACS up to June 29, 2020, for studies evaluating the detection rates of low-dose CT and standard-dose CT for diagnosing bone fractures. The Research Triangle Institute (RTI) item bank tool was used for methodological quality evaluation. RESULTS The fracture detection rate according to the number of bones evaluated, using CT with low-dose radiation was 20.3%, while with standard-dose radiation it was 19.2%, and the difference between the methods was not significant. The fracture detection rate according to the number of patients, using CT with low-dose radiation was 56.0%, while with standard-dose radiation it was 58.7%, and this difference between the methods was not significant, either. CONCLUSION CT with low-dose radiation presented detection rates similar to those of CT with standard-dose radiation, regardless of the bones evaluated. REGISTRATION NUMBER CRD42019148491 at the PROSPERO database.
Collapse
Affiliation(s)
- Márcio Luís Duarte
- MD, MSc. Musculoskeletal Radiologist, WEBIMAGEM, São Paulo (SP), Brazil; and Doctoral Student in Evidence-based Health Program, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil.
| | - Lucas Ribeiro dos Santos
- MD, MSc. Endocrinologist and Professor of Physiology and Medical Clinic, Centro Universitário Lusíada (UNILUS), Santos (SP), Brazil; and Doctoral Student in Evidence-based Health Program, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil.
| | - Acary Souza Bulle Oliveira
- MD, PhD. Afliated Professor, Department of Neurology, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil.
| | - Wagner Iared
- MD, PhD. Supervising Professor, Evidence-Based Health Postgraduate Program, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil.
| | - Maria Stella Peccin
- PT, PhD. Associate Professor, Department of Human Movement Sciences, and Advisor, Evidence-Based Health Postgraduate Program, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil.
| |
Collapse
|
7
|
Zhang T, Wu J, Chen YC, Wu X, Lu L, Mao C. Magnetic Resonance Imaging has Better Accuracy in Detecting New-Onset Rib Fractures as Compared to Computed Tomography. Med Sci Monit 2021; 27:e928463. [PMID: 33424019 PMCID: PMC7812695 DOI: 10.12659/msm.928463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Background The aim of this study was to explore the magnetic resonance imaging (MRI) manifestations of new-onset rib fractures and determine the utility of MRI through a comparative study of MRI and computed tomography (CT). Material/Methods Twenty-one patients with mild chest trauma who sought medical care from February 2019 to June 2020 were included in this study. The patients were subjected to CT and MRI scanning within 2 weeks after the trauma, and CT rescanning was scheduled 4–8 weeks later to classify rib fractures and determine the diagnostic accuracy of MRI and CT for new-onset rib fractures. Results Seventy-seven rib fractures were confirmed by CT rescanning, of which 72 (93.51%) were type I fractures and 5 (6.49%) were type II. MRI identified 76 fractures, of which 3 were false positive, with the diagnostic accuracy rate of 91.25% and sensitivity rate of 94.81%. Among them, type I fractures (n=71, 3 were false positive) showed the MRI “sandwich” sign (heterogeneous high-signal shadow within bone marrow of the inner layer, low-signal bony cortex of the middle layer, and high-signal subperiosteal effusion of the outer layer) in T2-weighted fat-suppressed sequences; type II fractures (n=5) displayed intramedullary high-signal intensities and no subperiosteal effusion. Forty-four fractures (all type I) were discovered in the initial CT examination, and the corresponding diagnostic accuracy rate and sensitivity rate were 57.14%, which were lower than that of MRI. Conclusions MRI is highly sensitive for new-onset rib fractures, especially type I, so it is a preferred method for patients with mild chest trauma.
Collapse
Affiliation(s)
- Tao Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China (mainland)
| | - Jing Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China (mainland)
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China (mainland)
| | - Xinying Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China (mainland)
| | - Lingquan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China (mainland)
| | - Cunnan Mao
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China (mainland)
| |
Collapse
|
8
|
Jin L, Yang J, Kuang K, Ni B, Gao Y, Sun Y, Gao P, Ma W, Tan M, Kang H, Chen J, Li M. Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet. EBioMedicine 2020; 62:103106. [PMID: 33186809 PMCID: PMC7670192 DOI: 10.1016/j.ebiom.2020.103106] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/17/2020] [Accepted: 10/19/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is also subject to the qualification of radiologist. We aim at a clinically applicable automatic system for rib fracture detection and segmentation from CT scans. METHODS A total of 7,473 annotated traumatic rib fractures from 900 patients in a single center were enrolled into our dataset, named RibFrac Dataset, which were annotated with a human-in-the-loop labeling procedure. We developed a deep learning model, named FracNet, to detect and segment rib fractures. 720, 60 and 120 patients were randomly split as training cohort, tuning cohort and test cohort, respectively. Free-Response ROC (FROC) analysis was used to evaluate the sensitivity and false positives of the detection performance, and Intersection-over-Union (IoU) and Dice Coefficient (Dice) were used to evaluate the segmentation performance of predicted rib fractures. Observer studies, including independent human-only study and human-collaboration study, were used to benchmark the FracNet with human performance and evaluate its clinical applicability. A annotated subset of RibFrac Dataset, including 420 for training, 60 for tuning and 120 for test, as well as our code for model training and evaluation, was open to research community to facilitate both clinical and engineering research. FINDINGS Our method achieved a detection sensitivity of 92.9% with 5.27 false positives per scan and a segmentation Dice of 71.5%on the test cohort. Human experts achieved much lower false positives per scan, while underperforming the deep neural networks in terms of detection sensitivities with longer time in diagnosis. With human-computer collobration, human experts achieved higher detection sensitivities than human-only or computer-only diagnosis. INTERPRETATION The proposed FracNet provided increasing detection sensitivity of rib fractures with significantly decreased clinical time consumed, which established a clinically applicable method to assist the radiologist in clinical practice. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section. The funding sources played no role in the study design; collection, analysis, and interpretation of data; writing of the report; or decision to submit the article for publication .
Collapse
Affiliation(s)
- Liang Jin
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China
| | - Jiancheng Yang
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, P.R. China; Dianei Technology, Shanghai, P.R. China
| | | | - Bingbing Ni
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, P.R. China; Huawei Hisilicon, Shanghai, P.R. China
| | - Yiyi Gao
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China
| | - Yingli Sun
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China
| | - Pan Gao
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China
| | - Weiling Ma
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China
| | - Mingyu Tan
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China
| | - Hui Kang
- Dianei Technology, Shanghai, P.R. China
| | | | - Ming Li
- Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| |
Collapse
|